Entries added 2026-10-11: Post-Vaccination Syndrome — Phase 10a synthesis (post-vaccination-syndrome)

Chapter Abstract

This chapter presents detailed protocols for research studies designed to test hypotheses derived from pediatric ME/CFS outcomes and to advance understanding of recovery mechanisms. These proposals range from observational cohort studies to randomized controlled trials, each with explicit hypotheses, design specifications, and expected outcomes.

The evidence tables that motivate and constrain these proposed studies are consolidated in Appendix G (Research Synthesis Tables), and the supporting literature is annotated in Appendix H (NE Axis Probes and Cross-Links — Annotated Entries (P5-P14)).

Chapter Roadmap: How to Use This Chapter

For patients: skim the early-intervention-trial and pediatric-adult-study sections to see how recovery-focused trials are being designed, and the HRV-pacing section for a pacing approach you could relate to daily management.

For caregivers: read the periodization and infection-decline study sections for designs relevant to pacing and relapse.

For clinicians: review the LSR-biomarker-validation and wound-healing-biomarker study sections as near-term candidate diagnostics, and the treatment-sequence-RCT section for a paradigm-shaping trial design.

For researchers: this chapter is a protocol library — start with the replication and prodromal sections, then the highest-value protocols (biomarker validation, PEM-recovery-duration, viral-clearance-longitudinal, pharmacogenomics).

ID / Label	Details	Phase / Cert
spec aluminum-adjuvant-nlrp3-immune-stimulation	Speculation (mechanism strand, ch08): chronic immune stimulation is a documented ME/CFS theme, and aluminium oxyhydroxide adjuvants activate the NLRP3 (Nalp3) inflammasome to drive IL-1β (Eisenbarth et al. 2008); aluminium-adjuvant particles biopersist in phagocytes and may reach brain (animal) (Gherardi et al. 2015), and MMF patient phagocytes show specific inflammatory, autophagic, and mitochondrial responses in vitro (Masson et al. 2024). The speculation is that, in a minority of vaccine-adjuvant-exposed people, sustained low-grade NLRP3/IL-1β signalling could contribute to ME/CFS-like fatigue. Mechanism strand only — does not assert population-level causation, which a nationwide registry already counts against (adjuvanted vaccine HR 0.97 (Magnus et al. 2015)). Certainty 0.33 (discounted: in vitro/animal provenance, no ME/CFS-specific data; 0.28→0.33: convergence with HS-NLRP3 bridge and IL-11 inflammaging on the shared NLRP3→IL-1β→neuroinflammation→fatigue endpoint via distinct triggers/labs). Severity applicability unknown — MMF cohort not severity-stratified. Consequence: if true, stratify immune phenotyping by adjuvant-exposure history and deltoid-biopsy MMF status; no diagnostic or treatment change currently. (ch08 Aluminium-Adjuvant NLRP3/IL-1$\beta$ Chronic Immune Stimulation as an Unproven ME/CFS Contributor; origin: literature — mechanism strand of aluminum-adjuvant-mmf.)	Phase 7 / 0.33
oq mmf-deltoid-biopsy-significance	Open question (biomarker strand, ch36): whether the deltoid-biopsy MMF finding is a genuine ME/CFS-relevant biomarker or a referral-selection artifact is unresolved. MMF is a validated histopathological entity (aluminium-particle biopersistence in deltoid macrophages), and the single Henri-Mondor group describes an ME/CFS-like presentation (Gherardi, Crépeaux, and Authier 2019) (Rigolet et al. 2014) (Van Der Gucht et al. 2015) (Van Der Gucht et al. 2017) (Aoun Sebaiti et al. 2018) — but no study applies formal diagnostic criteria (Fukuda/CCC/ICC/IOM), all literature derives from one cohort (not independent replication), and the strongest independent population evidence is negative (adjuvanted vaccine HR 0.97 (Magnus et al. 2015)). Severity applicability unknown — MMF cohort not severity-stratified. Falsifiable resolution: an independent criteria-based (IOM 2015) multicenter deltoid-biopsy MMF study. Consequence: until such data, a positive MMF deltoid biopsy should not diagnose ME/CFS or select treatment; it is a documented finding in a small referral-biased subgroup, not a validated ME/CFS biomarker. (ch36 Macrophagic Myofasciitis Deltoid Biopsy: A Contested Histological Biomarker; origin: literature — biomarker strand of aluminum-adjuvant-mmf.)	Phase 3 / n/a

ID / Label	Details	Phase / Cert
spec aluminum-adjuvant-amplifier-modulator	Speculation (mechanism strand, ch14d, H1): a sequential model reconciles the registry observations — the adjuvanted vaccine did not raise CFS/ME risk (HR 0.97) while infection did (HR 2.04) (Magnus et al. 2015). Aluminium-adjuvant particles biopersist and continuously gate NLRP3 priming (alum→NLRP3→IL-1β (Eisenbarth et al. 2008)), so alum sets a low-level NLRP3 “gain”; a later independent viral trigger tips IL-1β over threshold into symptom generation. Alum is an amplifier, not an independent cause. Operationalisation = R3: prospective post-infectious cohorts record pre-infection alum-adjuvant history and stratify by exposure. Falsifiable prediction: in a vaccine-exposed cohort, ME/CFS incidence after infection will be higher with a long vaccine-to-infection interval. Certainty 0.20 (mechanisms real; sequencing interaction unstudied in humans). Severity applicability unknown. Consequence: reframes the question from “does a vaccine cause ME/CFS?” to “does prior vaccine exposure change how strongly a later infection affects you?”. (ch14d Aluminium Adjuvant as an Amplifier, Not a Trigger, of Post-Infectious ME/CFS; origin: brainstorm H1/R3.)	Phase 5 / 0.20
oq aluminum-adjuvant-dose-response	Open question (research direction, ch14d, R2): the population null (HR 0.97) tested only any adjuvanted vaccine; a graded dose-response re-analysis of the registry data (number of adjuvanted doses, total aluminium dose, booster timing against IOM-coded incident ME/CFS) would sharply constrain the causal claim. Falsifiable prediction: cumulative aluminium-adjuvant dose shows no monotonic increase in ME/CFS incidence (flat HR ~1.0 across dose quartiles); a positive monotonic trend would contradict (Magnus et al. 2015). Certainty of resolution if run 0.38 (registry data exist; dose-stratification straightforward). Severity applicability unknown. Consequence: could resolve cheaply and within a few years whether aluminium dose is truly irrelevant. (ch14d Dose-Response Test: Cumulative Aluminium-Adjuvant Exposure Against Incident ME/CFS; origin: brainstorm R2.)	Phase 5 / 0.38
oq aluminum-vs-nonalum-immune-state	Open question (research direction, ch14d, R4): the registry compared vaccinated vs unvaccinated and cannot exclude a non-aluminium vaccine component; alum specifically activates NLRP3 (Eisenbarth et al. 2008) whereas other adjuvants engage different pathways, and MMF phagocytes show in vitro mitochondrial/autophagic changes (Masson et al. 2024). Compare long-term circulating immune state (IL-1β, IL-18, monocyte phenotype, mitochondrial-reserve markers) in alum- vs non-alum-vaccine recipients, matched for age/sex/indication. Falsifiable prediction: no durable ≥6-month difference in IL-1β/IL-18 or mitochondrial-reserve markers between alum and non-alum recipients; any alum signature transient and below the ME/CFS range. Certainty of resolution if run 0.30. Severity applicability unknown. Consequence: isolates the aluminium ingredient’s lasting immune trace from the general vaccination response. (ch14d Isolating the Aluminium Effect: Alum vs Non-Alum Adjuvant Immune-State Comparison; origin: brainstorm R4.)	Phase 5 / 0.30
oq aluminum-postvaccination-onset-distinction	Open question (case-definition, ch14d, NUL1): even if the null holds (aluminium adjuvants do not cause ME/CFS), MMF remains a real local histopathological entity but a bystander reaction, not an etiologic lesion. “Post-vaccination onset” (real, temporally associated) is not evidence of “aluminium-caused onset”; the temporally overlapping infection (HR 2.04 (Magnus et al. 2015)) is a better-supported explanation, or a non-aluminium vaccine component. Falsifiable prediction: under the null, ME/CFS-after-vaccination cases are attributable to the coincident infection and show no aluminium dose-dependence or deltoid-lesion correlation with severity. Confidence in the null interpretation 0.70 (reflects strong independent registry counter-evidence; this is counter-evidence confidence, not a positive causal claim — no positive mechanistic certainty crosses the 0.45 PARTIAL cap). Severity applicability unknown. Consequence: a patient developing ME/CFS shortly after a vaccine should not be told the aluminium caused it. (ch14d Post-Vaccination Onset vs Aluminium-Caused Onset: A Case-Definition Distinction; origin: brainstorm NUL1.)	Phase 5 / 0.70 counter
lim aluminum-adjuvant-confounding	Limitation (epistemic, ch14d, E3): vaccination is not randomly assigned; vaccinated vs non-recipients differ in health-seeking, chronic-illness avoidance, occupational exposure — a spurious vaccine association can arise. Reverse causation: MMF is diagnosed in symptomatic people who recall vaccine history, inflating perceived causality. The (Magnus et al. 2015) registry mitigates confounding for the population null but not the referral MMF cohort, whose association is compatible with confounding alone. Severity applicability unknown. Consequence: additional caution beyond single-site/referral concerns before treating any vaccine–ME/CFS association as causal. (ch14d Aluminum-Adjuvant MMF: Confounding by Indication and Reverse Causation; origin: brainstorm E3.)	Phase 5 / n/a
lim aluminum-adjuvant-measurement-validity	Limitation (epistemic, ch14d, E4): validity of exposure and outcome uncertain. (a) MMF is a single-site biopsy; a negative deltoid biopsy does not exclude alum-laden macrophages elsewhere, and population frequency unknown. (b) Aluminium “body burden” rests on pharmacokinetic modelling not directly measured in humans (Mitkus et al. 2011); circulating/urinary aluminium is a poor tissue-store proxy. (c) In-vitro findings use patient cells without a population comparator (Masson et al. 2024), so in-vivo effect size unknown. Consequence: widens confidence interval on any MMF→ME/CFS association; argues for direct tissue-aluminium quantitation (ICP-MS) correlated with histology/symptoms in a multicentre sample. Severity applicability unknown. (ch14d Aluminum-Adjuvant MMF: Measurement Validity of Exposure and Outcome; origin: brainstorm E4.)	Phase 5 / n/a

1 LDN Hormetic Dose-Response

ID / Label	Details	Phase / Cert
spec:ldn-hormetic-window	LDN shows non-monotonic dose-response within the clinical 0.5 to 4.5 mg range because TLR4 partial antagonism triggers Nrf2-mediated compensatory anti-inflammatory priming at lower doses; higher doses within the LDN range remove the basal TLR4 tone needed to sustain this compensatory response. Four distinct mechanisms have non-overlapping dose optima. No within-range dose-response trial in any condition. Falsifiable prediction: prospective four-arm crossover trial must show non-monotonic individual response curves. Falsified if all individual curves are monotonic within the tested range.	Phase 3 / 0.30
hyp:multi-target-dose-optimum-divergence	LDN’s four mechanisms have distinct concentration-response curves with potentially non-overlapping optima. A patient’s optimal dose reveals which mechanism is dominant. Prediction: patients with TRPM3 dysfunction should respond to higher LDN doses while patients with inflammation-driven symptoms should respond to lower doses. Falsified if there is no correlation between baseline TRPM3 function, inflammatory markers, and individual optimal LDN dose.	Phase 3 / 0.25
spec:hormesis-multi-drug-principle	Non-monotonic dose-response is a recurring pattern across ME/CFS pharmacotherapy spanning 18+ medications (LDN, LDA, lithium, melatonin, sulforaphane, corticosteroids, DORAs, duloxetine, beta-blockers, modafinil, guanfacine, gabapentinoids, H1 antihistamines, rapamycin, allopregnanolone, NAC, ketotifen, quercetin; taVNS is a hypothesis-stage candidate without a confirmed dose window). The hormesis framework unifies these observations via Nrf2-mediated compensatory upregulation (Calabrese corpus), catecholamine inverted-U at prefrontal D1/α2A receptors (Arnsten, Cools), mTORC1/mTORC2 dose selectivity (Sarbassov, Lamming), and biphasic concentration-response at GABA-A (Andreen). Falsifiable prediction: individual inversion-point position (standardised dose at which symptom improvement turns to worsening) should correlate across drugs within each patient if a hormetic reserve trait exists; mean pairwise r ≥ 0.4 expected, with ≥60% of all 153 pairwise comparisons reaching r ≥ 0.3. Falsified if mean pairwise r < 0.2 and fewer than 20% of pairs reach r ≥ 0.3. Indeterminate if mean r falls in 0.2–0.4 range or 20–60% of pairs are above threshold — then hormetic reserve signal is too weak/noisy for a single-trait model and would require mechanistic-clustering decomposition. (Calabrese 2002) (Calabrese and Baldwin 2003) (Calabrese 2010) (Sun et al. 2020) (Amy F. T. Arnsten 2011) (Cools and D’Esposito 2011) (Sarbassov et al. 2006) (Lamming et al. 2012) (Andréen et al. 2009)	Phase 3 / 0.35
oq:ldn-dose-response-research-gap	No within-range LDN dose-response trial exists for ME-CFS. The upcoming LIFT trial uses a single fixed dose and cannot address this gap.	Phase 3 / n/a
hyp:hormetic-mechanistic-clustering	Non-monotonic dose-response drugs group into five mechanistic categories (Nrf2, catecholamine, D2 partial-agonist, mTOR/autophagy, GABAergic/neurosteroid). Inversion-point positions should correlate within categories (r ≥ 0.4) but not across (r < 0.2). Cluster 3 (D2 partial-agonist: aripiprazole/LDA, single-member) is an occupancy-dependent inverted-U, pharmacologically distinct from both Nrf2 hormesis and PFC catecholamine inverted-U — the curve is identical in any population with dopamine deficit, not ME/CFS-specific. Lithium and corticosteroids are bridge drugs spanning multiple categories. Falsifiable prediction: within-patient intra-class r ≥ 0.4 in ≥4 of 5 categories; cross-category max pairwise r < 0.2. Falsified if all pairwise r < 0.2 including within-category. Indeterminate: 0.2 ≤ r < 0.4 across all (requires expanded N). (Calabrese 2010) (Amy F. T. Arnsten 2011) (Cools and D’Esposito 2011) (Sarbassov et al. 2006) (Andréen et al. 2009) (Crosby, Kalantar, and DeRisi 2021) (Sun et al. 2020)	Phase 4 / 0.25
spec:hormetic-inversion-point-battery-hip-b	A 6-drug within-patient crossover pilot trial (LDN, sulforaphane, duloxetine, modafinil, rapamycin, allopregnanolone — each at 4 dose levels) to estimate cross-drug inversion-point correlation effect size. With n=20, the 95% CI on pairwise r spans approximately -0.05 to 0.71 — the 0.2-to-0.4 transition zone is within the CI, preventing decisive classification for most outcomes. The pilot therefore estimates effect size for a definitive trial requiring n ≥ 80 to distinguish confirmed (r ≥ 0.4) from falsified (r < 0.2). If 30–50% show inversions, effect size is too small to reject confounding at n=20; requires n ≥ 80 replication. Due to high PEM burden, a staged design is proposed: Phase 1 = 2-drug crossover (LDN + duloxetine, ~14–18 weeks); Phase 2 = expand to 6 drugs only conditional on Phase 1 r ≥ 0.3. Estimated cost: $ 1.2M–$ 1.7M (Phase 1 + Phase 2 combined). No pharmaceutical company will fund (all 6 drugs generic). (Samuel et al. 2019) (Samuel et al. 2023)	Phase 4 / n/a
spec:hormetic-reserve-heritability	If inversion-point correlation r ≥ 0.4 is confirmed in HIP-B, hormetic quotient (HQ) is measurable and predicted to be ~40–60% heritable — a quantitative endophenotype for ME/CFS susceptibility analogous to P50 sensory gating in schizophrenia. Predictions: MZ twin pairs discordant for ME/CFS should show correlated HQ values (r ≥ 0.5); unaffected first-degree relatives should show intermediate HQ; GWAS on HQ (n ≥ 500) should identify loci in NFE2L2, KEAP1, COMT, TLR4, ADRB1/2, NR3C1, FKBP5, MTOR. Falsified if HQ shows no family concordance (r < 0.2 in MZ pairs).	Phase 4 / 0.20
oq:hormesis-cross-disease-generality	If hormetic reserve is heritable and measurable in ME/CFS, the same battery may reveal narrowed hormetic windows in other post-infectious diseases (Long COVID, PTLDS, fibromyalgia, POTS) and in elderly populations with decreased stress-response adaptivity. Null hypothesis: hormetic windows narrow with age in all populations, not disease-specifically. Testable with HIP-B in age-matched healthy controls vs ME/CFS vs Long COVID cohorts. Falsifiable prediction: if HIP-B is administered across all three groups, the HOIP model predicts disease-specific inversion-point narrowing (ME/CFS r < Long COVID r < PTLDS r < healthy r). Falsified if all groups show identical mean pairwise r — hormetic narrowing is age-driven, not disease-specific. Indeterminate if some disease pairs differ and others do not (mixed signal).	Phase 4 / n/a

2 Interoceptive Dose-Finding

ID / Label	Details	Phase / Cert
spec:interoceptive-dose-finding	Deliberate intermittent dosing in ME/CFS — patients skipping doses, taking drug holidays, pulsing medications — may represent rational interoceptive N-of-1 experimentation rather than non-adherence. The allostatic-interoceptive network computes allostatic significance from interoceptive afferent signals and updates predictive models of bodily state. Repeated perturbation-and-sensing cycles (dose → symptom change → dose adjustment) allow the network to approximate the dose-response curve without biomarkers. The hormetic dose-response framework establishes the therapeutic window as a 3D volume (dose × time × response) with time-dependent features; the interoceptive framework proposes that patients are probing this volume through deliberate pulse-and-assess cycles. Falsifiable: qualitative interview data from ME/CFS patients who pulse medications should reveal deliberate interoceptive experimentation in ≥10% of patients. Falsified if fewer than 10% report interoceptive dose-finding as a reason for pulsing. Individual mechanistic components range from cert 0.30 (time-dependent hormesis) to cert 0.70 (allostatic-interoceptive network mapping); the conceptual link between them is untested, cert 0.20 for the integrated claim. (J. Zhang et al. 2025) (Sun et al. 2018) (Mushak 2016)	Phase 1 / 0.20

3 Skeletal Asymmetry and Postural Mechanics

ID / Label	Details	Phase / Cert
spec skeletal-asymmetry-cascade	Skeletal asymmetry (rotoscoliosis, DAMI, ILMI) as primary mechanical driver of ME/CFS via four pathways: compensatory muscle overuse, sympathetic chain irritation, mechanical nerve compression, cervical aging spiral. Origin: Gerlier 2026-07-21, personal communication.	Phase 1 / 0.10
spec ch08-thoracolumbar-sympathetic-irritation	Thoracolumbar sympathetic chain irritation from rotoscoliosis producing multi-organ dysautonomia without requiring CCI. Anatomical precedent: Schulte2010 (sympathetic chain vulnerable to thoracic spine surgery).	Phase 1 / 0.10
spec mechanical-postural-subgroup	Skeletal asymmetry defines a distinct ME/CFS subgroup: non-hypermobile, positionally modulated, gradual-onset, age-worsening. Distinct from hypermobility/hEDS subgroup.	Phase 1 / 0.10
lim skeletal-asymmetry-evidence-gap	The Gerlier hypothesis has near-zero direct ME/CFS evidence at any link. 10 indirect papers; all supporting evidence comes from surgical complication reports, genetic disease models, and clinical reasoning. DAMI/ILMI yield zero PubMed results.	Phase 2 / n/a
spec skeletal-asymmetry-cascade, ch15 Fascial and Connective Tissue Innervation	Skeletal asymmetry produces chronic low-grade mechanical nerve compression (lateral femoral cutaneous, sciatic, pudendal) distinct from SFN — a compression neuropathy amplified by central sensitization. Improvement predicted with postural correction.	Phase 2 / 0.10
spec skeletal-asymmetry-cascade, “How does the asymmetry originate?”	Competing hypotheses for skeletal asymmetry origin: developmental/traumatic (Gerlier: neonatal cervical subluxation → spiralization), congenital/genetic, acquired postural/deconditioning, or idiopathic. Cascade downstream is agnostic to origin.	Phase 2 / n/a — competing origins

4 Hidradenitis Suppurativa as Autoinflammatory Comparator

L p=0.70Autoinflammatory Disease Category Absent from ME/CFS Literature

Evidence: PubMed search for “autoinflammatory disease” AND (“chronic fatigue syndrome” OR ME/CFS) returned zero results (July 2026). Autoinflammatory diseases (FMF, CAPS, TRAPS, HS, AOSD, SAPHO, Behçet’s) are innate-immune-driven, IL-1beta/IL-18/NLRP3-mediated conditions with fatigue as a major symptom — yet the entire category has never been compared to or studied alongside ME/CFS. This is a categorical research gap (absence of investigation), not a null result.
Mechanism: Not a mechanistic claim — an evidence-gap observation. Autoinflammatory diseases share the NLRP3/IL-1beta effector arm with ME/CFS PEM models, produce fluctuating fatigue, are treated with mechanistically-targeted therapies (anakinra, canakinumab, colchicine), but are distinguishable by elevated systemic inflammatory markers (CRP, SAA, ESR) that are typically normal/minimally elevated in ME/CFS. The gap may reflect a real biological distinction or a field artefact.
Chapter ref: ch14d: autoinflammatory category absent
Prediction: Not applicable — this is a categorical evidence gap, not a testable hypothesis. Adding ME/CFS screening instruments to autoinflammatory disease registries would directly address the gap.
Treatment: No treatment implication. Research implication: add DSQ-PEM and CCC/IOM criteria to autoinflammatory registries (Eurofever, HS ALLIANCE) at minimal cost — a one-year timeline to establish or refute autoinflammatory-ME/CFS comorbidity.
Limitation: Litigation search limitation: zero results may reflect PubMed indexing gaps (autoinflammatory disease is a relatively recent category, unified ~1999; many constituent diseases were historically indexed under “rheumatology” or “dermatology”). Google Scholar and EMBASE search may yield additional results. The gap is documented, not guaranteed — a positive finding could emerge from databases or registries not yet searched.

Speculation p=0.35HS-NLRP3-IL-1beta Pathway as Mechanistic Bridge to ME/CFS Fatigue

Evidence: NLRP3 is a firmly established driver of HS inflammation (scRNA-seq explant (Moran et al. 2023) cert 0.75; PCR overexpression (Krajewski, Szukała, and Szepietowski 2024) cert 0.50; metformin AMPK-NLRP3 (Petrasca et al. 2023) cert 0.65). NLRP3 mediates fatigue in mouse models: KO mice show markedly reduced LPS-fatigue and swim-fatigue with decreased brain IL-1beta (ZT Zhang et al. 2016) cert 0.70 (Z. Zhang et al. 2017) cert 0.65. PEM model in CFS/Long COVID includes NLRP3→IL-1beta→neuroinflammation (H. Jin et al. 2026) cert 0.65. Single epidemiological association HS–ME/CFS OR 1.72 (Prens et al. 2022) cert 0.70. Zero direct HS–ME/CFS mechanistic studies exist. Kilgour 2026: NLRP3 inhibition alone (AZD9056) insufficient for clinical HS response despite restoring PBMC cytokines — suggests redundant pathways.
Citations: (Moran et al. 2023) (Krajewski, Szukała, and Szepietowski 2024) (Petrasca et al. 2023) (ZT Zhang et al. 2016) (Z. Zhang et al. 2017) (H. Jin et al. 2026) (Prens et al. 2022)
Mechanism: HS systemic inflammation → NLRP3/IL-1beta/IL-17 → systemic cytokine spillover → neuroinflammation → fatigue. Both HS and ME/CFS share NLRP3/IL-1beta as a plausibly common fatigue mediator; HS provides a human autoinflammatory model where the NLRP3 axis is causal and therapeutic targets are FDA-approved, making it a tractable platform for studying cytokine→fatigue mechanisms that cannot be directly studied in ME/CFS. Distinct from classical autoimmunity (adaptive-T/B-cell-driven) — HS is an innate-immune-driven autoinflammatory disease of the IL-1beta/IL-18/NLRP3 axis.
Chapter ref: ch14d: hs nlrp3 mechanistic bridge
Prediction: Observational study of HS patients initiating biologics: anti-IL-1beta therapy (anakinra/canakinumab) will show the largest FSS/PROMIS-Fatigue reduction by Week 16 among cytokine-targeting agents, exceeding anti-TNF and anti-IL-17. Falsified if no anti-cytokine therapy reduces fatigue beyond placebo despite clinical HS response (HiSCR≥50).
Treatment: No direct ME/CFS treatment implication. HS biologics are not indicated for ME/CFS. The hypothesis is mechanistic: HS as model system for cytokine→fatigue biology. Kilgour 2026 (NLRP3 inhibition alone ineffective for HS) already constrains any single-agent NLRP3 strategy.
Limitation: Entire bridge is inferential — zero direct NLRP3 measurements in ME/CFS patients, zero HS biologic trials with fatigue endpoints. Prens 2022 is cross-sectional and self-reported (single study). HS has its own pathogenic drivers (follicular occlusion, keratinocyte dysfunction, pilosebaceous microbiome) unrelated to ME/CFS. Fatigue phenotype in HS never characterised for PEM — unknown whether Type 1 (post-exertional) or Type 2 (constant cytokine-driven).

Speculation p=0.35Kinesiophobia as Shared Behavioral Phenotype Across HS, FM, and ME/CFS

Evidence: HS patients have significantly higher kinesiophobia and lower physical activity vs controls; pain intensity, fatigue severity, and depressive symptoms are independent predictors in multivariable regression (Meral Ketenci, Meral, and Meral Obholzer 2026) cert 0.60. Convergent with documented activity-avoidance patterns in FM and ME/CFS. No cross-condition TSK comparison has been performed.
Citations: (Meral Ketenci, Meral, and Meral Obholzer 2026)
Mechanism: Repeated symptom exacerbation after physical activity → learned avoidance (kinesiophobia) → deconditioning → reduced activity tolerance → further avoidance — a unified behavioural phenotype of chronic inflammatory conditions. Mediated by a shared neurobiological substrate (possibly IL-1beta/IL-6 sickness behaviour acting on anterior insula/ACC) rather than condition-specific psychology. Distinct physical triggers (skin friction in HS, diffuse pain in FM, metabolic/PEM in ME/CFS) converge on the same behavioural output.
Chapter ref: ch14d: hs kinesiophobia shared phenotype
Prediction: TSK across HS/FM/ME/CFS cohorts: structural equation model shows configural invariance (CFI>0.95, RMSEA $<$ 0.06) for a latent “movement-avoidance” factor driven equally by pain and fatigue. Falsified if configural invariance is rejected across conditions.
Treatment: Graded-activity and pain-neuroscience-education interventions developed for FM could be adapted to HS and ME/CFS with minimal modification if kinesiophobia is a shared phenotype. Critical caveat: for ME/CFS patients with metabolic PEM, kinesiophobia reduction without addressing the metabolic limit could be harmful — distinguishing perceptual from metabolic avoidance is the clinical priority.
Limitation: Single HS study (Meral Ketenci 2026). No cross-condition TSK data. Correlation ≠ causation — fatigue-pain-depression-kinesiophobia cluster could be confounded by sleep, socioeconomic status, or diagnostic odyssey. TSK was validated in musculoskeletal pain, not inflammatory skin disease — psychometric properties in HS unknown. IL-1beta/insula→kinesiophobia link is itself inferential, never tested in any condition.

Speculation p=0.30Prodromal HS Fatigue as PEM Latency Model

Evidence: 83.3% of HS patients experience prodromal symptoms 12–24+ h before visible lesions; 32% report fatigue prodrome, alongside malaise (23%), headache (11%), nausea (2%) (Ring et al. 2017) cert 0.65. Temporal pattern parallels delayed PEM onset (12–48 h). NLRP3/IL-1beta activation in incipient follicular inflammation → systemic cytokine spillover → sickness behaviour is the proposed mechanism. Zero studies have directly compared HS prodrome time-course to ME/CFS PEM time-course.
Citations: (Ring et al. 2017)
Mechanism: Local NLRP3/IL-1beta activation in incipient HS lesion → systemic IL-1beta/IL-6/TNF-alpha spillover → sickness behaviour (fatigue, malaise, hyperalgesia) 12–24 h before clinical lesion — temporally analogous to PEM onset latency post-exertion. If both share a common effector (cytokine→sickness-behaviour), HS provides a tractable model where the trigger is visible/accessible (skin) and serial sampling is feasible — impractical in ME/CFS where the trigger (exertion) is not localisable.
Chapter ref: ch14d: hs prodrome pem latency
Prediction: Serial serum IL-6/TNF-alpha/IL-1beta Q4h through HS flare prodrome-to-resolution: cytokine peak temporally precedes fatigue-severity peak by ≥4 h in N-of-1 responder analyses. PEM-like symptom latency correlates with cytokine-to-fatigue lag (r>0.5). Falsified if fatigue rises simultaneously with or before systemic cytokines.
Treatment: Research-stage model only. If validated, HS becomes a “fast-track” platform for testing anti-IL-1beta pre-treatment for aborting cytokine-induced fatigue — relevant to both HS and ME/CFS but with an accessible spontaneous trigger rather than requiring controlled exertion.
Limitation: Ring 2017 is a questionnaire study (n=72) with no serum cytokines — prodrome entirely patient-reported, no validated fatigue instrument. PEM comparison is structural analogy, not empirical. HS biopsies cannot ethically confirm incipient inflammation during prodrome — cytokine source is inferred. Intrafollicular anaerobic microbiome may drive inflammation in ways with no ME/CFS parallel.

5 Clinical Trial Methodology

Hypothesis p=0.55Inadequate Treatment Duration and Spectrum as a Systematic Confound in Negative Chronic Disease Trials

Evidence: Lyme retreatment RCTs (Klempner 2001) found “no benefit” to 90-day antibiotics, but enrolled pre-treated patients (mean 4.7yr, 3+ prior courses) and did not cover co-infections (Klempner et al. 2001). DeLong’s independent biostatistical review found all 4 retreatment RCTs underpowered; Krupp 2003 and Fallon 2008 showed significant benefit (DeLong et al. 2012) (Krupp et al. 2003) (Fallon et al. 2008). Borrelia forms persister/biofilm variants requiring combination therapy (J. Feng et al. 2019). Standard regimens miss Babesia, Bartonella co-infections (Popov, Bashchobanov, and Andonova 2026). In ME/CFS: Watt (2012) found longer valganciclovir→better response (p=0.0002) (Watt et al. 2012); Fluge (2015) 23-week mean response lag (Ø. Fluge et al. 2015); Strayer (2020) treatment window at 2–8yr (Strayer, Young, and Mitchell 2020).
Citations: (Klempner et al. 2001) (DeLong et al. 2012) (Krupp et al. 2003) (Fallon et al. 2008) (J. Feng et al. 2019) (Popov, Bashchobanov, and Andonova 2026) (Watt et al. 2012) (Ø. Fluge et al. 2015) (Strayer, Young, and Mitchell 2020) (Ø. Fluge et al. 2019) (Cameron 2006)
Mechanism: Three-part framework for interrogating negative trials: (1) duration — was treatment long enough for chronic pathway normalization (not just acute intervention)? (2) spectrum — did coverage include co-morbid mechanisms (co-pathogens, co-morbid pathways)? (3) window — were patients treated within the disease-duration treatment window (before irreversible pathology)? The Lyme paradigm shows that all three factors can conspire to produce false-negative trial conclusions. Does NOT assert that all negative trials are false negatives — the definitive RituxME Phase III (Fluge 2019) was genuinely negative despite 12-month duration.
Chapter ref: ch32: lyme paradigm
Prediction: An ME/CFS antiviral trial testing 12+ months of ganciclovir derivatives in HHV-6/EBV co-infected patients within 2–8 years illness duration will show higher response rates than trials testing shorter courses in unstratified populations. Falsified if extended-duration combination antiviral trials in virus-selected ME/CFS patients show no benefit over placebo.
Treatment: Not a treatment recommendation — a methodological framework for trial design. Implies that negative trial results should not be cited as evidence that a mechanism is irrelevant without first checking whether duration, spectrum, and treatment window confounded the outcome.
Limitation: No prospective ME/CFS trial has tested extended-duration vs standard-duration head-to-head. Framework derived from cross-disease evidence (Lyme). Rituximab Phase III counterexample shows duration is not a universal explanation. High-certainty for Lyme; hypothesis-generating for ME/CFS.

Open Question p=n/aCaveats to the Duration Critique: Selection Bias, Placebo Response, and Natural Recovery as Alternative Confounds

Evidence: The duration critique has significant limitations (Cameron 2006) (Ø. Fluge et al. 2019) @[“Smith2015ME/CFSTxSysRev”] (Watt et al. 2012) (Horowitz and Freeman 2020). Selection bias toward pre-treated refractory patients may explain Klempner’s negative result better than treatment duration. Placebo response rates exceed 30% in blinded ME/CFS trials. Natural recovery (5–10%/year) can contaminate extended-duration trial results. Watt 2012’s duration-response finding is retrospective (confounding by indication). The three-part framework (duration × spectrum × window) may be unfalsifiable in practice because factors cannot be independently tested. Origin: brainstorm.
Citations: (Cameron 2006) (Ø. Fluge et al. 2019) @[“Smith2015ME/CFSTxSysRev”] (Watt et al. 2012) (Horowitz and Freeman 2020)
Mechanism: Five independent confounds that could produce positive signals in extended-duration trials without genuine treatment efficacy: (1) selection-bias enrollment of refractory patients → ‘negative’ trial is really a population effect not a treatment effect; (2) placebo-response maintenance in blinded trials; (3) natural recovery contamination; (4) retrospective confounding by indication (responders kept on drug longer); (5) unfalsifiability of the multi-factor framework.
Chapter ref: ch32: duration caveats
Prediction: Head-to-head duration-randomized RCT (6mo vs 12mo valganciclovir) will show the duration effect is present (treatment × time interaction p < 0.05) but its magnitude is ≤30% of what retrospective studies suggest, after controlling for placebo, natural recovery, and selection bias.
Treatment: N/A — methodological critique. Affects interpretation of existing trial results, not clinical recommendations.
Limitation: Synthesizes evidence from multiple studies at different certainty levels. No prospective data directly tests the alternative confounds in ME/CFS. The caveats similarly rest on indirect evidence and may themselves overestimate the importance of alternative explanations.

Open Question p=n/aPriority Research Directions to Test the Duration Critique in ME/CFS

Evidence: Four research programs derived from the duration criticism framework and the Lyme→ME/CFS analogy. (1) DeLong-style biostatistical reanalysis of all 35 ME/CFS treatment trials @[“Smith2015ME/CFSTxSysRev”] (DeLong et al. 2012). (2) Head-to-head 6mo vs 12mo valganciclovir RCT (Montoya et al. 2013) (Watt et al. 2012). (3) Prospective illness-duration-stratified trial testing the 2–8yr treatment window (Strayer, Young, and Mitchell 2020). (4) Adaptive platform trial with duration escalation based on interim response. Origin: brainstorm.
Citations: @[“Smith2015ME/CFSTxSysRev”] (DeLong et al. 2012) (Montoya et al. 2013) (Watt et al. 2012) (Strayer, Young, and Mitchell 2020)
Mechanism: Each direction directly tests one component of the duration critique without relying on the Lyme analogy. The DeLong-style reanalysis quantifies how many ME/CFS “negative” trials were structurally underpowered. The head-to-head duration RCT isolates the duration variable. The stratified trial prospectively tests the treatment-window hypothesis. The adaptive platform maps the dose-duration-response curve.
Chapter ref: ch32: duration critique research
Prediction: The DeLong-style reanalysis will find $>=$ 60% of ME/CFS treatment trials lacked adequate power for clinically meaningful effects. The 12-month valganciclovir arm will outperform the 6-month arm with a moderate effect size (Cohen’s d 0.3–0.5). The 3–8 year stratum will show the highest response rate, statistically distinguishable from less than 3 and more than 8 year strata.
Treatment: Indirectly: positive results would support longer treatment durations in clinical practice for virus-selected patients within the treatment window. Currently, all four research programs are hypothetical — no prospective duration-randomized data exist.
Limitation: None of the proposed studies have been conducted. The DeLong-style reanalysis relies on published (potentially selectively reported) data. The RCT designs are hypothetical and may prove infeasible due to cost, recruitment, or toxicity concerns with extended valganciclovir (myelotoxicity). Origin: brainstorm.

6 Pharmacodiagnostic Matrix

ID / Label	Details	Phase / Cert
sec pharmacodiagnostic-matrix	Pharmacodiagnostic matrix: formal multi-drug diagnostic inference — every medication cross-indexed against every mechanistic hypothesis, using constraint-satisfaction scoring to localize bottlenecks from multi-drug response patterns. Extends the extended diagnostic algorithm (Synthesis: Differential Diagnostic Algorithm (Extended)) from sequential probing to formal matrix-based inference. Methodological precedents: diagnosis ex juvantibus (Laragh 1988), N-of-1 Bayesian inference (Samuel 2019/2023), response-based stratified treatment (Zhang 2022), computational phenotyping (Strauss 2021), active learning diagnostic decision trees (Ravichandran 2024). Origin: methodology proposal.	Phase 1 / 0.30
sec pharmacodiagnostic-matrix, parameter stability	Test-retest reliability of drug-response-derived pharmacodiagnostic scores is unknown. Computational phenotyping literature (Schaaf 2024) demonstrates ICC 0.01–0.71 for RL model parameters. Within-patient variability (placebo, fluctuation, regression to mean) may similarly degrade pharmacodiagnostic parameter stability. Research needed before clinical deployment. Origin: methodology integration.	Phase 2 / n/a
sec pharmacodiagnostic-matrix, labeling bias	Matrix cell assignment (expected response direction) requires clinician judgment. Ravichandran 2024 demonstrated that clinician heuristics degrade diagnostic inference below random. Independent double-labeling + inter-rater reliability measurement needed before clinical deployment. Origin: methodology integration.	Phase 2 / n/a
sec pharmacodiagnostic-matrix, validation cohort	Validation: apply matrix retrospectively to patients with known mechanism (GPCR AAb confirmed, TRPM3 validated). Verify matrix places highest probability on correct mechanism. Validate against Scheibenbogen’s IA cohort, Stanford/System’s iCPET cohort, NIH ME/CFS study medication histories. Feasible within 6 months — no new trials required. Origin: methodology proposal.	Phase 2 / n/a

7 Stigmatisation and Nomenclature

Hypothesis p=0.50Perceived Stigma Mediates Worse Functional Outcomes in ME/CFS via Causal Attribution Pathway

Evidence: Froehlich 2022 (n=499 self-diagnosed ME/CFS): perceived stigma mediates relationship between negative causal attributions (controllable/unstable causes) and lower satisfaction with social roles + functional status. Looper 2004 (n=203): perceived stigma in FSS independently associated with worse health outcomes vs medically explained conditions, with ME/CFS among the most affected. Ko 2022 systematic review: stigma in FSS consistently associated with worse health outcomes — depression, lower QOL, higher symptom burden. (Froehlich et al. 2022) (Looper and Kirmayer 2004) (Ko et al. 2022)
Citations: (Froehlich et al. 2022) (Looper and Kirmayer 2004) (Ko et al. 2022)
Mechanism: Negative causal attributions (psychosomatic framing, controllable/unstable cause beliefs) → perceived stigma → reduced social role satisfaction + lower functional status + diminished health-related QOL. Stigma is not merely a psychosocial burden — it is an independent mediator of worse disease trajectories. Causal attributions by clinicians, family, and the public shape patient outcomes through stigma pathway.
Chapter ref: ch01: psychosocial
Prediction: Longitudinal study demonstrating that reduction in perceived stigma (via clinician validation or biomedical diagnosis) predicts improvement in functional status and social role satisfaction at 12-month follow-up. Falsified if changes in perceived stigma are not associated with changes in functional outcomes over time.
Treatment: Clinician validation + biomedical education may serve as therapeutic interventions by reducing perceived stigma. No clinical recommendation for formal stigma-targeted therapy without controlled trial data.
Limitation: Cross-sectional design — mediation pathway inferred, not demonstrated longitudinally. Self-diagnosed samples may overrepresent high-stigma patients. FSS stigma review primarily cross-sectional studies.

Hypothesis p=0.45Illness Name (“Chronic Fatigue Syndrome”) Is an Independent Vector of Stigma

Evidence: Jason 2002 (n=143): experimental vignette study — “chronic fatigue syndrome” label generated significantly more negative attributions (lazy, less responsible, less likely to recover) than “myalgic encephalopathy.” IOM 2015 report proposed renaming to SEID specifically to reduce stigmatising effects. (Leonard A. Jason et al. 2002) (Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 2015)
Citations: (Leonard A. Jason et al. 2002) (Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 2015)
Mechanism: The illness name “chronic fatigue syndrome” trivialises the condition — it emphasises a universal human experience (fatigue) while omitting PEM, the hallmark symptom. The name activates stereotypes of laziness/malingering, reducing clinician seriousness, social support, and patient self-perception. Name-based stigma is a modifiable structural determinant of healthcare interactions.
Chapter ref: ch34: nomenclature controversy
Prediction: Randomised experiment: clinicians presented with identical clinical vignettes but different diagnostic labels show measurable differences in treatment recommendations (biomedical investigation vs psychosocial referral), seriousness ratings, and empathy scores. “Myalgic encephalomyelitis” should elicit more biomedical investigation and higher seriousness ratings than “CFS.” Falsified if diagnostic label has no effect on clinician behaviour.
Treatment: Adopting less stigmatising nomenclature (ME, myalgic encephalomyelitis, SEID) may reduce clinical dismissal and improve diagnostic legitimacy. No clinical recommendation — this is a policy/advocacy issue.
Limitation: Single experimental vignette study (n=143) — partial replication only. Vignette studies may not fully capture real-world clinical behaviour. Name change alone cannot overcome decades of ingrained attitudes.

Hypothesis p=0.45ME/CFS Stigma Constitutes Comparable or Higher Burden than Other Functional Somatic Syndromes

Evidence: Looper 2004 (n=203): ME/CFS perceived stigma comparable to or higher than other FSS, substantially higher than medically explained conditions. Ko 2022 systematic review: stigma in FSS systematically associated with worse health outcomes; ME/CFS among most affected conditions in the FSS category. (Looper and Kirmayer 2004) (Ko et al. 2022)
Citations: (Looper and Kirmayer 2004) (Ko et al. 2022)
Mechanism: Conditions without visible signs or validated biomarkers attract greater delegitimisation irrespective of biological basis. ME/CFS sits at the extreme of this spectrum: contested nosology + psychiatric framing + invisible symptoms + fatigue-focused name → maximal delegitimisation. The pattern is structural — it tracks illness legitimacy perception, not disease severity.
Chapter ref: ch01: psychosocial
Prediction: Head-to-head comparison of perceived stigma, healthcare dismissal experiences, and public attitudes across ME/CFS, IBS, fibromyalgia, and medically explained conditions (RA, MS) should show ME/CFS at the extreme of the delegitimisation spectrum. Falsified if ME/CFS shows comparable stigma to RA or MS.
Treatment: De-stigmatisation strategies developed for other contested illnesses (MS historical trajectory, peptic ulcer/H. pylori paradigm shift) may be applicable to ME/CFS. Policy/advocacy implications.
Limitation: Only one head-to-head comparison study (Looper 2004). Systematic review aggregates across heterogeneous stigma measures. Public attitudes may have evolved since 2004.

Prediction p=0.35Biomedical Validation as Anti-Stigma Intervention with Measurable Clinical Benefit

Evidence: Qualitative evidence consistently shows that receiving a biomedical diagnosis and validation from a knowledgeable clinician is the most powerful anti-stigma intervention reported by patients (Guise 2010, Melby 2024, Asbring 2002). Terman 2020 validated stigma scale has not been tested as a clinical outcome measure. (Guise, McVittie, and McKinlay 2010) (Melby and Nair 2024) (Asbring and Närvänen 2002) (Terman et al. 2020)
Citations: (Guise, McVittie, and McKinlay 2010) (Melby and Nair 2024) (Terman et al. 2020)
Mechanism: Biomedical diagnosis + clinician validation → reduced perceived stigma → improved treatment engagement, reduced distress, potential biological benefit via stress pathway attenuation. Stigma reduction may be a measurable clinical outcome rather than merely a process variable.
Chapter ref: ch01: psychosocial
Prediction: Pre/post study: administer Terman 2020 ME/CFS Stigma Scale at first specialist ME/CFS clinic appointment and at 3-month follow-up after biomedical diagnosis and education. Stigma scores should decrease ≥1 SD. Reduction should correlate with improved treatment adherence and QOL. Concurrent biomarker measurements (CRP, IL-6, morning cortisol) should show improvement trajectory paralleling stigma reduction. Falsified if stigma scores do not change after biomedical diagnosis and education.
Treatment: ME/CFS-specialised clinical services with biomedical emphasis may provide measurable clinical benefit beyond diagnosis alone through stigma reduction. No specific anti-stigma intervention protocol validated.
Limitation: Qualitative evidence only — no pre/post stigma measurement study exists. Stigma scale not validated as outcome measure. Service context effects — specialist clinic patients may differ from community patients. Regression to the mean possible.

Speculation p=0.30Healthcare Dismissal as Chronic Stressor Accelerating Disease Progression

Evidence: Qualitative evidence spanning 25 years (1999–2024): healthcare dismissal — being disbelieved, trivialised, or labelled as psychiatric — is the most consistent finding across qualitative ME/CFS research. Stigma operates as a chronic psychosocial stressor; chronic stress is a known accelerator of inflammatory, metabolic, and neurodegenerative processes. McManimen 2018: unsupportive social interactions independently predict suicidal ideation after controlling for depression (n=495). (Asbring and Närvänen 2002) (Guise, McVittie, and McKinlay 2010) (Melby and Nair 2024) (Deale and Wessely 2001) (Stephanie L. McManimen et al. 2018)
Citations: (Stephanie L. McManimen et al. 2018) (Guise, McVittie, and McKinlay 2010) (Melby and Nair 2024)
Mechanism: Healthcare dismissal → chronic psychosocial stress → HPA axis dysregulation, sympathetic overactivity, increased inflammatory signalling → worsened disease trajectory. Stress-induced glucocorticoid resistance in immune cells → unresolved inflammation → amplification of existing ME/CFS pathophysiology (mitochondrial, autonomic, immune). Stigma → stress → biological amplification — not merely psychological suffering.
Chapter ref: ch01: psychosocial
Prediction: ME/CFS patients who report high healthcare dismissal should show elevated inflammatory markers (CRP, IL-6) and flattened cortisol diurnal slope vs those reporting low dismissal, after controlling for disease severity. Reduction in experienced dismissal (following change to validating clinician) should be associated with reduction in inflammatory markers at 6-month follow-up. Falsified if no association exists between dismissal and inflammatory markers.
Treatment: Clinician training in validation and trauma-informed care may have biological as well as psychological benefits — not merely “improving patient satisfaction.” No clinical recommendation beyond standard trauma-informed and validation approaches.
Limitation: Entirely inferred — no study has directly measured the biological (neuroendocrine/immune) effects of healthcare dismissal in ME/CFS patients. Qualitative evidence establishes prevalence and distress; biological mechanism remains hypothetical. Confounding by disease severity is possible (more severe patients both elicit more dismissal AND have worse inflammatory profiles).

Speculation p=0.30Media Framing Amplifies ME/CFS Stigma in Healthcare Encounters

Evidence: Knudsen 2011 (n=280 UK newspaper articles): 70% emphasised psychological/behavioural characterisations. Media psychiatric framing reinforces public perception of the disease as non-serious or self-inflicted. Patients report media coverage directly harming their social and healthcare interactions. (Knudsen et al. 2011)
Citations: (Knudsen et al. 2011)
Mechanism: Media coverage→public perception of ME/CFS as psychological/behavioural→social disbelief + clinician scepticism→patient experiences dismissal in medical encounters→amplified perceived stigma→worse health outcomes. Media is an upstream structural determinant of stigmatising healthcare interactions.
Chapter ref: ch01: psychosocial
Prediction: Temporal analysis: periods of increased psychiatric framing in media should be followed (within 3-6 months) by measurable increases in perceived stigma in clinical cohorts and online patient communities. Positive shift: after major biomedical discovery coverage, patient-reported dismissal should transiently decrease. Falsified if no temporal association between media framing and patient stigma reports.
Treatment: Strategic science communication — active media engagement by biomedical researchers to counter psychiatric framing narratives. Policy/media recommendation, not clinical intervention.
Limitation: Single content analysis (2011 UK only) — media landscape has changed substantially. Causal direction unclear — media may reflect rather than shape public attitudes. Social media, not just traditional press, now dominates information environment.

Speculation p=0.25Psychiatric Embedding in CFS Services Generates Structural Stigma

Evidence: Terman 2019 (n=336): mandatory or structurally embedded psychiatric referral in CFS services perceived by patients as delegitimising. This effect is pronounced in health systems where psychiatry gatekeeps CFS services (historically UK NICE pre-2021 model). Hussein 2024 scoping review (14 sources): lack of diagnostic pathways, contested nosology, insufficient clinician education, and absence of specialised services create structural stigma beyond individual clinician bias. (Terman, Cotler, and Jason 2019) (Hussein et al. 2024)
Citations: (Terman, Cotler, and Jason 2019) (Hussein et al. 2024) (Bayliss et al. 2014)
Mechanism: When psychiatry gatekeeps access to CFS services, the system structurally communicates that the condition is psychological — regardless of individual clinician intentions. Structural stigma operates through service design (psychiatric intake, CBT/GET as default treatments, absence of biomedical workup) rather than individual attitudes. Patients internalise this framing, reducing treatment engagement and hope.
Chapter ref: ch34: nomenclature section
Prediction: Comparison of patient outcomes (diagnostic confidence, treatment engagement, satisfaction, perceived stigma) in health systems with psychiatry-gatekept CFS services vs biomedically-led services (e.g., Norway biomedical clinics, post-2021 UK NICE). Psychiatry-gatekept systems should show higher perceived stigma and lower treatment engagement. Falsified if no difference in perceived stigma between service models.
Treatment: Separation of ME/CFS from mental health services — biomedical clinical pathways with psychiatry as optional consultation, not gatekeeping. Policy recommendation, not clinical guideline.
Limitation: Single cross-sectional study (n=336). Causal direction unclear — patients with higher pre-existing stigma may be more likely to perceive structural delegitimisation. System comparison observational only — no randomised service model studies exist.

Open Question p=n/aChild and Adolescent ME/CFS Stigma Understudied and Potentially Amplified by Educational Settings

Evidence: Parslow 2017: systematic review of qualitative child ME/CFS studies finds stigma as recurring theme — from teachers, peers, and healthcare providers. Children face unique stigmatisation risks: educational consequences (school refusal accusations, grade loss), dependency on adults for care access, limited agency in healthcare encounters. No dedicated child/adolescent ME/CFS stigma study exists. Parslow et al., 2017 qualitative systematic review identifies stigma as recurring theme in child ME/CFS studies — teachers, peers, healthcare providers.
Citations: —
Mechanism: Children with ME/CFS are triply vulnerable: invisible illness + developmental stage (perceived as malingering/coping avoidance) + dependency on adults who may disbelieve them. Stigma in educational settings — teachers and school administrators dismissing illness as avoidance — may compound health-related stigma, causing educational harm independent of physical illness burden.
Chapter ref: ch01: psychosocial
Prediction: Adapt the Terman 2020 stigma scale for paediatric populations and administer to adolescent ME/CFS patients + age-matched controls with T1DM (visible/manageable chronic illness) and juvenile arthritis (medically legitimised). ME/CFS adolescents should show highest stigma scores. Educational outcomes (grades, attendance, attainment) should correlate inversely with stigma in ME/CFS but not in comparison groups. Falsified if ME/CFS adolescents show stigma levels comparable to T1DM controls.
Treatment: Paediatric-specific anti-stigma interventions: school-based education, teacher training, formal educational support plans that acknowledge illness legitimacy. No clinical recommendation — research gap first.
Limitation: No direct paediatric stigma data. Paediatric stigma scale not developed. Parslow 2017 focuses on experiences broadly, not stigma specifically.

8 Economic Impact

ID / Label	Details	Phase / Cert
ach:iom-benchmark	IOM 2015 established baseline: 836,000–2.5M Americans affected; USD 17–24B annual economic cost; USD 18,000–29,000 per patient per year; indirect costs ~2/3 of total. Modeled extrapolation from prevalence surveys, not direct cost measurement. IOM + Clayton 2015 share same evidence base — treated as one source.	Phase 3 / 0.77
ach:australian-costs	Two independent Australian cost-of-illness studies converge on AU$14.5B annual (Zhao 2023 n=128, AU$28,800/patient) and AU$14,523/patient mean annual cost (Close 2020 n=485, 73% indirect costs, 16.6% employed). Cross-national replication of IOM magnitude.	Phase 3 / 0.70
ach:kce-belgium	KCE Belgian HTA needs assessment (Cornelis 2026, n=749): mean 8.6yr work invalidity; 73.2% ≥1 comorbidity; 50% >2yr diagnostic delay. Government HTA body formally evaluating ME/CFS signals institutional recognition.	Phase 3 / 0.68
ach:germany-cost-report	Risklayer 2026 Germany cost report: EUR 318.8B combined LC+ME/CFS cost 2020–2025; EUR 64.4B in 2025 = 1.44% of German GDP. Prevalence end-2025: 756,808 LC + 656,951 ME/CFS (>1.4M); ME/CFS rising, LC falling; ME/CFS recovery ~5%/yr. Gray-literature Monte Carlo model — modeled, not measured; authors state likely underestimate (non-COVID ME/CFS held constant). Walter 2022 confirms direct-cost component.	Phase 3 / 0.55
spec:growing-steady-state	ME/CFS prevalence has not reached steady state: positive incidence against ~5%/yr recovery implies eventual prevalence far above the current 656,951, so annual societal cost keeps climbing even if new SARS-CoV-2 conversions stop. Arithmetic consequence of the report’s own inputs, conditional on the load-bearing infection correction.	Phase 5 / 0.40
ach:employment-gap	Employment rates 16.6–27% across studies vs 60–84% general population. >75% unable to work (Podell 2020). NZ administrative data (Bowden 2026, n=1,902): 18.3% employed vs 83.8% general population. Spanish cohort (Castro-Marrero 2019): 52.3% unemployed, 45.3% on sick leave. Multiple countries, consistent direction.	Phase 3 / 0.75
oq:employment-retention	No study has tested workplace accommodations, flexible scheduling, remote work, or phased return-to-work programs for ME/CFS. Intervention question well-defined and testable; no funding body has prioritized it.	Phase 3 / 0.55
ach:caregiver-burden	Brittain 2021 (n=68 patients + 118 family members, FROM-16 tool): 87% family moderate/severe quality-of-life impact; 50.2% family-income reduction. No standalone monetary caregiver valuation exists. Informal care included in Australian cost-of-illness indirect-cost estimates.	Phase 3 / 0.55
ach:burden-funding-ratio	Mirin 2020: ME/CFS burden:funding ratio ~1,000:1 (USD 17–24B cost vs USD 15M NIH funding). MS comparator ~30:1 (USD 39,000–68,000/patient vs USD 115M); RA comparator ~32:1 (USD 12,000–21,000/patient vs USD 86M). Three-order-of-magnitude gap. Funding figures public NIH data and verifiable.	Phase 3 / 0.65
ach:germany-funding-gap	German federal research spending ~EUR 40M/yr = 0.06% of 2025 cost. Since 2022: 22% biomedical (EUR 50M) vs 71% healthcare-services (EUR 157M) — services that do not alter disease state in PEM-characterized ME/CFS. National Decade Against Post-infectious Diseases: EUR 500M/10yr (EUR 50M/yr). Gandjour 2024 peer-reviewed model: optimal German ME/CFS R&D ~EUR 676M; prospective drug ~EUR 2.6B societal value.	Phase 3 / 0.55
lim:no-roi	No formal return-on-investment analysis for ME/CFS research funding. Burden:funding ratio is descriptive, not causal. Cochrane 2021 systematic review confirmed zero cost-effectiveness studies for any ME/CFS intervention. This limitation is itself a consequence of the underfunding it critiques.	Phase 3 / n/a
lim:no-cea	Cochrane 2021: six pre-2021 economic evaluations, all CBT/GET-based, none based on modern non-GET care models. After NICE 2021 GET reversal, pre-2021 CEA evidence is not just thin — its clinical basis is obsolete. Wan 2024 bibliometric analysis confirms tiny, fragmented field.	Phase 3 / n/a
spec:structural-neglect	ME/CFS comparable to MS/RA on severity, prevalence, and economic burden but receives 7–13% of their per-DALY research funding. Gap too large to be explained by any single factor — likely self-reinforcing cycle: low funding → few researchers → slow progress → perceived intractable → continued low funding. Testable: dedicated research program with evaluation component.	Phase 3 / 0.35
oq:missing-evidence	Five identified gaps: (1) no US claims-data cost study; (2) no longitudinal lifetime-cost study; (3) no cost-effectiveness of diagnostic pathway; (4) no caregiver monetary valuation; (5) no developing-country data. All feasibly addressable with existing methods; gaps confirmed by systematic review (Cochrane 2021, Wan 2024).	Phase 3 / 0.60
syn:economic-impact-model	Cross-country synthesis: USD 18,000–29,000/patient/year; employment ~20% vs ~80% general; caregiver 50% income reduction; 1,000:1 burden:funding ratio; 30–40x worse than MS/RA. Strongest supported conclusion: current funding level unjustifiable on any cost-effectiveness principle. Policy change requires demonstrating current funding is indefensible, not computing optimal level.	Phase 3 / 0.65

9 Global Perspectives on ME/CFS

ID / Label	Details	Phase / Cert
ach:postinfectious-burden-geographic	Post-infectious fatigue follows infectious disease burden geographically. Dengue (endemic in 100+ countries, predominantly LMICs) produces chronic fatigue comparable to post-EBV CFS (Condé 2026, first SR). Chikungunya: 38% chronic fatigue at 30mo (Duvignaud 2018). Post-epidemic sequelae documented across history (Miller 2026). The countries with the most triggering infections have the least ME/CFS diagnostic infrastructure. Global ME/CFS research studies only the fraction of post-infectious fatigue cases occurring in places where ME/CFS is diagnosed — a geographic selection bias of unknown magnitude.	Phase 3 / 0.55
spec:lmic-fatigue-blind-spot	Hypothesis: If post-infectious ME/CFS occurs at comparable rates across all triggering infections, LMICs with highest infectious-disease burden should have highest ME/CFS prevalence — but they report the lowest because they have least diagnostic infrastructure. Single data point: Iran post-COVID CFS 17.5% (Simani 2021). No replication from other LMICs.	Phase 3 / 0.35
ach:neurasthenia-cultural-idiom	Cross-cultural evidence (Ware & Kleinman 1992, Lee 1998, Starcevic 1999): neurasthenia in China and CFS in the US are different cultural idioms for overlapping fatigue syndromes. Shenjing shuairuo persists as culturally acceptable idiom in Chinese society. ME/CFS prevalence estimates using Western diagnostic criteria may produce artefactually low prevalence in non-Western settings — core finding conceptually replicated; inference untested.	Phase 3 / 0.40
oq:cultural-idioms-beyond-east-asia	Neurasthenia/CFS literature covers China and East Asia. No empirical cross-cultural ME/CFS studies from South Asia (kamzori, vata imbalance), Sub-Saharan Africa (post-malaria fatigue, “thinking too much”), or Latin America. Culturally validated ME/CFS screening tools do not exist for non-Western settings.	Phase 3 / n/a
ach:clinical-diagnosis-without-testing	IOM/CCC criteria require exclusion of alternative diagnoses via laboratory testing. In LMICs without reliable TSH, CBC, ferritin, CRP, or ANA testing, the diagnostic algorithm cannot be executed as written. Bangladesh RCT protocol (Sarker 2024) applies Fukuda criteria pragmatically. ICD codes exist globally; the diagnostic pathway that justifies assigning them does not.	Phase 3 / 0.50
ach:icd-coding-disability-gap	ME/CFS coded as G93.3 (ICD-10), 8E49 (ICD-11). ICD coding exists globally, but disability recognition requires national systems that operationalise the code. In LMICs where disability insurance is weak or absent, ICD coding confers no practical benefit. Policy wins at the global level do not translate to protection at the local level without national adoption.	Phase 3 / 0.50
spec:tcm-herbal	TCM herbal formulations show signal for fatigue reduction in 23 RCTs (n=1,776), but all low quality — small samples, high risk of bias, heterogeneous interventions (Wang 2014). Evidence insufficient for specific formulation recommendation.	Phase 3 / 0.50
spec:tcm-mind-body	Tai Chi, Qigong, Baduanjin show moderate effect on fatigue with no SAEs (Kong 2023 SR/MA). Accessible in LMIC settings where these practices are culturally embedded and low-cost. Generalisability to non-Chinese populations uncertain. PEM risk unstudied in these protocols — severe/very severe patients unlikely to tolerate.	Phase 3 / 0.50
spec:acupuncture-moxibustion	Acupuncture/moxibustion show small-to-moderate effect in 31 RCTs (Wang 2017 NMA). Widely available across East Asia and Indian AYUSH integration. Low-risk symptom-management option; not disease-modifying. Blinding limitations inherent to acupuncture RCTs.	Phase 3 / 0.50
lim:tcm-evidence-fractional	Traditional medicine evidence base for ME/CFS is almost entirely TCM. Systematic reviews of Ayurveda, Siddha, Unani, and African traditional medicine for chronic fatigue are absent from PubMed. Cannot generalise from TCM to other traditional medicine systems.	Phase 3 / n/a
ach:neglected-disease-framing	ME/CFS satisfies criteria for a neglected disease (Arron 2024): severe morbidity (EQ-5D ~0.40–0.55, worse than MS/stroke), mechanisms understudied, research concentrated in high-income countries. No WHO global ME/CFS initiative exists. No ME/CFS disability weight in GBD. National Academies IACI workshop (2024) identified cross-disease mechanisms but lacked LMIC representation.	Phase 3 / 0.55
oq:global-surveillance	No international surveillance system tracks post-infectious fatigue following dengue/chikungunya/COVID-19 outbreaks in LMICs. Bangladesh RCT protocol (Sarker 2024) provides small-scale model. Until a surveillance system exists, every assertion about global ME/CFS prevalence is an extrapolation.	Phase 3 / n/a
lim:unfalsifiability-trap	The hidden-burden thesis has unrestricted auxiliary hypotheses to absorb falsification — any null finding can be attributed to cultural inappropriateness of criteria, unavailable exclusionary workup, or wrong triggering infection. A non-falsifiable claim should carry lower certainty than structural plausibility alone warrants. The chapter must specify what evidence would change its mind. Origin: brainstorm Phase 4, categories 10 and 12.	Phase 5 / n/a
oq:falsifying-hidden-burden-thesis	Three categories of evidence would weaken/refute the hidden-burden thesis: (1) differential pathogen profiles — if post-infectious ME/CFS conversion rates vary by order of magnitude across triggering infections; (2) protective factors — helminth immune modulation, early-life immune training, younger population structure; (3) competing mortality — susceptible patients die of acute infection in LMICs. All counterarguments and the thesis they challenge rest on inference from the same absence of data. Origin: brainstorm Phase 4, categories 10 and 11.	Phase 5 / 0.20
spec:icd11-empty-vessel	ICD-11 8E49 is globally available — but the implementation pathway (national HIT adoption, physician training, diagnostic algorithms, disability systems, clinical guidelines) requires conditions absent from most LMICs. The code may produce zero measurable change in diagnosis rates where none of the 5 implementation conditions hold. Origin: brainstorm Phase 4, category 1.	Phase 5 / 0.40
spec:sentinel-pem-surveillance	Lowest-cost highest-value research action: add PEM-screening module (DSQ-PEM, 1-min sit-to-stand) to existing post-dengue/chikungunya/Zika cohort follow-ups in Brazil, India, Vietnam, Thailand. USD 150–300K, 5 sentinel sites, no lab infrastructure needed. Would generate first post-arboviral ME/CFS prevalence estimates. Origin: brainstorm Phase 4, category 2.	Phase 5 / 0.40
oq:culturally-adapted-pem-tool	PEM screening translated into Hindi, Bengali, Swahili cannot just translate “do you feel worse after exertion?” — must culturally calibrate exertion concepts, rest/work boundaries, and fatigue idioms. Cognitive interview methodology standard; gold-standard circularity problem: need culturally adapted criteria to identify patients to validate the tool. Origin: brainstorm Phase 4, category 2.	Phase 5 / 0.35

10 History of ME/CFS

ID / Label	Details	Phase / Cert
fhyp:psychosomatic-lag	When an illness cluster appears with consistent clinical phenotype, negative standard laboratory tests, female predominance, and absence of mortality, the default medical response is psychosomatic attribution. Documented across >5 diseases: ME/CFS (45 years McEvedy→IOM), MS (>100 years Charcot→MRI), fibromyalgia (~20 years), Gulf War Illness (19 years), Long COVID (~18 months). Compression trend observable. Mechanism: medicine’s evidentiary hierarchy privileges positive lab findings over clinical phenomenology. Predicts next unexplained cluster will follow same trajectory within 10 years. Origin: brainstorm.	Phase 5 / 0.65
oq:institutional-incentives-over-evidence	McEvedy 1970 mass hysteria hypothesis was empirically weak but dominated clinical teaching for 40 years — was this driven by evidence quality or institutional incentives (reduced disability claims, psychiatric department jurisdictional expansion, confirmation bias under McEvedy’s logic)? Falsifiable via analysis of disability claim denial rates, psychiatric department funding, and guideline change timing across diseases. If evidence quality is primary driver, the hypothesis is disproven. Origin: brainstorm — null hypothesis assessment.	Phase 5 / 0.70
lim:retrospective-outbreak-diagnosis	Pre-1988 outbreak reports (Gilliam 1938, Royal Free 1957, Acheson 1959) cannot be validated against modern diagnostic criteria — no archival samples exist. Retrospective diagnostic assignment is inductive. Alternative: multiple distinct post-infectious syndromes with similar clinical description. Origin: brainstorm — evidence quality.	Phase 5 / 0.55
lim:evidence-quality-outbreak-sources	Foundational ME/CFS documents are non-peer-reviewed field investigation reports and case series, not modern peer-reviewed studies. DecodeME 2025 is a preprint. Core institutional events (IOM 2015, NICE 2021) are higher-certainty. Origin: brainstorm — evidence quality.	Phase 5 / n/a
lim:narrative-arc-confirmation-bias	Chapter’s psychosomatic-to-biological arc is one defensible reading, not the only reading. A skeptic could organize the same events differently: provisional psychiatric explanation → continued negative biological investigation → patient advocacy pressured institutions → guidelines reversed → tentative biological signals. Transparency about interpretive choice. Origin: brainstorm — evidence quality.	Phase 5 / n/a
lim:asymmetrical-methodological-scrutiny	Chapter applies detailed criticism to PACE trial (n=641, Lancet) but less scrutiny to biological evidence: Walitt 2024 (n=17, small), DecodeME 2025 (preprint). Asymmetry defensible because PACE shaped clinical guidelines affecting hundreds of thousands, but should be acknowledged. Origin: brainstorm — evidence quality.	Phase 5 / n/a
lim:history-not-therapeutic	Historical knowledge does not reduce PEM, improve sleep, or restore function. Chapter value is epistemic (understanding how medicine arrived here) and strategic (recognizing patterns), not therapeutic. Inclusion justified by document scope, not immediate patient benefit. Origin: brainstorm — self-critique.	Phase 5 / n/a

11 Subjective-Measurable Discrepancy Index

ID / Label	Details	Phase / Cert
ach:subjective-measurable-discrepancy-domains	The subjective-measurable discrepancy — patients report severe symptoms that standard clinical tests fail to capture or contradict — is a replicated, multi-domain, diagnosis-independent finding documented across sleep (Watson2003, Watson2004, Armitage2009 — MZ twin-controlled), autonomic (Novak2024 — n=2627, zero subjective-objective correlation), physical activity (Vergauwen2021), cognition (Teodoro2018 — 186-study review), and systemic biomarkers (Byrnes2009 — zero peripheral blood transcriptomic signal in 44 twin pairs). Seven independent research cohorts, convergent designs, zero contradictory findings. Not explained by psychiatric comorbidity, poor effort, deconditioning, or instrument invalidity. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.75
syn:subjective-measurable-discrepancy-index	Synthesis of three non-exclusive explanations for the discrepancy: (1) provocation-dependence — abnormalities emerge only under stress, standard resting labs miss them; (2) CNS confinement — primary pathology is brainstem/hypothalamic/microglial, not reflected in peripheral blood; (3) interoceptive amplification — patients perceive real physiological signals that healthy brains filter out. Together they reframe the discrepancy from “patients report what tests can’t confirm” to “patients report what standard tests can’t measure.” Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.75 (inherited from constituent evidence)
hyp:discrepancy-index-dysautonomia-subtype	A formal discrepancy index — subjective autonomic symptom score (COMPASS-31) ÷ objective autonomic deficit (QASAT) — may subtype patients: high discrepancy → dysautonomia-dominant (normal resting, abnormal only on provocation, autonomic-responsive); low discrepancy → immune-inflammatory (abnormal resting biomarkers, immunomodulation-responsive). Predicts treatment stratification: high-discrepancy patients respond better to midodrine/pyridostigmine; low-discrepancy patients respond better to IVIG/corticosteroids. Untested. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.45
spec:discrepancy-index-interoceptive-accuracy	Alternative framing: the discrepancy index is a readout of interoceptive processing gain — how precisely the brain weights ascending interoceptive signals. High-discrepancy patients have high-gain interoceptive systems perceiving signals that healthy brains filter out. Reframes discrepancy as neurobiological trait, not disease classification. Predictive processing framework, testable via heartbeat detection and respiratory resistance discrimination tasks. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.30
lim:discrepancy-index-validation-gap	The discrepancy index is a conceptual framework, not a validated clinical instrument. No standardised formula exists; no test-retest reliability, sensitivity/specificity, or clinical utility data exist. Structural issues: domain selection/weighting, provocation standardisation, severity dependency (may invert in severe patients), threshold calibration, and domain-specific convergence (HGS correlates — Paffrath2024). Should be treated as hypothesis-generating, not a clinical decision tool. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / n/a
oq:bridging-subjective-objective-gap	Is there any instrument that can bridge the COMPASS-31/QASAT orthogonality? Candidates: provocation-based patient-report, wearable + ecological momentary assessment, microstructural objective measures, challenge-test protocols. Null result (no instrument bridges the gap) would support CNS confinement — constructs are ontologically distinct, patient’s experience integrates CNS-level information inaccessible to any peripheral instrument. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / n/a
oq:convergence-domain-patterns	Why do some domains show subjective-objective convergence (HGS, HRV, CBF during tilt) while others show complete divergence (PSG, MSLT, QEEG, COMPASS-31/QASAT, peripheral blood transcriptomics)? Hypotheses: provocation intensity, measurement specificity, temporal coupling. Identifying systematic patterns guides instrument design — invest in high-provocation, high-specificity, temporally-coupled measures. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.30
spec:discrepancy-trajectory-predictor	Extrapolation from Friedberg2022 (lower HRV → nonimprovement, but patient-reported activity patterns did not discriminate): high-discrepancy patients (severe report, mild objective dysfunction) may have better prognosis than low-discrepancy patients (severe report, severe objective dysfunction). Alternatively: high discrepancy = early-stage (subjective distress precedes measurable pathology), low discrepancy = late-stage (measurable damage accumulated). If discrepancy predicts trajectory, index becomes treatment-allocation decision tool. Origin: /integrate-topic subjective-measurable-discrepancy-index.	Phase 3 / 0.25
spec:energy-conservation-signal	The brain’s hypothalamic AMPK/mTOR metabolic sensors detect ATP flux failure (rate insufficiency) even when metabolite pool sizes (blood glucose, lactate) are normal. Standard blood tests measure pools; the brain measures flux. Subjective fatigue reflects correct detection of flux insufficiency — no perceptual amplification needed. The discrepancy is a measurement domain mismatch (pools vs. flux), not a patient error. Impaired phosphocreatine recovery on 31P-MRS supports this; the hypothalamic link to fatigue perception is untested in ME/CFS. Origin: brainstorm — Phase 5, category 1.	Phase 5 / 0.30
spec:temporal-decoupling	Subjective symptom reports integrate across weeks (recall period of validated instruments), objective measures capture a single timepoint. If ME/CFS fluctuates, subjective report captures worst moments (peak-end rule); objective measure randomly samples from the distribution’s body. Discrepancy = distribution tail vs. random draw — would occur with perfect interoception. Vergauwen2021 (matched 12-day windows) still found rs=0.35-0.38 — temporal decoupling is partial only. Origin: brainstorm — Phase 5, category 1.	Phase 5 / 0.40
lim:instrument-selection-bias	Every study in the discrepancy evidence base uses multi-week-recall subjective instruments vs. single-timepoint objectives. The discrepancy is partly designed into the protocol — a consequence of retrospective-integrative vs. instantaneous-sampling instruments, not solely a discovery about the disease. Vergauwen2021 with matched 12-day windows still shows weak correlation — instrument bias is partial. Origin: brainstorm — Phase 5, category 12 (evidence quality).	Phase 5 / 0.60
lim:tissue-mismatch-byrnes2009	Byrnes2009’s null (zero PBMC transcriptomic signal) is cited as strongest evidence for CNS confinement. Alternative: tissue-mismatch — peripheral blood leukocytes are the wrong cell type for muscle/endothelial/brainstem/enterochromaffin pathology. The null is strong evidence against systemic transcriptomic signal in blood leukocytes; weak evidence for CNS confinement specifically. Muscle biopsy, endothelial isolation, or CSF proteomics might reveal the signal. Origin: brainstorm — Phase 5, category 12.	Phase 5 / 0.50
lim:severity-confound-fatal	The discrepancy index may measure severity, not subtype — mild (low objective, high subjective → high discrepancy), severe (high objective, high subjective → low discrepancy). The subtyping hypothesis survives only if discrepancy predicts treatment response WITHIN severity tiers. Ambiguity admitted but magnitude/direction unknown — without severity-stratified validation, subtyping is unfalsifiable. Origin: brainstorm — Phase 5, category 12.	Phase 5 / 0.45
lim:overestimated-certainty	The achievement’s cert 0.75 treats 5 domains as 5 independent replications. Reality: sleep uses one twin cohort twice (Watson2003, Armitage2009); autonomic is 1 study (Novak2024); activity found weak positive, not zero (Vergauwen2021 rs=0.35–0.38); cognition is a review, not primary data (Teodoro2018); biomarker is a tissue-specific null (Byrnes2009). Five legs, shared wood. More appropriate cert: 0.55–0.60. Origin: brainstorm — Phase 5, category 12.	Phase 5 / 0.55
oq:discrepancy-index-null-hypothesis	The default null for any novel clinical instrument: does the discrepancy index add diagnostic, prognostic, or therapeutic value beyond existing assessment (symptom severity + duration)? If all three arms fail the null, the index is scientifically interesting but clinically useless — an academic construct. Burden of proof is on demonstrating added value, not assuming it. Origin: brainstorm — Phase 5, category 11 (null hypothesis).	Phase 5 / n/a
oq:confound-explained-null	After controlling for severity, symptom fluctuation variance, psychiatric comorbidity (HADS), medication count/class, age, and gender — the ME/CFS-specific residual discrepancy may be zero. If so, the discrepancy is a quantitative amplification of normal interoceptive noise driven by severity, not an ME/CFS-specific mechanism. The framework collapses to “severe illness → large subjective-objective mismatch — any disease.” A surviving disease-specific residual confirms the discrepancy as an ME/CFS property. Origin: brainstorm — Phase 5, category 11.	Phase 5 / 0.40

Speculation p=0.20Null Subtype 1 — Absent: Receptor or Enzyme Never Present

Evidence: Receptor occupancy theory establishes that a null agonist response does not equate to receptor absence — it only indicates insufficient occupancy at that concentration. Bypass agonist logic (direct downstream activator confirming downstream machinery intact) is pharmacologically sound but never tested in ME/CFS. (Finlay, Duffull, and Glass 2020)
Citations: (Finlay, Duffull, and Glass 2020)
Mechanism: Receptor, ion channel, enzyme, or transporter never existed in patient’s tissue — or lost irreversibly. Drug never had a target. Bypass probe: give downstream agonist; if bypass produces response, receptor was absent. If bypass fails, downstream machinery also broken.
Chapter ref: ch33: null subtyping — absent vs blocked vs overwhelmed
Prediction: In patients with TRPM3 null response to pregnenolone, calcium ionophore (A23187 or ionomycin) produces calcium flux in PBMCs — confirming TRPM3 is specifically absent while calcium signalling machinery is intact. Falsified if ionophore also produces null, though this would suggest broader calcium signalling defect rather than TRPM3-specific absence.
Treatment: None — absent target cannot be targeted. Treatment shifts to downstream pathways or unrelated mechanisms.
Limitation: No published ME/CFS study has tested bypass-agonist logic systematically. Probe requires in vitro or ex vivo setup (PBMC calcium flux, functional receptor assay) not available in standard clinical settings.

Speculation p=0.20Null Subtype 2 — Blocked: Receptor Present but Orthosteric Site Inaccessible

Evidence: GPCR autoantibodies can act as orthosteric antagonists, allosteric modulators, biased agonists — well-established pharmacology (Skiba and Kruse 2021). GPCR AAb documented in POTS (allosteric modulation (Sunami, Sugaya, and Takahashi 2025)) and ME/CFS (detection methods contested — Germain2025 REAP/Luminex null vs CellTrend ELISA positive). β-arrestin-biased AAb signalling produces chronic functional blockade distinct from simple orthosteric antagonism (Dodge-Kafka et al. 2026).
Citations: (Skiba and Kruse 2021) (Sunami, Sugaya, and Takahashi 2025) (Dodge-Kafka et al. 2026)
Mechanism: Receptor structurally present but orthosteric site occupied — by autoantibody, competitive endogenous ligand, or PTM locking inactive conformation. Probe: allosteric ligand binding different site may produce response when orthosteric agonist fails. If allosteric works → blocked, not absent. If both fail → absent or allosteric site also disrupted.
Chapter ref: ch33: null subtyping — absent vs blocked vs overwhelmed
Prediction: In β2-AR AAb-positive ME/CFS patients (by validated functional assay), allosteric β2-AR modulator (e.g. Cmpd-6 or biased ligand) produces measurable cAMP response in PBMCs while orthosteric agonist (isoproterenol) does not — confirming orthosteric-site blockade. Falsified if both produce null, suggesting receptor absence or broader signalling defect rather than site-specific blockade.
Treatment: Immunoadsorption or plasmapheresis to remove AAb, then retest orthosteric drug. B-cell depletion for AAb source elimination. Allosteric bypass as alternative strategy when AAb removal is impractical.
Limitation: GPCR AAb detection in ME/CFS is contested — largest screen (Germain2025 n=172) null. Probe requires validated functional GPCR AAb assay not clinically available. No published study has tested allosteric bypass in AAb-positive ME/CFS patients. If GPCR AAb in ME/CFS are non-pathogenic or measurement artefacts, this null subtype collapses.

Speculation p=0.15Null Subtype 3 — Overwhelmed: Receptor Outcompeted by Endogenous Antagonist

Evidence: KYNA as endogenous NMDA antagonist binding glycine co-agonist site is well-established neuroscience (Schwarcz et al. 2012). Kynurenine pathway dysregulated in ME/CFS (Behrouz Kavyani et al. 2024) but KYNA direction inconsistent — some studies show elevation, others depletion. KYNA binding site (glycine co-agonist, not glutamate site) means concentration-competition probe must control for glycine levels (Hazrati et al. 2024).
Citations: (Schwarcz et al. 2012) (Behrouz Kavyani et al. 2024) (Hazrati et al. 2024)
Mechanism: Receptor present, structurally intact, capable of signalling — but outcompeted by supraphysiological endogenous antagonist at orthosteric or co-agonist site. Probe: escalate drug dose above therapeutic range; if drug works at high dose → overwhelmed (higher concentration displaced endogenous antagonist). If fails at all doses → absent or blocked.
Chapter ref: ch33: null subtyping — absent vs blocked vs overwhelmed
Prediction: In ME/CFS patients with confirmed elevated plasma KYNA (>2 SD above healthy controls), escalating ketamine dose over 3 sessions (0.1 → 0.5 → 1.0 mg/kg) produces dose-dependent symptom improvement linear with dose — while NMDA-agonist-naive patients show flat dose-response. Falsified if high-dose ketamine produces no improvement or produces psychosis/dissociation before therapeutic effect — suggesting KYNA elevation is compensatory (neuroprotective) rather than causal for NMDA hypofunction.
Treatment: Kynurenine pathway inhibition (IDO/TDO inhibitor) to lower KYNA, allowing standard-dose NMDA drugs to work. Alternatively, glycine-site agonist (D-serine) to competitively displace KYNA without NMDA overstimulation risk. Dose escalation of NMDA-targeting drug with safety monitoring.
Limitation: KYNA direction inconsistent in ME/CFS — applies only to elevated-KYNA subset (minority). Probe requires measurement of both KYNA and glycine levels. Concentration-competition probe (escalating doses of psychoactive drugs) carries clinical risk (dissociation, psychosis). No published study has tested this probe. Framework may apply to a negligible fraction of ME/CFS patients.

Open Question p=0.20Null Ladder Extension — Tripartite Null Taxonomy with Distinguishing Probes

Evidence: Synthesises receptor occupancy theory (Finlay, Duffull, and Glass 2020), GPCR AAb pharmacology (Skiba and Kruse 2021), GPCR AAb in POTS (Sunami, Sugaya, and Takahashi 2025), β-arrestin-biased AAb signalling (Dodge-Kafka et al. 2026), and kynurenine pathway dysregulation in ME/CFS (Behrouz Kavyani et al. 2024) into a unified framework for distinguishing absent/blocked/overwhelmed null responses. Zero empirical validation in ME/CFS — all probes are proposed and untested.
Citations: (Finlay, Duffull, and Glass 2020) (Skiba and Kruse 2021) (Sunami, Sugaya, and Takahashi 2025) (Dodge-Kafka et al. 2026) (Behrouz Kavyani et al. 2024) (Hazrati et al. 2024)
Mechanism: Binary null logic (null → mechanism eliminated) is incomplete. Three distinct null types exist with mutually exclusive diagnostic implications and distinguishing probes. Absent: receptor never present (bypass agonist probe). Blocked: receptor present but orthosteric site occupied by AAb/ligand/PTM (allosteric probe). Overwhelmed: receptor present but outcompeted by endogenous antagonist (concentration-competition probe). Each null type changes treatment strategy differently — blocked nulls should be unblocked, not abandoned; overwhelmed nulls should be dose-adjusted, not abandoned.
Chapter ref: ch33: null subtyping — absent vs blocked vs overwhelmed
Prediction: In a cohort of 100 ME/CFS patients with documented null responses to ≥3 drugs, systematic null-subtyping using the three probes identifies ≥5% of nulls as BLOCKED or OVERWHELMED (not ABSENT) — confirming that binary null logic produces false eliminations in a measurable minority. Falsified if all nulls resolve to ABSENT on probing, meaning the tripartite framework adds no clinical value over binary null logic.
Treatment: Framework-level — no direct treatment recommendation. Informs interpretation of existing null responses and design of future pharmacodiagnostic protocols.
Limitation: All probes untested in ME/CFS. BLOCKED subtype contingent on validated pathogenic GPCR AAb detection (contested). OVERWHELMED subtype contingent on KYNA elevation (minority of patients). Framework may add conceptual precision with zero practical impact if blocked/overwhelmed null prevalence is negligible.

Speculation p=0.25Menstrual Cycle as Endogenous Pharmacodiagnostic Probe — Drug Response Tracking Across Cycle Phases

Evidence: Oestrogen rapidly potentiates NMDA signalling via ER transactivation of MAPK/ERK and PI3K/Akt (review, largely animal data (Sellers, Raval, and Srivastava 2015), cert 0.36 discounted). Progesterone-derived allopregnanolone enhances GABA-A tonic current, with receptor subunit composition shifting drug sensitivity (review, preclinical (Locci and Pinna 2017), cert 0.36 discounted). Oestrogen modulates serotonin synthesis, release, and receptor sensitivity (review (Shadani et al. 2024), cert 0.44 discounted). Menstrual cycle hormones produce measurable whole-brain structural changes in humans (n=30 (Rizor et al. 2024), cert 0.53 discounted). Thomas et al. 2026 explicitly call for menstrual-cycle-aware sampling in ME/CFS research (review (Thomas et al. 2026), cert 0.60 discounted). PET null findings constrain but do not falsify: 5-HT1A binding unchanged across cycle (n=13 (Jovanovic et al. 2009)), D2 availability unchanged (n=16 (Petersen et al. 2021)). CYP450 expression stable across rat oestrous cycle (S.-Y. Lee et al. 2012) suggests PD over PK interpretation. PK data in humans mixed (review (Bosch, Sommer, and Touw 2025)). Zero human studies testing cycle-phase-dependent drug response as receptor-system probe. Origin: brainstorm — menstrual cycle dopaminergic mast cell probe, extended to pharmacodiagnostic drug-response tracking.
Citations: (Sellers, Raval, and Srivastava 2015) (Locci and Pinna 2017) (Shadani et al. 2024) (Rizor et al. 2024) (Thomas et al. 2026) (Jovanovic et al. 2009) (Petersen et al. 2021) (S.-Y. Lee et al. 2012) (Bosch, Sommer, and Touw 2025)
Mechanism: Menstrual hormone fluctuations (oestrogen at follicular/ovulatory peak → potentiates NMDA, D2, 5-HT1A signalling; progesterone/allopregnanolone at luteal peak → potentiates GABA-A) provide a predictable, endogenous oscillation of receptor function. Tracking drug response across two full cycles maps the drug’s mechanism to hormone-gated receptor families: follicular benefit → oestrogen-coupled (NMDA/D2/serotonergic); luteal benefit → progesterone/GABA-coupled; flat response → hormone-independent. The probe relies on functional sensitivity differences (downstream signalling cascade potency), not receptor density changes (PET-null for D2/5-HT1A binding). PK confound (oestrogen modulation of CYP) must be controlled via serum drug level measurement.
Chapter ref: ch34 sec-10 subsec-07: endogenous probes — menstrual cycle pharmacodiagnostic
Prediction: In a prospective N-of-1 trial, premenopausal ME/CFS patients track a drug with known receptor pharmacology across 3+ cycles. Cycle-phase response variation >30% with follicular-phase benefit → D2 is oestrogen-coupled. Falsified if (a) drug serum levels vary by cycle phase (PK confound), (b) same variation with placebo, or (c) D2/5-HT1A functional output (cAMP response to agonist in PBMCs) shows zero cycle-phase variation in humans.
Treatment: None — observational probe, not a treatment recommendation. Formalises cycle-phase drug-response tracking as a zero-cost diagnostic method. Applies only to menstruating patients with regular cycles (excludes postmenopausal, continuous hormonal contraception, prepubertal, male, amenorrhoeic).
Limitation: No human study has directly tested the probe concept in any disease. The >30% threshold is arbitrary. Two-timepoint sampling (follicular vs luteal) may miss periovulatory oestrogen peak. Cycle-phase symptom patterns confounded by non-hormonal factors (menstrual pain, sleep disruption). Requires minimum 8 weeks for reliable inference. All mechanistic evidence for oestrogen→receptor modulation from general-population and animal studies; no ME/CFS-specific validation. Risk of over-interpretation, though risk of harm is negligible (observational only). Origin: brainstorm.

Speculation p=0.20Menstrual Cycle Phase at Drug Initiation as Confound in N-of-1 Trial Design

Evidence: Logical corollary of Menstrual Cycle as Endogenous Pharmacodiagnostic Probe. If cycle-phase hormone levels modulate receptor sensitivity, a D2 agonist started during high-oestrogen follicular phase will produce a different initial response than the same drug started during low-oestrogen luteal phase — creating a systematic confound in N-of-1 trial interpretation that reads as tachyphylaxis (if started at follicular peak followed by luteal decline) or inefficacy (if started at luteal nadir). Zero human data. Origin: brainstorm — Phase 5 extension.
Citations: (Sellers, Raval, and Srivastava 2015) (Petersen et al. 2021) (Rizor et al. 2024)
Mechanism: Cycle phase at drug initiation → initial receptor state (oestrogen-potentiated vs baseline) → initial drug response magnitude. Follicular-start trials produce higher initial efficacy for oestrogen-coupled drugs → subsequent luteal-phase decline misinterpreted as tachyphylaxis or tolerance. Luteal-start trials produce lower initial efficacy → drug abandoned as ineffective. Standardising initiation to day 1 of menses (hormone nadir) OR recording cycle phase as covariate eliminates the confound.
Chapter ref: ch34 sec-10 subsec-07: endogenous probes — menstrual cycle pharmacodiagnostic
Prediction: In pooled N-of-1 trial data from premenopausal ME/CFS patients, cycle phase at drug initiation predicts response magnitude for dopaminergic drugs (follicular-start higher initial response than luteal-start). Null result would suggest cycle-phase-independent initial response, removing the confound.
Treatment: Methodological recommendation: standardise drug trial initiation to a specific cycle phase (day 1 of menses) or record phase as covariate and test for phase × response interaction. No treatment recommendation.
Limitation: No prospective data. Requires coordinating drug trial initiation with menstrual phase — adds logistical burden. If confirmed null, this recommendation is unnecessary. If confirmed positive, applies only to oestrogen-coupled drugs (D2 agonists, SSRIs, NMDA modulators), not all medications. Origin: brainstorm.

Open Question p=n/aCan Cycle-Phase Drug Response Patterns Stratify ME/CFS Patients by Mechanism?

Evidence: Entirely inferential — builds on Menstrual Cycle as Endogenous Pharmacodiagnostic Probe. If cycle-phase-dependent drug response identifies patient subgroups with oestrogen- or progesterone-coupled pathophysiology, these subgroups may differ in prognosis, treatment trajectory, or biomarker profile. Zero data.
Mechanism: Cycle-phase response pattern → patient-level mechanism identification → treatment stratification: oestrogen-dependent responders may benefit from hormonal modulation or continuous-cycle contraceptives; hormone-independent responders would waste time on hormonal strategies. The concept is that cycle-phase variation is not just diagnostic for the drug’s mechanism but for the patient’s disease pathophysiology.
Chapter ref: ch34 sec-10 subsec-07: endogenous probes — menstrual cycle pharmacodiagnostic
Prediction: If cycle-phase drug response is prospectively tracked across a cohort, patients with >30% follicular response variation will differ from those with less than 15% variation in: (a) response to hormonal modulation, (b) D2 agonist efficacy, (c) oestrogen receptor gene expression in PBMCs. Falsified if no patient-level differences emerge beyond the drug-level mechanism inference.
Treatment: Research-stage only — not a basis for current clinical decisions.
Limitation: Requires validation of the base probe concept first. Patient stratification by cycle-phase response is a second-order inference. Entirely untested. Origin: brainstorm.

FH p=0.65OXPHOS–V̇O₂ₘₐₓ Uncoupling Reflects Disease-Intrinsic Mitochondrial Dysfunction, Not Deconditioning

Evidence: Charlton et al. (2026, Nature Communications) (Charlton et al. 2026): n=105 total (26 ME/CFS, 25 Long COVID, 30 healthy controls, 24 pre-/post-bed-rest). OXPHOS capacity (high-resolution respirometry) correlated with V̇O₂ₘₐₓ (maximal CPET) in healthy controls (r>0) and pre-/post-bed-rest, but not in either patient group. Intrinsic mitochondrial function (OXPHOS/SDH, E/L coupling) impaired in patients but not after bed rest. Corroborated by Joseph 2021 (invasive CPET: impaired O₂ extraction in ME/CFS (Joseph et al. 2021)), Germain 2025 (plasma proteomics: disrupted proteomic-VO₂max associations in ME/CFS (Germain et al. 2025)), and Eggelbusch 2024 (bed rest: preserved OXPHOS–performance coupling despite atrophy (Eggelbusch et al. 2024)).
Citations: (Charlton et al. 2026) (Joseph et al. 2021) (Germain et al. 2025) (Eggelbusch et al. 2024)
Mechanism: Mitochondrial respiratory capacity normally scales with whole-body aerobic performance. In ME/CFS, the coupling breaks: patients have low V̇O₂ₘₐₓ regardless of how much mitochondrial machinery they retain, and intrinsic mitochondrial function is impaired (lower OXPHOS/SDH, lower E/L coupling). This is not a deconditioning effect — bed rest reduces both OXPHOS and V̇O₂ₘₐₓ but preserves their correlation. The limiting factor is either downstream of the mitochondrion (O₂ delivery failure, microvascular obstruction) or upstream (CNS-mediated motor output restriction).
Chapter ref: ch20: universal mechanisms — Family 17 structural integrity / (fhyp-oxphos-vo2max-uncoupling?)
Prediction: Interventions that increase mitochondrial content (pyruvate dehydrogenase activation, NAD⁺ precursors) should restore the OXPHOS–V̇O₂ₘₐₓ correlation if the uncoupling is a modifiable mitochondrial failure. If V̇O₂ₘₐₓ remains uncoupled despite raised OXPHOS capacity, the limiting factor is upstream (CNS motor output) or delivery-side (microvascular obstruction). Interventions improving O₂ delivery (volume expansion, pyridostigmine) that restore the correlation would localise the bottleneck to the microvasculature.
Treatment: Research-stage only. Implications for clinical approach: “reconditioning” is not a rational therapeutic strategy — the molecular machinery needed to adapt to exercise is itself impaired. Mitochondrial support strategies (CoQ10, NAD⁺ precursors) and microvascular therapies are rational targets.
Limitation: Single cohort, single lab (Wüst/VU Amsterdam). No independent replication of the OXPHOS–V̇O₂ₘₐₓ dissociation specifically. Mild–moderate patients only (daily steps 733–8609). Organelle-level finding; no patient-reported outcome data. Correlation design (change in V̇O₂ₘₐₓ after raising OXPHOS has not been tested prospectively).

A p=0.85Sixty-Day Bed Rest Fails to Reproduce ME/CFS Muscle Phenotype — Deconditioning Not the Explanation

Evidence: Charlton et al. (2026, Nature Communications) (Charlton et al. 2026) compared 60-day strict head-down tilt bed rest (n=24 healthy volunteers) against 26 ME/CFS patients, 25 Long COVID patients, and 30 healthy controls across multiple independent measurements: fibre typing (IHC), fibre-type-specific atrophy, mitochondrial respirometry, capillary morphology, whole-body CPET, ventilatory patterns. Five phenotypic dimensions diverged between bed rest and patients, each excluding deconditioning as the sole explanation.
Citations: (Charlton et al. 2026)
Mechanism: Bed rest produces: (1) generalised atrophy of all fibre types without shift; patients show Type I-selective atrophy and glycolytic shift. (2) Preserved OXPHOS–V̇O₂ₘₐₓ coupling; patients lose it. (3) Increased capillary density (atrophy outpaces capillary loss); ME/CFS shows lower capillary-to-fibre ratios. (4) Altered ventilatory pattern (increased V̇E/V̇CO₂ slope); patients’ ventilation is largely normal. (5) No intrinsic mitochondrial function impairment; patients show reduced OXPHOS/SDH and E/L coupling. The qualitative divergence at five independent levels makes deconditioning an insufficient explanation.
Chapter ref: ch20: Sixty-Day Bed Rest Fails to Reproduce the ME/CFS Muscle Phenotype — Deconditioning Is Not the Explanation; ch07: PEM connection (subsec-16); ch11: cardiovascular summary
Prediction: Future studies that include activity-matched sedentary controls (not bed rest but matched daily step counts to ME/CFS patients) should reproduce the qualitative divergence found by Charlton et al. — the patient phenotype is not reproducible by activity reduction alone. Falsified if an activity-matched sedentary control group (not bed rest) develops the same glycolytic shift, Type I atrophy, or OXPHOS–V̇O₂ₘₐₓ uncoupling as patients.
Treatment: Structural evidence that deconditioning is not the primary driver of exercise intolerance. Supports the concept that PEM is a disease-intrinsic phenomenon requiring targeted treatment of its underlying mechanisms (mitochondrial support, microvascular therapy, CNS energy budget), not “reconditioning.” Does not provide specific treatment evidence.
Limitation: Cross-sectional in patients (no longitudinal patient cohort). Bed rest participants younger than patient-matched controls. Head-down tilt is an imperfect model of ambulant deconditioning. All ambulant patients (mild–moderate). Single-lab findings awaiting independent replication. Correlation study — causation inferred from laboratory-controlled bed rest comparator, not directly tested in patients prospectively.

M p=0.25Contraindication Ladder: 4-Level Mechanism-Based Drug Interaction Classification

Evidence: Pharmacological classification derived from receptor pharmacology (Level 1), CYP metabolism (Level 2 — fluvoxamine+duloxetine: 460% AUC increase (Knadler et al. 2011)), autonomic physiology + 1 case report (Level 3 — (Dayal et al. 2025)), and metabolic-stress modelling + 1 ME/CFS stimulant RCT (Level 4 — (Blockmans et al. 2006), (Eckey et al. 2025)). Level 1 pairs are textbook pharmacology. Level 2 best-documented pair (fluvoxamine+duloxetine) has dedicated manufacturer PK study; cimetidine+aripiprazole is predicted from CYP pharmacology with zero PubMed interaction studies. Level 4 stimulant-PEM risk has direct ME/CFS evidence; rapamycin+metformin is mechanistic inference only. No existing DDI framework organizes interactions by mechanism type — the ladder is a novel organizational contribution.
Citations: (Knadler et al. 2011) (Blockmans et al. 2006) (Eckey et al. 2025) (Dayal et al. 2025) (Due Bruun et al. 2024) (Palma and Kaufmann 2020a) (Everly, Heaton, and Cluxton 2004) (Lipworth and Grove 1997) (Ponticelli, Moroni, and Reggiani 2023)
Mechanism: Four ladder levels: (L1) Direct receptor antagonism — competitive occupation of same receptor for opposing purposes → pharmacologically certain, absolute contraindication. (L2) Pharmacokinetic catastrophe — Drug A inhibits CYP enzyme that Drug B needs for clearance → Drug B accumulates to toxic levels. (L3) Pharmacodynamic opposition — opposing physiological goals at systems level, not same receptor → temporally mitigatable. (L4) Metabolic PEM risk — drug increases metabolic demand, masks fatigue signal, or induces autophagy in an energy-compromised system → PEM amplification. Contraindication table: patient-experience-based permanent class-wide contraindications (e.g., LDN dysphoria → avoid all opioid-modulating drugs). (Origin: brainstorm)
Chapter ref: ch34: Diurnal Response Window as Circadian Pharmacodiagnostic Probe
Prediction: Falsifiable per-level: (L1) If a patient known to be on an opioid agonist tolerates LDN without blunted opioid effect or precipitated withdrawal → LDN did not achieve μOR occupancy at dose — the antagonism is dose-dependent, not absolute. (L2) If duloxetine at standard dose is co-administered with fluvoxamine and duloxetine AUC remains within normal range → patient is a CYP1A2 ultrarapid metaboliser (genotype-dependent bypass). (L3) If clonidine 0.1 mg + midodrine 5 mg co-administered simultaneously produces stable BP within 10% of baseline → the sympathetic-α1 axis has sufficient reserve to absorb dual modulation. (L4) If stimulant-treated ME/CFS patients show no increase in PEM frequency over 3 months vs placebo → activity masking is not the dominant PEM mechanism in that subpopulation.
Treatment: Structural safety framework — defines what NOT to combine. No new treatment proposed. Clinical utility: each contraindication is also a diagnostic signal — the adverse response reveals which system is load-bearing for that patient’s homeostasis. The table translates adverse events into mechanism-specific diagnostic inferences.
Limitation: L2 cimetidine+aripiprazole predicted-only (0 PubMed hits). L4 rapamycin+metformin predicted-only (0 adverse-interaction papers). L3 supported by 1 case report in a non-ME/CFS population. L1 pairs lack dedicated interaction studies (pharmacology-level evidence only). Contraindication table entries lack formal rechallenge data. No prospective validation of the ladder structure. Origin: brainstorm — pharmacological inference from receptor physiology + CYP metabolism + autonomic physiology + metabolic constraint modelling.

Speculation p=0.20Diurnal Response Window as Circadian Pharmacodiagnostic Probe

Evidence: Circadian receptor expression established in chronopharmacology: GR peak 04:00–08:00, TLR4 peak 18:00–00:00, H1 peak 02:00–06:00, D2/D3 striatal peak 12:00–16:00 (Levi 2024 review (Lévi et al. 2024)). ME/CFS: blunted cortisol awakening response, exaggerated HPA feedback (meta-analysis, n=1,388, 46 datasets (Woo et al. 2026) — discounted cert 0.88). Chronotherapy proof-of-concept: evening-timed prednisone improves fatigue in RA (CAPRA-2 RCT, n=350, FACIT-F +3.8 vs +1.6, p=0.0032 (Alten et al. 2015)). Diurnal cytokine-symptom coupling: PMR IL-6/IL-8/TNF-α peak 04:00–08:00, prednisolone chronotherapy abolishes symptoms (Galbo and Kall 2016). Post-infectious fatigue: clock gene dysregulation (CLOCK, BMAL1, PER2), epigenetic clock changes (Livieratos, Lockley, and Tsiodras 2025). CYP450/transporter circadian regulation (Okyar et al. 2024). Trial methodology guidelines (Hermida et al. 2021). (Origin: brainstorm)
Citations: (Lévi et al. 2024) (Woo et al. 2026) (Alten et al. 2015) (Galbo and Kall 2016) (Livieratos, Lockley, and Tsiodras 2025) (Okyar et al. 2024) (Hermida et al. 2021) (Cutolo 2019) (Zaki et al. 2019) (Ohdo 2021) (Guarnotta, Amodei, and Giordano 2021)
Mechanism: Drug at 08:00 vs 20:00 produces >30% benefit difference if target follows circadian rhythm; flat → constitutively expressed or downstream. Three patterns: (1) morning-only → GR/cortisol-coupled; (2) evening-only → melatonin/sleep-dependent (TLR4 peak 18:00–00:00); (3) flat → non-circadian. PK vs PD disambiguation via plasma drug levels at both time points. ME/CFS blunted HPA may amplify diurnal signal.
Chapter ref: ch34: Diurnal Response Window as Circadian Pharmacodiagnostic Probe
Prediction: Crossover trial (08:00 vs 20:00, 2 weeks each, n≥1, ≤7 days washout): drug with circadian target shows >30% difference. If all drugs lt.eq 30% in patient with normal circadian phase → probe assumption falsified. If non-circadian drug shows >30% difference → PK confound unless plasma levels controlled. Falsified if pilot (n≥10, 2 drugs with known circadian vs non-circadian targets) finds no drug with >30% diurnal difference in any patient with confirmed circadian phase.
Treatment: No treatment recommendation — pure pharmacodiagnostic methodology. If validated, zero-cost clinical test: 08:00 vs 20:00 response difference reveals receptor circadian coupling, guiding dosing timing without lab work.
Limitation: No ME/CFS chronopharmacology trial exists — concept untested in this population. Assumes circadian phase aligned with clock time (false for DSPD/ASPD). Diurnal symptom variation confounds drug effect. Requires actigraphy + plasma drug levels for PK/PD disambiguation. All evidence analogical (general chronopharmacology + autoimmune chronotherapy). Origin: brainstorm.

Speculation p=0.25Response Onset Latency as Mechanism Kinetics Classifier

Evidence: Onset latency well-documented for drug classes but never systematised for ME/CFS mechanism inference. SSRI onset 2–4 weeks (postsynaptic adaptation), stimulant hours (DAT inhibition), IVIG 48–72h (immune complex) vs 2–4 weeks (immunomodulation). Existing ch34 content covers LDN, aripiprazole, IVIG, duloxetine, gabapentin, DORA latency inferences. No trial validates latency-to-mechanism mapping. (Origin: brainstorm)
Citations: Existing ch34 references; no dedicated ME/CFS latency studies
Mechanism: Before 48h → ion channel gating, receptor agonism/antagonism; 2–7 days → enzyme induction/acute phase protein; 1–4 weeks → receptor upregulation, microglial M1→M2 shift; 4–12 weeks → epigenetic remodelling, mitochondrial biogenesis. LDN at 48h: TRPM3/TLR4 direct (too fast for endorphin). LDN at 3–6 weeks: endorphin or microglial shift.
Chapter ref: ch34: subsec-04 subsubsec-03 response onset latency
Prediction: Prospective LDN latency tracking: ~30% respond by day 2 (TRPM3/TLR4), ~40% at weeks 3–6 (endorphin/microglial), 30% non-responders. Falsified if unimodal distribution.
Treatment: If validated, onset latency classifies fast vs slow responders, avoiding futile 8-week trials. No clinical recommendation without validation. Origin: brainstorm.
Limitation: No prospective latency study. Confounded by placebo (also ~48h onset), concurrent changes, disease fluctuation. Bimodal hypothesis theoretical. Inter-individual PK variability produces pseudo-latency differences.

Speculation p=0.20Sequential Ordering Principle — Five-Rule Drug Trial Priority Order

Evidence: Mechanistic inference framework combining pharmacology (drug half-life, metabolic cost, receptor breadth) with clinical heuristics (cost, accessibility). No prospective study validates the ordering principle for ME/CFS. Existing ch34 content covers individual drug mechanisms and synergy/antagonism pairs; the 5-rule framework synthesizes them into a clinical decision sequence. (Origin: brainstorm sec 13c)
Citations: (Eckey et al. 2025)
Mechanism: Five priority rules: (1) Restorative > Corrective > Threshold-modulatory > Substrate > Symptomatic — trial drugs in descending therapeutic depth. (2) Broad > Narrow — each trial should eliminate the most hypotheses. (3) Energy-neutral > Energy-consuming — conserve PEM budget. (4) Reversible > Irreversible — short-washout drugs allow rapid reinterpretation. (5) Cheap/OTC > Expensive/Prescription — access principle, applied when mechanistic criteria are equal. Integrated sequence: LDN → mast cell stabilisers → LDA → mitochondrial cofactors → valacyclovir → stimulants cautiously → IVIG/rituximab.
Chapter ref: ch34: Diurnal Response Window as Circadian Pharmacodiagnostic Probe
Prediction: Prospective 7-drug sequence trial: cumulative diagnostic resolution (proportion with >30% improvement + mechanism validated by synergy probe) ≥60% by step 5 (LDN through valacyclovir). Falsified if cumulative resolution <40% or ≥30% of resolution occurs after step 5 (stimulants/IVIG — the end-of-sequence drugs).
Treatment: Clinical decision scaffold — provides a defensible “which drug next?” algorithm. Reduces trial-and-error PEM cost. No validated treatment recommendation. Origin: brainstorm.
Limitation: No prospective validation. Ordering conflicts possible — broad but expensive (IVIG) vs narrow but cheap (NAC). Population-specific: ordering assumes patient has the energy budget for 7+ drug trials; severe patients may exhaust PEM budget before step 3. POTS-dominant cluster may need autonomic probes (midodrine, pyridostigmine) earlier.

Speculation p=0.20Do Not Disturb Rule — Four Stopping Conditions for Pharmacodiagnostic Trials

Evidence: Clinical heuristics derived from side-effect diagnostic patterns (Patterns 1–5) and PEM budget constraint. None validated prospectively for ME/CFS pharmacodiagnostic trialling. Existing ch34 sec-10 documents all 6 side-effect patterns; this entry formalizes their clinical stopping implications. (Origin: brainstorm sec 13d)
Citations: Existing ch34 references; no dedicated stopping-rule literature
Mechanism: Four stopping conditions: (C1) Any drug causes severe PEM (>24h self-care-blocking crash or >50% more severe than typical) → stop, do not escalate. PEM obscures diagnostic signal. (C2) Energy-neutral drug causes fatigue → stop, the fatigue IS the diagnostic signal (Pattern 1 — zero reserve in target system). (C3) Three consecutive nulls in same mechanism class → eliminate the class. (C4) >50% improvement from any drug → pause, fully characterise the response before adding a second drug. Each stop IS a diagnostic outcome.
Chapter ref: ch34: do not disturb rule
Prediction: Per-condition falsifiable: (C1) Early-resumers after drug-induced PEM (<1 week) show higher false-positive rates than late-resumers (≥2 weeks). (C2) Dose reduction to 50% eliminates energy-neutral fatigue. (C3) <10% of patients respond to a fourth same-class drug after three nulls. (C4) Fast-sequencers (<1 week after >50% improvement) show lower cumulative resolution than characterisation-first sequencers (≥6 weeks + washout).
Treatment: Safety framework — prevents cumulative PEM from futile pharmacodiagnostic trialling. Reduces risk of false-positive attribution and class-wide adverse event discovery. Clinical utility: conserves PEM budget and redirects toward validated mechanisms. Origin: brainstorm.
Limitation: All conditions are heuristic — no prospective validation. C3 threshold (3 nulls) is arbitrary. C2 (energy-neutral → fatigue = Pattern 1) not validated. C4 (>50% improvement) may miss additive benefits from combining with a second drug. Severe patients may trigger C1 from drugs well-tolerated in moderate patients — severity-level interaction effect unknown.

Speculation p=0.35Waking Local Sleep as a Convergent Electrophysiological Mechanism for ME/CFS Brain Fog

Evidence: Well-established local-sleep phenomenon: sleep-like slow waves (1–4 Hz delta) in localized cortical patches during wakefulness cause attention lapses, RT variability, and subjective sleepiness (Pinggal et al. 2026) (Andrillon and Oudiette 2023). Catecholamines gate waking slow waves: noradrenaline/dopamine suppress, serotonin promotes (Pinggal et al. 2022). ME/CFS involves: (a) central noradrenergic deficiency (Walitt et al. 2024), (b) chronic neuroinflammation increasing local sleep probability (Leemburg et al. 2025), (c) energy-metabolic limitation reducing ion-pump capacity for maintaining waking membrane potentials (Alfonsa et al. 2023). Zero studies have applied the waking slow-wave EEG paradigm to ME/CFS patients. The entire inferential chain is cross-disease extrapolation from ADHD, post-COVID, healthy-volunteer, and animal models. PDE: 0.35 – speculative, not hypothesis-level. (Translation gap: general-population/adult-ADHD → ME/CFS. Not validated in ME/CFS patients.)
Citations: (Pinggal et al. 2026) (Pinggal et al. 2022) (Andrillon and Oudiette 2023) (Alfonsa et al. 2023) (Leemburg et al. 2025) (Van Dongen 2025) (Fatt et al. 2020) (Neu et al. 2015) (Ortelli et al. 2022) (Walitt et al. 2024)
Mechanism: Upstream ME/CFS pathology (noradrenergic deficiency + neuroinflammation + ATP deficit → impaired cortical maintenance of waking desynchronisation) → individual cortical columns enter sleep-like slow-wave oscillations during wakefulness → functional disconnection of those circuits from ongoing cognitive processing → attention lapses, slowed RT, increased RT variability, subjective brain fog. Convergent endpoint of three disrupted gatekeeping factors: noradrenergic suppression failure, inflammatory priming, and ion-pump energy depletion.
Chapter ref: ch18: Waking Local Sleep as a Convergent Mechanism for Brain Fog; research protocol: Waking Slow-Wave Density as a Direct Electrophysiological Biomarker of Brain Fog in ME/CFS
Prediction: ME/CFS patients performing sustained attention task under EEG show higher waking SW density (SW/min, parieto-temporal electrodes) vs controls, correlating with brain fog severity, omission errors, and RT variability. Density increases 24–48h post-CPET. Atomoxetine/methylphenidate acutely reduces SW density. Falsified if no group difference, no cognitive correlation, or no catecholaminergic modulation.
Treatment: No clinical treatment recommendation – mechanism unverified in ME/CFS. If confirmed: (a) waking slow-wave density becomes objective electrophysiological biomarker of brain fog; (b) adrenergic support (atomoxetine, guanfacine) and anti-neuroinflammatory strategies (LDN, PEA) target measureable endpoint; (c) sleep-quality interventions (SWS enhancement, sleep hygiene) indirectly reduce waking slow-wave pressure. Research-stage only.
Limitation: Zero ME/CFS waking slow-wave data. Entire mechanism is cross-disease extrapolation. Behavioral signature (RT slowing, lapses) is generic – present across many neurological conditions, not specific to local sleep. Competing mechanisms (glymphatic failure, orthostatic hypoperfusion, kynurenine neurotoxicity, sleep fragmentation) produce same behavioral endpoint and may be dominant. BDNF findings mixed in ME/CFS. Catecholamine deficiency group-level only; intra-individual catecholamine–SW correlation unmeasured. Severity applicability: unknown. (Origin: /integrate-topic local-sleep-waking-slow-waves.)

Prediction —Waking Slow-Wave Density as Direct Electrophysiological Biomarker of Brain Fog – Proposed EEG Study Protocol

Evidence: Prediction only – test not yet performed. Grounded in established paradigms: Pinggal 2026 waking slow-wave EEG (n=63), Pinggal 2022 pharmacological SW modulation (n=32 RCT crossover), Walitt 2024 noradrenergic deficiency in ME/CFS (n=17), Ortelli 2022 post-COVID attention deficits (n=117), Leemburg 2025 LPS→waking SW in animals. No component requires novel technology.
Citations: (Pinggal et al. 2026) (Pinggal et al. 2022) (Walitt et al. 2024) (Ortelli et al. 2022) (Leemburg et al. 2025)
Mechanism: Prediction of the waking local-sleep model: ME/CFS cognitive dysfunction results from elevated waking slow-wave density, which in turn results from catecholamine deficiency + neuroinflammation + metabolic limitation. The study protocol tests each link: resting-state SW density (baseline), task-related SW density (attention paradigm), post-CPET SW density (PEM timecourse), pharmacological modulation (atomoxetine crossover), and negative controls (absence of correlation with motor slowing or sleep latency).
Chapter ref: ch18: Waking Slow-Wave Density as a Direct Electrophysiological Biomarker of Brain Fog in ME/CFS
Prediction: All predictions specified in the protocol below. Falsified if: (1) no group difference in waking SW density ME/CFS vs controls; (2) SW density does not correlate with cognitive performance; (3) atomoxetine/methylphenidate does not reduce SW density; or (4) post-CPET SW density does not track subjective brain fog timecourse.
Treatment: If positive, identifies atomoxetine and methylphenidate as first-line pharmacological probes targeting the waking local-sleep mechanism with a quantifiable electrophysiological endpoint. Research-stage only until confirmed.
Limitation: No ME/CFS waking slow-wave EEG data exist – the entire protocol is a prediction. EEG hardware and analysis pipeline are standard but require operator training for waking slow-wave detection. CSF DHPG measurement requires lumbar puncture – only feasible in research settings, not routine clinical monitoring. Cross-sectional first; longitudinal PEM timecourse adds scheduling complexity. Atomoxetine and methylphenidate carry sympathomimetic side effects that may be poorly tolerated in ME/CFS patients with POTS. (Origin: /integrate-topic local-sleep-waking-slow-waves.)

Speculation p=0.40The Inflammatory-Mediated Neurodegeneration Template: Bidirectional Mental-Illness–Inflammation Link

Evidence: Meta-analyses testing the inflammatory-mediated-neurodegeneration hypothesis found (a) Alzheimer’s disease associated with elevated IL-17A, IL-1$\alpha$, IL-10, G-CSF, GM-CSF, IL-3 independent of comorbid psychiatric/physical inflammatory conditions (Kuring et al. 2026); (b) depression, anxiety, PTSD each associated with inflammation independent of such comorbidities (Kuring et al. 2023). IL-17A elevated in both AD and depression; G-CSF in AD, depression, PTSD. Excluding comorbid inflammatory conditions removed classically-reported IL-6/TNF-$\alpha$ elevations in AD (Kuring et al. 2026). Cross-disease (dementia/mental-illness → ME/CFS inference); specific AD markers do not match ME/CFS profile (TNF-$\alpha$/IL-4/TGF-$\beta$/CRP per (Strawbridge et al. 2019)), so only the general template, not the specific markers, transfers.
Citations: (Kuring et al. 2026) (Kuring et al. 2023) (Strawbridge et al. 2019)
Mechanism: Pre-existing mental-illness-associated inflammation → IL-17A/IL-1$\alpha$/G-CSF signaling → CNS neuroinflammation and neuroinflammatory-mediated CNS pathology. In ME/CFS this runs REVERSE to the “neuroinflammation as common upstream driver” model (depression as secondary); the two imply a bidirectional inflammation–mental-state loop with causality direction unresolved. Comorbidity masking: excluding comorbid inflammatory conditions changes which cytokines appear elevated (a methodological caution applicable to ME/CFS inflammatory-marker research).
Chapter ref: ch14d-cross-disease: bidirectional mood inflammation template; ch08 pro-inflammatory cytokines: Comorbidity Masking of Cytokine Signals — A Cross-Disease Caution; ch19 causal hierarchy: chronicity criterion cross-disease precedent
Prediction: Within ME/CFS, a depression/anxiety-comorbid subgroup (excluding other inflammatory conditions) will show a distinct inflammatory-marker profile (higher IL-17A, G-CSF) correlating with depressive-symptom severity, with mental-symptom onset preceding the marker rise in longitudinal sampling. Falsified if mental symptoms are uniformly secondary (marker rise follows mental-symptom onset in all patients) or if the sub-group marker profile is not distinct.
Treatment: No clinical treatment recommendation. If confirmed, the bidirectional link would imply anticytokine/anti-neuroinflammatory strategies and mental-health treatment may each partially address the other — but this is speculative and not currently actionable.
Limitation: Cross-sectional included studies cannot establish causality vs prodrome vs epiphenomenon. Direction of mental-illness→inflammation arrow unresolved. Specific dementia markers not transferable to ME/CFS. Severity applicability unknown (dementia populations not ME/CFS-severity-stratified). No comorbidity-screened ME/CFS replication yet. (Origin: /integrate-topic dementia-inflammation.)

Speculation p=0.30Shared Th17/Myeloid-Axis Signal Between ME/CFS and Inflammatory CNS Conditions

Evidence: G-CSF and GM-CSF are severity-correlated inflammatory markers in ME/CFS (Montoya PNAS 2017 cohort (Montoya et al. 2017)) and are elevated in Alzheimer’s disease independent of comorbidity (Kuring et al. 2026). IL-17F (Th17) is part of early-disease ME/CFS signatures (Hornig 2015); IL-17A is elevated in both Alzheimer’s and depression (Kuring et al. 2023). This specific Th17/myeloid-hematopoiesis axis (G-CSF/GM-CSF/IL-17 family) converges across conditions, in contrast to the overall divergent ME/CFS (TNF-$\alpha$/IL-4/TGF-$\beta$/CRP (Strawbridge et al. 2019)) vs AD (IL-17A/IL-1$\alpha$/IL-10/G-CSF/IL-3) profiles.
Citations: (Kuring et al. 2026) (Kuring et al. 2023) (Strawbridge et al. 2019) (Montoya et al. 2017)
Mechanism: Chronic inflammatory-CNS conditions may show a shared, co-elevated set of G-CSF/GM-CSF/IL-17 markers (grouped by observed correlation, not a proven shared regulatory node — G-CSF/GM-CSF are myeloid growth factors, IL-17A/F are T-cell-derived) even when their broader inflammatory profiles diverge. In ME/CFS this marker set may track fatigue/cognitive severity.
Chapter ref: ch14d-cross-disease: th17 myeloid shared signal
Prediction: A comorbidity-screened ME/CFS cohort will show elevated G-CSF and GM-CSF with a Th17-correlated profile (raised IL-17F/IL-17A) tracking fatigue/cognitive severity. Falsified if comorbidity-screened ME/CFS cohorts show neither elevated G-CSF/GM-CSF nor a Th17-correlated signal.
Treatment: No clinical recommendation. If confirmed, identifies a specific measurable immune axis for biomarker and stratification use; anti-Th17/anti-IL-17 biologics would be research-stage probes only with no ME/CFS data.
Limitation: Cross-study marker panels differ; none run on comorbidity-screened ME/CFS cohorts. Inference from cross-disease panels, not direct ME/CFS replication. Severity applicability unknown. (Origin: /integrate-topic dementia-inflammation — brainstorm.)

Hypothesis p=0.30Dysbiotic Microbiome-Immune-Metabolome Crosstalk with a Benzoate-to-Hippurate Axis in ME/CFS

Evidence: BioMapAI (n=249; 96 controls, 153 ME/CFS; 4-year longitudinal; 515 timepoints) integrated gut metagenomics, plasma metabolomics, immune profiling, blood labs, and 12 symptoms; classification AUC=0.99 with held-out + four external-cohort validation (Xiong et al. 2025). Connectivity map: decreased butyrate/BCAA pathways, increased tryptophan/benzoate pathways, heightened MAIT/gamma-delta T-cell IFN-gamma + granzyme A, increase in benzoate-to-hippurate linked to sleep/emotional/fatigue symptoms. Pillars individually supported: butyrate deficiency (Xiong et al. 2023); tryptophan/kynurenine diversion (Z. Kavyani et al. 2022); T-cell remodeling (Shahbaz2026single?)-cell-immune (J.-S. Lee et al. 2025). The benzoate-to-hippurate axis itself has only indirect, neighbouring-population support (hippuric acid elevated in post-infectious syndromes (Brigo et al. 2025) and fibromyalgia (Malatji et al. 2017) — both tiny pilots, not ME/CFS replication). Symptom attributions and direction are model output, not held-out validated; the integrated axis is constrained by review (adversarial) to 0.30.
Citations: (Xiong et al. 2025) (Xiong et al. 2023) (Z. Kavyani et al. 2022) (Shahbaz2026single?)-cell-immune (J.-S. Lee et al. 2025) (Brigo et al. 2025) (Malatji et al. 2017)
Mechanism: Hypothesis (direction of causation unresolved; simpler shared-driver alternative not excluded): gut dysbiosis may alter microbial-metabolite output (decreased SCFA/BCAA, increased tryptophan/benzoate) read by the immune system and reflected in plasma lipids/bile acids, with inflammatory innate-like T-cell activity (MAIT/gamma-delta IFN-gamma, granzyme A) contributing to symptom burden; benzoate-to-hippurate (hepatic glycine conjugation of microbial benzoate) as a possible readout. Reverse causation and dietary benzoate intake are unaddressed confounds.
Chapter ref: ch15: Dysbiotic Microbiome-Immune-Metabolome Crosstalk with a Benzoate-to-Hippurate Axis; BioMapAI: Explainable AI Integration of Five Omics Layers; ch12: Replication Status: Partially Replicated
Prediction: Independent ME/CFS cohort with paired metagenomics + plasma/urine metabolomics: (a) reduced butyrate/BCAA-producing taxa + reduced metabolites; (b) increased benzoate and hippurate; (c) significant positive correlation between urinary/plasma hippurate (or benzoate-pathway score) and fatigue/sleep/emotional severity — after adjusting for dietary benzoate intake and overall dysbiosis burden. If the axis is only a global-dysbiosis (or diet) marker, it should not independently predict symptom severity after those adjustments.
Treatment: No clinical treatment recommendation. If replicated after diet- and dysbiosis-adjustment, benzoate-to-hippurate could become a research readout of the microbial-immune interface — not yet a clinical monitoring metric.
Limitation: Symptom-specific biomarkers computed on the full dataset, not held-out validated. Single-country (US) cohort, n=249. Associative, not causal. Benzoate axis rests on one model plus small neighbouring-population pilots (Brigo n=41, Malatji n=18; not replication). Unaddressed reverse-causation and dietary-benzoate confound. Tension: model shows MAIT/gamma-delta ACTIVATION (per-cell function) while single-cell data show DEPLETION (frequency) in Long-COVID ME/CFS — possibly orthogonal readouts. Severity applicability unknown — cohort not stratified. (Origin: /integrate-topic ai-multiomics-mecfs-xiong2025.)

Hypothesis p=0.35A Cross-Layer Coordination Signature, Not Any Single Marker, Defines ME/CFS

Evidence: BioMapAI PCoA: each omics layer alone leaves patients largely indistinguishable from controls; only integration separates groups with high accuracy (Xiong et al. 2025). Consistent with discrepancy framework (single-resting-measurement domains miss the disease The Subjective-Measurable Discrepancy as a Diagnostic Index). BUT integration can win via a technical concatenation of individually-weak main effects, requiring no cross-layer coordination; the paper does not report an interaction-versus-concatenation test. The strong “coordination rewiring” reading is contingent and unreplicated.
Citations: (Xiong et al. 2025)
Mechanism: Contingent hypothesis: ME/CFS may be a pathological coordination (association) structure across compartments — normal correlated relationships between microbiome, immune system, and metabolome rewired even though each layer’s marginal distribution is largely preserved. Alternative (simpler): a flat feature-concatenation model reproduces the integrated result with no interaction. The model alone cannot discriminate these.
Chapter ref: ch15: A Cross-Layer Coordination Signature, Not Any Single Marker, Defines ME/CFS
Prediction: Independent multi-omics ME/CFS cohort, two baselines required: (a) each single omics layer alone fails to separate; (b) a FLAT feature-concatenation model is meaningfully worse than a model explicitly modeling cross-layer interactions. If concatenation matches the interaction model, the coordination reading collapses to additive main effects and is rejected; if a single layer discriminates as well as integration, the disease is better described by a single-compartment signature.
Treatment: No clinical recommendation. If confirmed after the concatenation control, biomarker strategy shifts from a single blood marker toward algorithms reading cross-layer relationship patterns; until then only “integration works better than any single layer” is defensible.
Limitation: The “coordination structure” is partly defined by the model architecture (may not be a natural-kind property); no interaction/permutation control reported; not severity-stratified; the observed integrated-vs-single-layer advantage is not evidence of coordination per se. Severity applicability unknown. (Origin: /integrate-topic ai-multiomics-mecfs-xiong2025 — brainstorm.)

Speculation p=0.25Longitudinal Symptom Stability Is Symptom-Specific: Stable Fatigue Core, Labile Emotional Component

Evidence: BioMapAI 3-4 year longitudinal data: fatigue persistently severe across years; emotional dysregulation highly variable/unstable (Xiong et al. 2025). Single (US) cohort, model-reconstructed/observed timecourse, not independently replicated. A non-biological alternative — coarse ordinal fatigue scales near ceiling (measurement artifact) and symptom-circularity — is not excluded.
Citations: (Xiong et al. 2025)
Mechanism: Contingent: IF the pattern survives range-normalization and scale-artifact control, different symptoms may be driven by different biological timescales: stable driver (chronic metabolic or neuroimmune deficit) for fatigue; labile driver (fluctuating gut-metabolic state, autonomic reactivity, state-dependent inflammatory tone) for emotional symptoms. Measurement artifact (scale ceiling/coarseness) is the primary alternative.
Chapter ref: ch15: Longitudinal Symptom Stability Is Symptom-Specific: A Stable Fatigue Core with a Labile Emotional Component
Prediction: Prospective longitudinal cohort with continuous, range-normalized fatigue AND emotional scales over 12+ months: test-retest stability (ICC) significantly higher for fatigue than emotional items AFTER controlling for scale range; a stable biological measure correlates significantly more strongly with the time-stable fatigue score than the fluctuating emotional score. Falsified as biological if fatigue is as labile as emotional symptoms after scale control (i.e., the stability was a ceiling artifact).
Treatment: No clinical recommendation. If confirmed biologically, affects trial-design sampling frequency (stable vs fluctuating endpoints); does NOT justify single-visit clinical assessment of an individual patient.
Limitation: Single-cohort, model-derived; severity unknown; measurement-artifact explanation (scale ceiling/coarseness, symptom-circularity) not excluded; not replicated. (Origin: /integrate-topic ai-multiomics-mecfs-xiong2025 — brainstorm.)

Hypothesis p=0.50Distinct Brain Neurochemical Signatures Distinguish ME/CFS from Long COVID (7T MRS)

Evidence: 7T MRS head-to-head (24 ME/CFS, 25 long COVID, 24 HC): ME/CFS brain lactate elevated in both pgACC (p=0.004) and dACC (p=0.006); long COVID showed reduced total choline in dACC (p=0.0002); resting calf-muscle metabolites did not differ between groups (Godlewska et al. 2025). The ME/CFS brain-lactate elevation is consistent with a 3-organic-lab replication cluster (Mount Sinai: (Murrough et al. 2010), (Natelson et al. 2017); UAB: (Mueller et al. 2020)).
Citations: (Godlewska et al. 2025) (Murrough et al. 2010) (Natelson et al. 2017) (Mueller et al. 2020)
Mechanism: ME/CFS and long COVID show separable anterior-cingulate neurochemistry (lactate elevation vs choline reduction) despite overlapping clinical symptoms; consistent with distinct underlying neurobiological mechanisms rather than a single pooled post-infectious fatigue. Brain lactate does not uniquely localise a cause (mitochondrial vs astrocyte-lactate-shuttle vs hypoperfusion vs inflammatory-cell glycolysis).
Chapter ref: ch14d-cross-disease: Distinct Brain Neurochemical Signatures Distinguish ME/CFS from Long COVID (7T MRS); ch09: existing brain-lactate glial-cell environment (Godlewska et al. 2025) baseline
Prediction: A head-to-head 7T MRS replication will reproduce the divergence (ME lactate elevated, long COVID lactate not; vice versa for choline). Long COVID patients who later meet full ME/CFS criteria will shift toward the ME/CFS lactate-elevation signature. Concurrent perfusion imaging will resolve whether ME/CFS lactate tracks hypoperfusion (vascular) or persists with normal perfusion (mitochondrial/anabolic).
Treatment: No clinical recommendation. If confirmed, argues against pooling ME/CFS and long COVID in treatment trials and supports MRS as a stratification/mechanistic-readout tool (research-stage).
Limitation: Single-site 7T cohort, not yet independently replicated at 7T; cross-sectional (no causation); choline direction is region/age/condition-dependent (Pajuelo corpus-callosum increase (Pajuelo et al. 2024); Mueller cingulate increase (Mueller et al. 2020)) so should not be generalised. Severity applicability unknown – cohort not severity-stratified. (Origin: /integrate-topic godlewska-7t-mrs-brain-muscle.)

Speculation p=0.35Long COVID Dorsal-Cingulate Choline Reduction: Coagulation and “Brain Fog” Substrate

Evidence

Reduced total choline (tCho) in dorsal anterior cingulate of long COVID patients (p=0.0002) (Godlewska et al. 2025). Author-linked to the blood-clot/brain-fog association and to animal work showing choline can prevent intravascular coagulation. Purely associative and cross-sectional; no in-cohort coagulation assay and no causal demonstration. Direction heterogeneity: Pajuelo corpus-callosum choline increase in older patients (Pajuelo et al. 2024); Mueller cingulate choline increase in ME/CFS (Mueller et al. 2020).

Citations

(Godlewska et al. 2025) (Pajuelo et al. 2024) (Mueller et al. 2020)

Mechanism

Brain-region-specific choline deficit in long COVID may reflect altered membrane phospholipid metabolism; speculative link to a pro-coagulant local milieu relevant to cognitive symptoms via choline’s role in the one-carbon/anticoagulation axis.

Chapter ref

ch14d-cross-disease: Long COVID Dorsal-Cingulate Choline Reduction: Coagulation and “Brain Fog” Substrate

Prediction

Long COVID patients with low dACC choline will show elevated coagulation/fibrinolysis markers (D-dimer, fibrinogen, microclot assay) relative to those with normal choline. (2) If choline’s anticoagulant role were causal, dietary choline supplementation would raise dACC choline and lower coagulation markers. Falsified if dACC choline is unrelated to coagulation markers and cognitive severity.

Treatment

No clinical recommendation; no evidence choline supplementation changes long COVID brain chemistry or symptoms.

Limitation

Associative, cross-sectional, single cohort; no coagulation co-measure; choline direction heterogeneous across regions/ages/conditions. Severity applicability unknown. (Origin: /integrate-topic godlewska-7t-mrs-brain-muscle.)

Hypothesis p=0.40Neuroinflammation-Activated Dedicated Sickness and Torpor Neural Circuits — a Hypothesis for ME/CFS Symptom Generation

Evidence: Komaroff & Dantzer 2025 (Cell Reports Medicine) propose the sickness-behavior and torpor framework (Komaroff and Dantzer 2025): dedicated neural circuits – area postrema to brainstem (suppresses eating/drinking/movement), organum vasculosum to ventromedial preoptic area (appetite suppression, warmth seeking, fever), and median preoptic nucleus to torpor (hypometabolic) – are activated by neuroinflammation and generate stereotyped symptom bundles. The rodent circuits are well-established (Hrvatin et al. 2020), (Machado et al. 2025), but application to chronic human ME/CFS is inferential and rests on a single review by the framework’s originator, with no independent ME/CFS-cohort evidence; multiple rival readings (deconditioning, neural plasticity, the model not extending to inflammation-negative patients) are not currently distinguished.
Citations: (Komaroff and Dantzer 2025) (Hrvatin et al. 2020) (Machado et al. 2025) (Tuomaala et al. 2026)
Mechanism: Neuroinflammation (whose presence in established ME/CFS is uncertain – Omdal peripheral null, regional-not-global TSPO) could activate dedicated sickness/torpor circuits whose output is the ME/CFS symptom set. An important rival: the closest human neuroinflammatory data (limbic) do not correspond to the named area-postrema/preoptic circuits, so the human signal is indirect support at best.
Chapter ref: ch18: Dedicated Sickness and Torpor Neural Circuits as a Hypothesis for ME/CFS Symptom Generation; ch18: Constraints on, and Rival Readings of, the Torpor/Sickness-Circuit Model
Prediction: The discriminating test must resolve a specific circuit (area-postrema→brainstem) tracking symptoms under a modality able to resolve millimeter-scale nuclei (ultra-high-field fMRI or higher-resolution tracers; current TSPO-PET cannot). Absence of any circuit-specific activation change with symptoms would falsify the dedicated-circuit (versus diffuse-inflammation or plasticity-maintained) claim. The neuroinflammation-input claim is separately refuted by consistent absence of neuroinflammatory signal in ME/CFS cohorts.
Treatment: Research-stage. Principal implied target is neuroinflammation (existing anti-neuroinflammatory rationale unchanged); no new test or drug follows from the dedicated-circuit framing. No ME/CFS dosing or efficacy data.
Limitation: Rodent-only circuits; zero direct human ME/CFS evidence; torpor-with-hypothermia phenotype not reproduced in ME/CFS; severity applicability unknown; all proposed tests research-only and unsuitable for severe patients. (Origin: /integrate-topic komaroff-dantzer-symptom-persistence.)

Hypothesis p=0.45Symptom Persistence Arises from Self-Reinforcing Vicious Cycles Across Underlying Abnormalities

Evidence: Komaroff & Dantzer 2025 synthesize bidirectional reinforcement among abnormalities (mitochondrial injury <-> inflammation; redox <-> energy; redox <-> endothelial; endothelial+NK+neuroinflammation; spike/fibrin pro-inflammatory epitope) as vicious cycles that perpetuate pathology and symptoms (Komaroff and Dantzer 2025). Each individual connection is supported by the primary literature the review cites; the unified vicious-cycle model is the review’s integrative claim and is consistent with but not directly proven as a closed loop in patients.
Citations: (Komaroff and Dantzer 2025) (Baraniuk, Eaton-Fitch, and Marshall-Gradisnik 2024)
Mechanism: Chronicity results not from one lesion but from persistent agents/antigens + defective resolution + reactivation + dysbiosis + autoimmunity + a defective circuit switch, amplified by positive-feedback loops among the biological abnormalities – so that correcting any single abnormality may not break the cycle.
Chapter ref: ch16: chronicity/amplifier synthesis
Prediction: If self-reinforcing vicious cycles sustain the illness, then (a) severity should correlate with the NUMBER of co-active abnormalities and bidirectional constraints, not any single marker; (b) therapies that break a single node should show partial but not complete remission; (c) residual unexplained variance should persist when any one abnormality is corrected. Falsified if correcting one specific abnormality reliably induces sustained remission in a substantial fraction of patients.
Treatment: Research-stage frame; argues for combination/multi-target strategies and for monitoring multiple abnormality axes. No specific clinical dosing recommendation.
Limitation: Integrative/synthesis claim (review), not a single testable primary finding; the closed-loop structure is inferred, not measured directly. Severity applicability unknown. (Origin: /integrate-topic komaroff-dantzer-symptom-persistence.)

Speculation p=0.52Peripheral Blood Methylome Distinguishes Long COVID from ME/CFS While Sharing a Common Epigenetic Core

Evidence: Peppercorn et al. 2025 (RRBS of PBMCs, n=5 ME/CFS / n=5 Long COVID / n=5 HC, age/sex-matched, University of Otago) — ME/CFS 214 DMFs vs HC (145 hyper / 69 hypo), Long COVID 429 DMFs (281 hyper / 148 hypo); 118 DMFs shared with Pearson R=0.88; 26 of 118 differ by more than 10% between the two conditions; 6 show opposite-direction methylation (Peppercorn et al. 2025). Single lab, small n, no independent replication (raw 0.52 discounted unchanged for ME/CFS population, weight 1.00).
Citations: (Peppercorn et al. 2025)
Mechanism: PBMC DNA methylation across ME/CFS and Long COVID share a common epigenetic core (Pearson R=0.88 on shared DMFs) yet retain condition-specific sites, of which six show opposite-direction changes; LC shows more abundant methylation changes, consistent with earlier disease stage (LC ~1 yr vs ME/CFS ~12 yr) or SARS-CoV-2-specific effects. PCA separates the three cohorts into distinct clusters.
Chapter ref: ch14d cross-disease: Peripheral Blood Methylome as a Long COVID vs ME/CFS Distinguishing Signature; ch19: Consolidation as Loss of Methylation; ch56: Per-Locus Dynamics: Vector Model for Bidirectional Methylation
Prediction: A stage-matched, larger (n≥50/group) methylation study with LC and ME/CFS at comparable time-from-onset should (i) reproduce the Pearson R=0.88 shared-makeup relationship, (ii) shrink the LC-vs-ME/CFS DMF amplitude gap when stage is matched, and (iii) determine whether the six opposite-direction DMFs replicate as condition-specific or collapse with stage matching. Falsified if stage-matched cohorts show no reproducible condition-specific DMFs.
Treatment: None yet — a diagnostic/differential-stratification hypothesis, not a treatment target. If the six opposite-direction DMFs replicate, they could support a blood-based test distinguishing LC from ME/CFS, but this is research-stage and unvalidated.
Limitation: Small n (5/5/5), single lab, no independent replication, cross-sectional (no directionality), PBMC proxy not tissue; stage mismatch (LC 1 yr vs ME/CFS 12 yr) confounds condition-specific vs time-from-onset attribution; opposite-direction DMFs not validated for diagnosis. Severity applicability unknown. (Origin: /integrate-topic peppercorn-methylation-landscapes.)

Speculation p=0.25The EP3R “Stuck Switch” — a Candidate (Unproven) Nervous-System Persistence Mechanism

Evidence: Preoptic EP3 receptor neurons act as a two-way switch routing between fever (hypermetabolic) and torpor-like (hypometabolic) states in rodents (Machado et al. 2025). A speculation that a persistent (post-washout) failure of that switch could maintain a low-energy state without ongoing neuroinflammation – offered as a partial reading of the Omdal null (Komaroff and Dantzer 2025). Restates persistence in circuit vocabulary; no EP3R latch or desensitization demonstrated in any species or in ME/CFS (zero direct human evidence).
Citations: (Machado et al. 2025) (Komaroff and Dantzer 2025)
Mechanism: Candidate lesion (EP3R desensitization, biased signaling, fever-arm failure) could leave the brain switch in an energy-conserving position; a simpler rival reading is that the sickness-behavior model simply does not extend to inflammation-negative patients, whose symptoms may have a different cause.
Chapter ref: ch18: The EP3R “Stuck Switch” Hypothesis — a Candidate, Not a Demonstrated Mechanism
Prediction: Discriminating test is a reversible latch: transient PGE2 that persists as torpor output after ligand washout (bistability) in rodents; in humans, a maintained low defended temperature set-point with acute disengageability. PBMC EP3R is excluded as a CNS proxy. Falsified if no persistent reversible latch is demonstrable under washout, or no maintained set-point with acute disengageability in ME/CFS.
Treatment: Research/hypothesis-generation only. No switch-resetting intervention exists or is in trials; no clinical recommendation.
Limitation: Rodent-only; zero direct human evidence; torpor-with-hypothermia phenotype not reproduced in ME/CFS; not severity-stratified. (Origin: /integrate-topic komaroff-dantzer-symptom-persistence – brainstorm re-run.)

Speculation p=0.20Area Postrema Autoantibody × GLP-1 Gate — an Untested Cross-Reactivity Hypothesis

Evidence: The area postrema is both a route for circulating GPCR autoantibody access to brainstem nuclei and a dense GLP-1 receptor region (Azcue et al. 2026) (Komaroff and Dantzer 2025). The interference step requires an explicit, unsupported premise that ME/CFS GPCR autoantibodies (literature targets include adrenergic/muscarinic receptors) cross-react with or sterically impede GLP-1 receptors – no such cross-family interaction has been shown. Co-location of two facts does not by itself entail an interaction.
Citations: (Azcue et al. 2026) (Komaroff and Dantzer 2025)
Mechanism: If (unsupported premise) GPCR autoantibodies interfered with GLP-1 receptor signaling at the area postrema, they could blunt GLP-1-dependent anti-inflammatory signaling and help sustain neuroinflammation. A simpler established mechanism (agonistic autoantibodies acting on their own receptors) already explains part of the autonomic symptom profile.
Chapter ref: ch18: Area Postrema as a Possible Gate — an Untested Autoantibody × GLP-1 Hypothesis
Prediction: Decisive test: purified ME/CFS patient IgG vs controls in competitive binding against labeled GLP-1 on area-postrema membranes. If no displacement, the competitive-interference claim is falsified. Clinical predictions (blunted GLP-1-induced nausea; stratified GLP-1-RA response) are confounded by vagal dysfunction/dysautonomia and cannot stand alone.
Treatment: Not a prescribing recommendation. No GLP-1-RA use in ME/CFS is trial-supported; GPCR-autoantibody testing is research-only; GLP-1 RAs risk reduced intake/weight loss and nausea-driven orthostatic worsening in comorbid POTS and severe ME/CFS.
Limitation: Cross-reactivity premise untested and mechanistically unmotivated; GPCR-autoantibody findings contested and single-source; not severity-stratified. (Origin: /integrate-topic komaroff-dantzer-symptom-persistence – brainstorm re-run.)

Hypothesis p=0.65Viral dUTPase drives the TFH/activin-A axis that generates the antibody-secreting plasma cells daratumumab depletes

Evidence: EBV/HHV-6A dUTPases enhance TFH-cell differentiation and extrafollicular activities in ME/CFS, with ME/CFS patients showing elevated activin A and IL-21 correlating with anti-viral-dUTPase antibodies; EBV dUTPase induced marginal-zone B and invariant NKT-FH expansion in mouse spleen (Cox et al. 2022). Raw 0.65 (ME/CFS cohort, peer-reviewed, partially replicated via later dUTPase-antibody findings), discounted unchanged at weight 1.00.
Citations: (Cox et al. 2022)
Mechanism: Persistent herpesvirus reactivation (via dUTPase, an early lytic protein) diverges the germinal-center response toward activin A/IL-21-driven extrafollicular antibody output, plausibly generating CD38+ long-lived plasma cells (LLPCs) that continue secreting pathogenic autoantibodies independent of ongoing B-cell cycling – the cell type rituximab (CD20+) spares but daratumumab (anti-CD38) deletes (Cox et al. 2022).
Chapter ref: ch34 sec-12 Daratumumab (Anti-CD38); ch19: The Plasma Cell Sanctuary; ch33 sec-02h origin-elimination
Prediction: If this mechanism holds, long-lived autoantibody-producing cells in ME/CFS should be CD38+ and CD20−, and their depletion (daratumumab) should produce a sustained drop in the offending autoantibody titers with a response latency matching antibody decay (8–9 mo), as observed in the pilot. Falsified if daratumumab responders show no fall in the candidature autoantibody, or if non-responders retain elevated titers with intact CD38+ plasma cells.
Treatment: Research-stage only. Narrows the humoral-autoimmunity target to the CD38+ long-lived plasma cell and supports plasma-cell-directed agents (daratumumab) over B-cell depletion in the autoantibody-positive subgroup; none of this is a prescribing recommendation.
Limitation: Direct dUTPase→LLPC causality not experimentally proven (mouse used EBV dUTPase only); TFH/activin-A axis not independently replicated; dUTPase antibodies could reflect reactivation rather than steady-state LLPC output; not severity-stratified. NK data (Ray 2026) constrain any planned depletion by warning that CD38+ NK co-deletion may blunt response in already-NK-poor patients (Ray et al. 2026). (Origin: /integrate-topic daratumumab update.)

Hypothesis p=0.26A circulating serum factor in ME/CFS and Long COVID reproduces a biphasic muscle metabolic phenotype in otherwise-healthy biofabricated tissue

Evidence: Healthy 3D biofabricated skeletal muscle exposed in vitro to ME/CFS and Long COVID patient sera shows up-regulated glycolysis, mitochondrial hyperfusion, elevated oxygen consumption early, then contractile weakness and toroidal mitochondrial fragmentation by 96–144 h (Mughal et al. 2025). The serum-transfer phenomenon has prior support across models (Schreiner et al. 2020) (Appelman et al. 2024); anti-mitochondrial antibodies are excluded as the mediator (Nilsson et al. 2020). Note: the Fluge monolayer-myoblast layer (myoblasts exposed to ME/CFS serum) failed a large pre-registered replication that found no difference in maximal respiratory capacity across >1,700 Seahorse tests (Ryback et al. 2026); the null is scoped to the monolayer assay and does not extend to the structurally intact 3D biofabricated model or in-tissue/activity-dependent models (Øystein Fluge et al. 2016) (Ryback et al. 2026). Raw 0.65, discounted 0.26 by in vitro population weight 0.40.
Citations: (Mughal et al. 2025) (Øystein Fluge et al. 2016) (Schreiner et al. 2020) (Appelman et al. 2024) (Nilsson et al. 2020) (Ryback et al. 2026)
Mechanism: A serum-borne mediator (candidate classes: cytokines, HHV-6 microRNA, exosomes, viral fragments) drives pyruvate dehydrogenase impairment and a compensatory glycolytic/hypermetabolic adaptation that decompensates into mitochondrial fragmentation and reduced contractile capacity – reproducing in isolated healthy muscle the glycolytic shift documented in patient biopsies, independent of deconditioning, neural outflow, or vascular factors.
Chapter ref: ch20 Family 17: A Circulating Serum Factor Reproduces the ME/CFS Muscle Metabolic Phenotype in Healthy Tissue; ch07 sec-21: Is Short-Exposure Muscle Metabolism Hypermetabolic or Hypometabolic in ME/CFS?
Prediction: Depleting or neutralizing the putative serum factor (immunoadsorption, specific cytokine/exosome blockade) should prevent or reverse the derangement – attenuated glycolysis, preserved mitochondrial integrity – in the 3D model, and measurably reduce post-exertional muscle fatigue if translatable. Falsified if patient sera no longer induce the biphasic response with the factor removed.
Treatment: Not a prescribing recommendation. Identifies the circulating mediator as a research target (immunoadsorption, cytokine blockade) that is still preclinical; no clinical mitigation is established.
Limitation: Anchor finding is single in vitro study (unpopulated; not independently replicated); not severity-stratified; mediator identity unknown; in vitro population weight 0.40 caps clinical relevance. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera.)

Speculation p=0.28Biofabricated 3D skeletal muscle as a functional ex vivo assay for serum-borne muscle pathology

Evidence: A validated 3D biofabrication platform has been used to expose healthy muscle to patient sera and produce a mechanistic metabolic readout (Mughal et al. 2025), built on platform validation in Duchenne MD and drug screening (Fernández-Garibay et al. 2022). Raw 0.70, discounted 0.28 by in vitro population weight 0.40.
Citations: (Mughal et al. 2025) (Fernández-Garibay et al. 2022)
Mechanism: If the biphasic serum response is reproducible and patient-specific, the biofabricated tissue provides a medium-throughput functional endophenotype – a measurable readout of whether an individual’s serum impairs muscle metabolism – usable to screen candidate circulating mediators and modulators before animal or human trials.
Chapter ref: ch20: Biofabricated 3D Skeletal Muscle as a Functional Assay for Serum-Borne Muscle Pathogens
Prediction: A blinded cohort of ME/CFS, Long COVID, and healthy sera should yield a biphasic muscle response that is significantly more frequent/severe in patient versus control sera and correlates with reported PEM or fatigue severity.
Treatment: Research tool only; not a therapeutic proposal.
Limitation: Diagnostic utility, inter-lab reproducibility, and correlation with clinical severity are unproven; lacks neural/immune/vascular context; not severity-stratified. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera.)

Speculation p=0.30The in vitro serum response is a three-phase sequence (PDH compensation → cofactor exhaustion → mitochondrial collapse), not a static state

Evidence: Biofabricated 3D skeletal muscle exposed to patient serum shows up-regulated glycolysis and mitochondrial hyperfusion at 48 h, then toroidal fragmentation and contractile failure at 96–144 h (Mughal et al. 2025); patient sera were also reported to drive PDH-related myoblast metabolic shift (Øystein Fluge et al. 2016), though this monolayer-myoblast serum effect failed a large pre-registered replication (Ryback et al. 2026). The phase decomposition is an interpretation reconciling short-exposure hypermetabolic and long-exposure hypometabolic findings; raw 0.30, discounted from in vitro weight.
Citations: (Mughal et al. 2025) (Øystein Fluge et al. 2016) (Ryback et al. 2026)
Mechanism: Serum-driven pyruvate-dehydrogenase inhibition forces compensatory glycolysis and substrate shift (Phase I); sustained high-flux glycolysis depletes cofactor and antioxidant pools (NAD⁺/NADH, thiamine pyrophosphate, glutathione) (Phase II); exhaustion tips into mitochondrial fragmentation and contractile failure (Phase III) – reconciling ostensibly contradictory hypermetabolic and hypometabolic bodies of evidence into one temporal cycle that maps onto the PEM time-course.
Chapter ref: ch07 sec-21: A Three-Phase Model: PDH Compensation, Cofactor Exhaustion, Mitochondrial Collapse
Prediction: Serial PDH activity, NAD⁺/NADH, and mitochondrial morphology measurements in the 3D model must show PDH decline first, then cofactor depletion, then fragmentation – any different ordering falsifies the model.
Treatment: Not a prescribing recommendation. Suggests phase-specific, mechanistically-motivated candidate metabolic support (thiamine/riboflavin/alpha-lipoic acid, tested in vitro first); no clinical use established.
Limitation: Single un-replicated in vitro study; phase sequence not directly measured as a continuous trajectory; not severity-stratified. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera – brainstorm 1.1.)

Speculation p=0.22The circulating serum factor is likely a multi-component cocktail rather than a single molecule

Evidence: The biphasic response engages at least four distinct processes (PDH inhibition, calcium dysregulation, mitochondrial fission, contractile impairment), which no single species plausibly drives (Mughal et al. 2025); anti-mitochondrial antibodies are excluded as the sole mediator (Nilsson et al. 2020); serum transfers a multi-faceted antiviral-metabolic state (Schreiner et al. 2020). Discounted from in vitro/indirect evidence.
Citations: (Mughal et al. 2025) (Nilsson et al. 2020) (Schreiner et al. 2020)
Mechanism: A concurrent set of mediators – a PDH-inhibitory cytokine (TNFα/IL-6), a mitochondrial-fission or cell-danger signal (e.g. HHV-6 microRNA), a calcium/ion-channel disruptor, and an inflammatory/exosomal danger signal – rather than one molecule, explains why removing any single ingredient may fail to rescue the muscle phenotype.
Chapter ref: ch20 Family 17: The Circulating Serum Factor Is Likely a Multi-Component Cocktail, Not a Single Molecule
Prediction: Serum fractionation should show that no single fraction (e.g. exosomes alone, a single cytokine) recapitulates the full biphasic phenotype; falsified if one isolated fraction reproduces the complete picture.
Treatment: Not a prescribing recommendation. Argues against single-cytokine-target strategies and toward broader serum-factor removal (immunoadsorption) as the research direction; no clinical use.
Limitation: Structurally motivated inference, not directly demonstrated; mediator identity unknown; not severity-stratified. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera – brainstorm 1.3.)

Open Question p=n/aSerum fractionation of ME/CFS sera is the highest-priority experiment to identify the circulating mediator

Evidence: The unknown serum mediator must be narrowed from an anonymous class to a molecular species; fractionation by size, heat-lability, nuclease-sensitivity, and immunoglobulin depletion, tested on the 3D model, is the decisive experiment (Mughal et al. 2025) (Øystein Fluge et al. 2016) (Schreiner et al. 2020) (Nilsson et al. 2020).
Citations: (Mughal et al. 2025) (Øystein Fluge et al. 2016) (Schreiner et al. 2020) (Nilsson et al. 2020) (Ryback et al. 2026)
Mechanism: Fractionating serum and applying each fraction to biofabricated muscle with quantitative readouts (lactate efflux, PDK mRNA, mitochondrial morphology, contractile force) identifies which molecular class transmits the metabolic pathology; a parallel blinded ME/CFS vs LC vs healthy comparison tests whether the assay discriminates conditions. The fractionation rationale rests on the 3D biofabricated model and Schreiner cell-transfer findings; the monolayer-myoblast serum layer failed a large pre-registered replication (Ryback et al. 2026), so the fractionation target is the 3D-model phenotype rather than the refuted monolayer readout.
Chapter ref: ch20: Serum Fractionation Is the Highest-Priority Next Experiment for Identifying the Circulating Mediator
Prediction: The active mediator class will be recoverable, and patient sera will induce a fraction-dependent biphasic response reproducibly; falsified if no fraction reproduces the phenotype in a validated assay.
Treatment: Research-direction formulation; not a treatment proposal.
Limitation: Requires replication of the anchoring in vitro platform and individual (non-pooled) patient sera; not yet performed. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera – brainstorm 2.1.)

Speculation p=0.20Toroidal mitochondrial fragmentation as a candidate disease-specific ultrastructural signature in ME/CFS muscle

Evidence: Biofabricated 3D muscle exposed to patient serum shows ring-shaped (toroidal) mitochondrial fragmentation at the decompensation phase (Mughal et al. 2025), a morphology distinct from normal fission and linked to Drp1 over-activation/Opa1 loss; whether it generalizes to patient biopsies is untested. Discounted from in vitro evidence.
Citations: (Mughal et al. 2025) (Charlton et al. 2025)
Mechanism: A stress-specific mitochondrial morphology distinct from normal fission, plausibly reflecting Drp1 over-activation and Opa1 loss; if reproduced in patient-muscle EM and absent from the deconditioning bed-rest phenotype and inflammatory myopathy, it could serve as an objective, self-report-independent microscope-based marker of ME/CFS muscle pathology.
Chapter ref: ch20 Family 17: Toroidal Mitochondrial Fragmentation as a Candidate Disease-Specific Ultrastructural Signature
Prediction: Electron microscopy of ME/CFS patient muscle biopsies should show toroidal mitochondria at a higher rate than bed-rest-treated healthy muscle and age-matched controls; falsified if toroidal forms are equally common in bed-rest or healthy tissue.
Treatment: Not a prescribing recommendation. If toroidal morphology proves irreversible, it would reinforce energy pacing as tissue preservation rather than mere symptom control; no clinical intervention.
Limitation: Morphology seen in one in vitro study; may be an artifact of the culture system; biopsy confirmation and specificity untested; not severity-stratified. (Origin: /integrate-topic biofabrication-mughal-3d-muscle-cfs-sera – brainstorm 1.2.)

Speculation p=0.40Hibernation Arousal Machinery as a Cross-Species Resource for the ME/CFS Low-Energy State

Evidence: Hibernating mammals exhibit a reversible, spontaneously-arousing torpor state driven by conserved signals: central adenosine A1-receptor activation induces torpor in a non-hibernating rat (Shimaoka et al. 2018); arachidonic-acid -> PPAR-alpha/TRPV-Ca2+ is an arousal lipid regulator (L. Wang et al. 2026); mTORC1 reactivation is a conserved metabolic switch (C.-W. Wu and Storey 2021); BAT UCP1 thermogenesis drives arousal (Hunstiger, Johannsen, and Oliver 2023); non-neuronal choroid-plexus sensors may initiate arousal (Markussen et al. 2024); and non-hibernating mammalian cells retain a latent “hibernation-like” adaptive-pausing program (Jobava et al. 2021). All model-system/cross-species (heavily discounted); no ME/CFS human data.
Citations: (Shimaoka et al. 2018) (L. Wang et al. 2026) (C.-W. Wu and Storey 2021) (Hunstiger, Johannsen, and Oliver 2023) (Markussen et al. 2024) (Jobava et al. 2021) (Duffy, Staples, and Tessier 2022)
Mechanism: The arousal/recovery machinery is a (reversible) energy-state-switch resource whose signals (mTORC1, ISR/adaptive-pausing, arachidonic-acid/PPAR-alpha, A1AR-as-entry/A2A-as-arousal-shift) overlap the paper’s existing ISR/mTORC1 content (ch07 Post-Exertional Malaise Timing Reflects Integrated Stress Response Kinetics and ISR assay content); it contributes a specific assay (polysome-profile) and a cross-species precedent that ISR/pausing is reversible, rather than a distinct novel mechanism.
Chapter ref: ch18: Hibernation Arousal Biology as a Candidate Resource for Metabolic-Reactivation Research; ch07 ISR cross-ref Post-Exertional Malaise Timing Reflects Integrated Stress Response Kinetics
Prediction: A discriminating test with a matched-sedentary control arm: patient-derived ME/CFS PBMCs/iPSC cells should show a reduced polysome:monosome ratio and stalled-initiation-codon mRNAs relative to both healthy AND matched-sedentary controls, partially reversible on ISR inhibition (ISRIB) or mTORC1 reactivation beyond the sedentary-control component. A normal polysome profile falsifies pausing engagement; a positive-only-vs-healthy (not sedentary) result is ambiguous. Research prediction only.
Treatment: None. Basic-science drug-discovery resource; no clinical recommendation. Interbout arousal increases oxidative damage (Duffy, Staples, and Tessier 2022) – a safety caveat gating any future therapeutic extrapolation.
Limitation: All model-system/cross-species (translation gap); no ME/CFS human data; torpor is largely a small-mammal adaptation (primate hibernation rare – dwarf lemur (Blanco et al. 2024)). Severity applicability unknown. (Origin: /integrate-topic hibernation-biology-torpor-arousal.)

Hypothesis p=0.75SARS-CoV-2 infection raises new-onset ME/CFS risk, persisting up to four years post-infection

Evidence: A large electronic health record cohort ($n = 147{,}377$; Montefiore) found elevated new-onset ME/CFS risk sustained to four years after SARS-CoV-2 infection, adjusted hazard ratio 1.46 (hospitalized) and 1.56 (non-hospitalized) versus 111{,}301 controls (Hadidchi et al. 2025). Directionally consistent with RECOVER ($n = 11{,}785$, approx. 2.8-fold incidence, IOM criteria) (Leonard A. Jason et al. 2025), the CDC INSPIRE cohort ($n = 4{,}376$) (Unger et al. 2024), and a CDC fatiguing-illness cohort ($n = 13{,}611$; outcome is post-COVID fatigue, not ME/CFS criteria) (Vu et al. 2024). Raw 0.75 (ME/CFS cohort, peer-reviewed, large n), discounted unchanged at weight 1.00.
Citations: (Hadidchi et al. 2025) (Leonard A. Jason et al. 2025) (Unger et al. 2024) (Vu et al. 2024)
Mechanism: An acute SARS-CoV-2 infection triggers a post-infectious process that, in a susceptible subset, resolves into chronic ME/CFS – consistent with the post-infectious trigger model established for Epstein-Barr virus and SARS-CoV-1 (Hickie et al. 2006a) (Moldofsky and Patcai 2011). The four-year persistence argues against a transient pandemic-era effect and for a sustained post-infectious pathophysiology. None of the acute-phase biomarkers tested (ferritin, D-dimer, LDH, CRP) discriminated converters, separating acute inflammatory markers from post-acute predictors (Hadidchi et al. 2025).
Chapter ref: ch01 sec-04: Epidemiology, Epidemiology; ch14d: Speculative Cross-Disease Connections; ch06: Onset Patterns
Prediction: ME/CFS incidence after SARS-CoV-2 should remain above pre-pandemic baseline for several years post-infection in other large cohorts with comparable follow-up; the four-year hazard-ratio elevation should replicate. Falsified if cohorts with $\ge$ 4-year follow-up find the excess risk decays to baseline by year two to three, or if the finding reflects EHR/ascertainment artifact rather than true new-onset disease.
Treatment: None – epidemiological risk finding, not an intervention. Implies post-COVID monitoring: patients with persisting post-acute symptoms should be assessed for ME/CFS criteria and managed per standard ME/CFS guidance; no novel treatment is indicated.
Limitation: The four-year horizon is single-study (not yet directly replicated – comparators have 6-month to 2-year follow-up); EHR ICD-coded outcome underestimates true ME/CFS and cannot confirm diagnostic criteria; pre-COVID baseline ME/CFS status was not excluded; severity applicability unknown – cohort not severity-stratified. (Origin: /integrate-topic hadidchi2025-cfs-me-4yrs-post-covid.)

Hypothesis p=0.40Post-acute (not acute) iron dynamics as the temporal predictor of post-COVID ME/CFS conversion

Evidence: The Hadidchi null that acute-phase biomarkers (ferritin, D-dimer, LDH, CRP) do not predict post-COVID ME/CFS conversion, combined with the iron-redox temporal phase-shift model (Hadidchi et al. 2025) (Hanson et al. 2024) (Sonnweber et al. 2022) (Bahar Kavyani et al. 2024), constrains the converter-discriminating window to the post-acute transition (weeks 2–12 onward) rather than the acute phase.
Citations: (Hadidchi et al. 2025) (Hanson et al. 2024) (Sonnweber et al. 2022) (Bahar Kavyani et al. 2024)
Mechanism: Acute inflammatory hypoferremia (IL-6 -> hepcidin spike) is universal and non-discriminatory; converters diverge from recoverers in the post-acute transition as hepcidin falls and functional iron deficiency (elevated ferritin, low TSAT) emerges – so a converter-predicting blood test is expected at 3–6 months, not day 0–30.
Chapter ref: ch14d: postcovid postacute iron prediction
Prediction: A longitudinal study measuring ferritin, TSAT, hepcidin at 1, 3, 6, 12 months post-COVID must show 3-month (not 1-month) iron trajectories separating future ME/CFS converters (IOM-2015) from recoverers, e.g. AUC > 0.70. Falsified if all post-acute timepoints are non-predictive.
Treatment: None – research/diagnostic prediction, not an intervention. If validated, an approx. $20 ferritin+TSAT measurement at the 3-month post-COVID visit could flag patients for ME/CFS monitoring.
Limitation: No existing longitudinal post-COVID iron-panel study; severity applicability unknown (incidence cohorts not severity-stratified); single-study null. (Origin: /integrate-topic hadidchi2025-cfs-me-4yrs-post-covid – Phase-4 brainstorm idea 1.1.)

Open Question p=0.50Medical Education Reform — What Would Move the Needle?

Evidence: ch40 documents a medical-education deficit but no intervention studies; no study tests whether adding ME/CFS content to curricula changes diagnosis/referral/outcomes (Bateman et al. 2021). A mandatory-CME model (e.g., opioid/implicit-bias-mandate-style) is proposed but never trialed. (Certainty: descriptive evidence only; no intervention).
Citations: (Bateman et al. 2021)
Mechanism: The education deficit is self-perpetuating — physicians untrained in ME/CFS cannot recognize it or teach the next generation; reform requires evidence-tested interventions, not only aspirational recommendations.
Chapter ref: ch40: Medical Education Reform: What Would Move the Needle?
Prediction: A CME/curriculum intervention should measurably change diagnosis rate, referral pattern, or outcome (vs. no-intervention control); absence of change would indicate the barrier is structural, not educational.
Limitation: No prospective intervention data in ME/CFS; severity applicable across all levels. (ch40, prior cycle.)

Hypothesis p=0.55Diagnostic Delay as a Healthcare-System Metric (iatrogenic harm)

Evidence: Average ME/CFS diagnostic delay 5-10 years across surveys (Bateman et al. 2021); delay is iatrogenic harm (inappropriate GET/psychiatric misdiagnosis during the gap, employment/savings loss, repeated invalidation). Consistent across multiple surveys; no prospective delay-to-harm study.
Citations: (Bateman et al. 2021)
Mechanism: Diagnostic delay exposes patients to harmful recommendations and invalidation; reducing delay is a low-cost “treatment” (each month of delay avoided is a month of appropriate care).
Chapter ref: ch40: Diagnostic Delay as a Healthcare-System Metric
Prediction: A registry linking diagnostic delay (months) to later QoL/employment outcomes should show a monotonic relationship (delay -> harm).
Limitation: Cross-sectional survey evidence; no prospective study; severity applicability all, greatest for those progressing to severe. (ch40, prior cycle.)

Open Question p=N/ADo Specialist Clinics Improve Outcomes?

Evidence: Zero RCTs/controlled comparisons of specialist ME/CFS clinics (BHC, Stanford, OMI, Hadassah, Charite) vs. primary care on diagnostic accuracy, functional outcomes, or satisfaction. Recommendation for specialist care rests on expert consensus/patient preference, not demonstrated superiority.
Citations: (Bateman et al. 2021)
Mechanism: Whether specialty structure improves outcomes is unmeasured; clinic existence creates an implicit standard of care that most patients cannot access, without evidence it is superior.
Chapter ref: ch40: Do Specialist Clinics Improve Outcomes?; ch40 BHC care-delivery example
Prediction: A controlled comparison (or quasi-experiment, The Specialist Clinic Quasi-Experiment) of specialist vs. well-informed primary care; null would mean current unequal access is not justified by outcomes.
Limitation: Evidence gap identification, not a factual claim; severity most critical for severe/very-severe. (ch40, prior cycle.)

Speculation p=0.50Co-Production as a Healthcare Design Principle

Evidence: Qualitative co-production study (n=8) with ME/CFS patients identified preference for multidisciplinary teams, mHealth symptom tracking, PEM-aware pathways (Thornton et al. 2025). Small/unvalidated in ME/CFS; methodology sound generically.
Citations: (Thornton et al. 2025)
Mechanism: Designing services WITH patients (co-production) may better meet energy-limited, multidisciplinary, PEM-aware needs than standard clinic-centered design.
Chapter ref: ch40: Co-Production as a Healthcare Design Principle
Prediction: Co-produced ME/CFS services should show higher retention/satisfaction than non-co-designed services.
Limitation: n=8 qualitative; not ME/CFS-validated; severity most important for severe/very-severe. CONFLICT-TENSION: the S4ME (2026) severe-care factsheet argues multidisciplinary teams are “likely counterproductive” for housebound/bedbound patients and recommends a single point of contact instead (Severe-Care Advocacy Favors a Single Point of Contact Over Multidisciplinary Teams); the preference for multidisciplinary teams may be severity-stratified (ambulatory vs housebound), untested. (ch40, prior cycle + severe-mecfs-management cycle.)

Open Question p=N/AThe Universal Access Null Hypothesis — What If Reform Doesn’t Change Outcomes?

Evidence: If all barriers removed (education, delay, guidelines, specialist access, disability, invalidation), would outcomes improve? Partly refuted (reducing delay/GET-avoidance almost certainly prevents iatrogenic harm) but not for benefit (no evidence any care model improves long-term function).
Citations: (Bateman et al. 2021)
Mechanism: Distinguishes removing HARM (GET, invalidation, benefit denial — evidence-backed) from providing BENEFIT (specialist care, co-production, multidisciplinary — untested).
Chapter ref: ch40: The Universal Access Null Hypothesis
Prediction: Reform separating harm-removal from benefit-provision; benefit claims remain untested until controlled comparisons exist.
Limitation: Descriptive/qualitative health-services evidence; no care-model comparative studies. (ch40, prior cycle.)

Hypothesis p=0.45Diagnostic Desert — Geographic Access as a Self-Reinforcing Trap

Evidence: 10x variation in ME/CFS diagnosis across English ICBs (Samms and Ponting 2025); PEM makes travel an active harm (radius constraint shorter than any other chronic disease), producing a self-reinforcing desert (fewer diagnoses -> apparent low prevalence -> no deployment -> fewer access).
Citations: (Samms and Ponting 2025)
Mechanism: PEM-adjusted travel impedance (distance x PEM severity) predicts diagnosis probability better than raw distance, and is ME/CFS-specific (fibromyalgia, lacking PEM barrier, shows weaker clustering).
Chapter ref: ch40: Diagnostic Desert Hypothesis
Prediction: PEM-adjusted distance from nearest specialist inversely correlates with ICB diagnosis rate, stronger for ME/CFS than fibromyalgia; opening a clinic in a desert should produce a diagnostic surge.
Limitation: Geographic variation documented; the trap mechanism is novel/untested; severity most relevant for severe/very-severe. (ch40, prior cycle.)

Speculation p=0.35The Guideline Cascade Duration — Contested Diseases Have Longer Implementation Gaps

Evidence: Evidence-to-practice lag averages ~17 years; contested illnesses likely longer (pre-existing beliefs, no specialty champions, hard-to-audit negative recommendations). German G-BA statutory directive is a natural experiment.
Citations: (Bateman et al. 2021)
Mechanism: For contested diseases, guideline uptake is systematically delayed: belief revision, absence of specialty champions, and negative (GET-withdrawal) recommendations are harder to audit than positive ones.
Chapter ref: ch40: The Guideline Cascade Duration — Contested Diseases Have Longer Implementation Gaps
Prediction: German physicians show higher PEM-concern awareness than UK at matched time-from-publication (Germany: binding directive vs. UK advisory NICE 2021).
Limitation: Implementation-science support is general; contested-illness amplification novel; NICE-2021 cascade unobserved; all severity. (ch40, prior cycle.)

Speculation p=0.40Healthcare Invalidation x PEM Synergy — a Bootstrap Trap Preventing Care Re-Entry

Evidence: Invalidation (Bontempo 2025) drives healthcare avoidance; PEM prevents clinic participation (Bontempo, Bontempo, and Duberstein 2025). Interaction: too-traumatized-to-trust AND too-fatigued-to-shop = bootstrap trap, even when good care becomes available.
Citations: (Bontempo, Bontempo, and Duberstein 2025)
Mechanism: Past invalidation + PEM (provider-searching is physiologically costly) keeps the most-harmed patients outside the system; reform investments may preferentially benefit milder patients.
Chapter ref: ch40: Healthcare Invalidation × PEM Synergy — A Bootstrap Trap Preventing Care Re-Entry
Prediction: When a new specialist opens, the first 50 patients will have lower baseline invalidation scores and milder PEM than the regional ME/CFS population; refuted if avoidance does not interact with PEM to predict time-to-specialist-contact.
Limitation: Both mechanisms individually documented; synergy inferred not directly observed; severity most critical for long-duration severe patients. (ch40, prior cycle.)

Open Question p=0.75The NICE 2021 Natural Experiment — did UK practice change?

Evidence: NICE NG206 (Oct 2021) withdrew GET and recognized PEM; UK centralized data (CPRD ~20m patients) makes an interrupted-time-series answerable: pre-NICE vs post-NICE on diagnoses/GET-proxy-referrals/delay, with fibromyalgia+RA controls (Samms and Ponting 2025).
Citations: (Samms and Ponting 2025)
Mechanism: Guideline-attributable change detected by structural break in referral/exercise patterns for ME/CFS but not controls; if no break, implementation-gap concern confirmed.
Chapter ref: ch40: The NICE 2021 Natural Experiment
Prediction: Structural break in ME/CFS referral/diagnosis patterns post-NICE-2021 vs. controls; absence confirms the implementation gap.
Limitation: Feasibility high (standard method, data exist); the study has not yet been run. (ch40, prior cycle.)

Open Question p=0.55The Specialist Clinic Quasi-Experiment — distance as instrument

Evidence: Geographic proximity to specialist clinic as instrumental variable (PEM-adjusted distance); two-stage least squares estimates clinic-use effect on delay/function/PE-related ED visits, with MS comparator.
Mechanism: If specialist clinics improve outcomes, distance->use->better function; if null (Do Specialist Clinics Improve Outcomes?), apparent benefit reflects selection (wealthier/milder/health-literate reach them).
Chapter ref: ch40: The Specialist Clinic Quasi-Experiment
Prediction: Two-stage: PEM-adjusted distance predicts clinic use; clinic use predicts diagnostic delay, functional status, and PEM-ED visits; controls for selection by severity.
Limitation: Feasibility reasonable (UK data exist; US richer on claims, poorer on function); un-run. (ch40, prior cycle.)

Open Question p=0.50The Medical Education Pilot Trial — does teaching doctors change what they do?

Evidence: Cluster-RCT (n=20-30 practices, 1:1) of a 2-hour ME/CFS module (criteria, PEM, guidelines, EHR tool) vs. usual care; primary outcome new ME/CFS diagnoses per 10k patients over 18 months; powered to detect a doubling.
Mechanism: If education raises knowledge but not diagnosis rate, the barrier is structural (time, referral-pathway absence), not educational — upstream-failure model would need revision.
Chapter ref: ch40: The Medical Education Pilot Trial
Prediction: Education arm shows higher ME/CFS diagnosis rate; absence implies structural (not educational) barrier.
Limitation: 18-month follow-up may be too short (delay averages 5+ years); feasible but un-run. (ch40, prior cycle.)

Open Question p=0.55The PEM-Proof Care Delivery Trial — visits designed around what ME/CFS does to patients

Evidence: RCT (n=200 newly diagnosed, two arms, identical clinical content) comparing PEM-avoiding delivery (telehealth, async messaging, actigraphy, coordinator, energy-conservation pre-visit) vs. standard in-person clinic; primary SF-36 physical function at 12 months, secondary PEM frequency.
Mechanism: If PEM-avoiding delivery is superior on functional outcomes, PEM-aware delivery is a clinical-effectiveness lever, not just a preference; severe/housebound hypothesized to benefit most.
Chapter ref: ch40: The PEM-Proof Care Delivery Trial
Prediction: PEM-avoiding arm shows better SF-36 physical function / fewer PEM episodes at 12 months than standard care.
Limitation: Blinding to delivery channel impossible (actigraphy as objective check); feasible but un-run. (ch40, prior cycle.)

Hypothesis p=0.60PEM Is a Multi-Symptom Cascade Extending Beyond Simple Fatigue

Evidence: Meta-analytic odds ratio ~10.4 for PEM in ME/CFS vs controls (A. Brown and Jason 2020); factor analysis in n=2,308 loads PEM as a distinct primary factor separable from cognitive/sleep domains (Conroy, Islam, and Jason 2023); exploratory factor analysis shows the fatigue component of PEM itself has two sub-components (generalized and muscle-specific) (Stephanie L. McManimen, Sunnquist, and Jason 2019); PEM-specific instruments (DSQ-PEM) retain discriminant validity against fatigue scales (Kuczyk et al. 2025). Replicated across factor-analytic, meta-analytic, and psychometric lines.
Citations: (A. Brown and Jason 2020), (Conroy, Islam, and Jason 2023), (Stephanie L. McManimen, Sunnquist, and Jason 2019), (Kuczyk et al. 2025)
Mechanism: PEM is a multi-effector post-exertional cascade; fatigue is one component readout (itself multi-faceted) among several; this supports that PEM extends beyond simple fatigue, but does not by itself establish that PEM can occur with zero fatigue.
Chapter ref: ch03 sec-01-pem: PEM Is a Multi-Symptom Cascade That Extends Beyond Simple Fatigue
Prediction: A within-subject crash time-series will determine whether crashes dominated by cognitive/autonomic/pain symptoms can occur with fatigue absent (two independent criteria: free-text fatigue-absence, and zero fatigue under direct multi-synonym probing).
Limitation: Construct-distinction evidence is moderately strong; the stronger claim “PEM can occur with zero fatigue” remains a candidate hypothesis without direct documentation. Severity coverage not stratified; ambulatory cohorts. (ch03, this cycle.)

Speculation p=0.60Diagnostic Criteria Hierarchy: PEM-Required vs PEM-Optional Definitions

Evidence: Systematic review of 25 case definitions: Fukuda (1994) requires fatigue, makes PEM optional; CCC (2003), ICC (2011), IOM (2015) make PEM compulsory (E.-J. Lim and Son 2020) (Institute of Medicine 2015). PEM-required criteria identify patients differing on intervention tolerance (Kielland, Liu, and Jason 2023) and stratify severity independent of fatigue (May et al. 2020).
Citations: (E.-J. Lim and Son 2020), (Institute of Medicine 2015), (Kielland, Liu, and Jason 2023)
Mechanism: Using PEM-optional (Fukuda) criteria dilutes the diagnosis with chronic-fatigue-of-other-etiology patients, weakening the cohort for PEM-relevant trials and epidemiology.
Chapter ref: ch03 sec-01-pem: Diagnostic Criteria Differentiate PEM-Requiring from PEM-Optional Definitions
Prediction: Fukuda-positive/IOM-negative patients should show at least a 0.3-point smaller SF-36 physical-function improvement or at least a 10-percentage-point higher non-recovery rate vs CCC/IOM-positive patients over 12 months; indistinguishable trajectories refute PEM’s independent prognostic weight.
Limitation: Hierarchy documented; the trajectory/response prediction is indirect (mixed-criteria cohorts, no direct within-subject trial). Severity not stratified; ambulatory. (ch03, this cycle.)

Open Question p=N/ASelf-Reported vs Objective PEM: Why the Gap?

Evidence: In Long COVID, 67% self-report PEM but only 5.9% (2/34) show objectively provoked PEM after standardized CPET (Stussman et al. 2025); PEM-item wording materially changes classification (Leonard A. Jason et al. 2015).
Citations: (Stussman et al. 2025), (Leonard A. Jason et al. 2015)
Mechanism: Open question: gap could reflect protocol failure to provoke PEM, non-physical triggers, conflation of exercise fatigue with PEM, or need for 2-day CPET — four candidate explanations with precedent.
Chapter ref: ch03 sec-01-pem: How Well Does Self-Reported PEM Align with Objectively Provoked PEM?
Prediction: A study using both self-report and 2-day objective provocation with multi-trigger protocols will resolve which explanation(s) account for the self-report/objective dissociation.
Limitation: Small Long COVID cohort (n=34) able to attend CPET — biased toward ambulatory patients; not stratified by severity. (ch03, this cycle.)

Speculation p=0.40PEM-Without-Fatigue: Temporal Cases (crash window / onset / baseline)

Evidence: No study directly tests PEM-without-fatigue. Indirect support: four-symptom PEM prediction set includes three non-fatigue domains (Davenport et al. 2023); PEM stratifies severity independent of baseline fatigue (May et al. 2020); crash-predominant phenotype recognised (ch18).
Citations: (Davenport et al. 2023), (May et al. 2020)
Mechanism: PEM is effort-triggered multi-system; a crash may be dominated by cognitive/autonomic/pain/flulike symptoms (fatigue subdominant or below reporting threshold), at onset, or a patient may have near-normal baseline fatigue yet be PEM-positive (crash-predominant).
Chapter ref: ch03 sec-01-pem: Is Post-Exertional Malaise Possible Without Fatigue?
Prediction: Two independent criteria: (a) true biological fatigue-absence is falsified if no objectively confirmed crash is ever described in purely non-fatigue terms under free-text elicitation (no fatigue prompt); (b) the artifact reading is falsified if a measurable fraction (>10%) of crashes are described in purely non-fatigue terms under free-text and still report zero fatigue under direct multi-synonym probing.
Limitation: All scenarios candidate; no direct within-title documentation; severity not stratified, severe/very-severe reporting constrained. (ch03, this cycle.)

Speculation p=0.40PEM-Without-Fatigue: Candidate Mechanisms M1–M5

Evidence: Five indirect mechanisms: (M1) non-fatigue dominant expressive channels (Davenport et al. 2023) (Conroy, Islam, and Jason 2023); (M2) two-factor PEM substructure (Stephanie L. McManimen, Sunnquist, and Jason 2019); (M3) trigger-type dependence; (M4) labeling/measurement frame (Leonard A. Jason et al. 2015); (M5) self-report vs objective dissociation (Stussman et al. 2025). M4 is the most parsimonious (no novel pathway; directly supported by wording-sensitivity evidence; cert 0.45).
Citations: (Davenport et al. 2023), (Conroy, Islam, and Jason 2023), (Stephanie L. McManimen, Sunnquist, and Jason 2019), (Leonard A. Jason et al. 2015), (Stussman et al. 2025)
Mechanism: If PEM-without-fatigue occurs, any of five pathways (channel dominance, two-factor PEM, trigger type, labeling, measurement gap) could explain a non-fatigue-dominant crash; none isolated or directly tested. The labeling/measurement-artifact account (M4, with M5) is the default until disproven.
Chapter ref: ch03 sec-01-pem: Mechanisms by Which a Crash Could Express Without Prominent Fatigue
Prediction: Falsifiable, two independent criteria: (a) true biological fatigue-absence falsified if no crash is ever fatigue-free under free-text elicitation (no fatigue prompt); (b) artifact reading falsified if a measurable fraction of crashes are fatigue-free under free-text AND still report zero fatigue under direct multi-synonym probing. Additional: (c) trigger-type differences in fatigue intensity; (d) M2 low-muscle-fatigue-arm sparing.
Limitation: All mechanisms inferred (indirect/analogical); none directly tested; stringent honesty constraint — must not assert as established fact. Severity not stratified. (ch03, this cycle.)

Open Question p=N/AIs “PEM Without Fatigue” Real Biology or Always a Measurement Artifact?

Evidence: Strong null interpretation: reported fatigue-absence may always be measurement/labeling artifact — fatigue is imprecise and culturally loaded (Leonard A. Jason et al. 2015); IOM requires fatigue alongside PEM (Institute of Medicine 2015); no validated PEM instrument scores PEM-positive with explicit zero fatigue.
Citations: (Leonard A. Jason et al. 2015), (Institute of Medicine 2015)
Mechanism: If null holds, the temporal cases and mechanisms above describe experience/reporting variants (channel dominance, vocabulary, trigger type) of fatigue-looking crashes, not biological fatigue-absence. Deciding biology-vs-artifact determines whether low-fatigue crashes are treated as real PEM or an elicitation gap.
Chapter ref: ch03 sec-01-pem: Is “PEM Without Fatigue” Ever a Real Phenomenon, or Always a Measurement Artifact?
Prediction: A within-subject study pairing free-text elicitation (no fatigue prompt) with a structured multi-system PEM instrument discriminates: if patients describing non-fatigue crashes still rate fatigue when directly probed, artifact dominates; if a measurable fraction report no fatigue even under direct multi-synonym probing, artifact reading is incomplete.
Limitation: Open question — no decisive data; presented as the balancing null, not an assertion. Severity not stratified. (ch03, this cycle.)

Hypothesis p=0.60Fatigue Is Embedded Within Coherent Multi-System Symptom Clusters (Brain / Gut-Immune / Autonomic)

Evidence: Factor analysis in n=748 adults with ME/CFS identified three symptom clusters — Brain, Gut-Immune, Autonomic — each with strong model fit (EFA/CFA/SEM) (Habermann-Horstmeier and Horstmeier 2025); fatigue distributed across domains rather than forming a separate factor.
Citations: (Habermann-Horstmeier and Horstmeier 2025)
Mechanism: Fatigue is a cross-cutting symptom whose precipitating driver may differ by cluster, rather than a unitary construct; treating fatigue as a single target may miss cluster-specific mechanisms.
Chapter ref: ch03 sec-00-fatigue: Fatigue Is Embedded Within Coherent Multi-System Symptom Clusters
Prediction: In a pre-registered trial stratifying ME/CFS patients by dominant symptom cluster and assigning cluster-targeted intervention, a significant cluster × treatment interaction on a validated fatigue instrument must emerge (p < 0.05 corrected; interaction η²p ≥ 0.01). Absence of interaction or only a main effect refutes cluster-specific fatigue mechanism.
Limitation: Cross-sectional; self-report; single-sample factor solution not yet independently replicated; does not establish causal mechanism. Severity not stratified. (ch03, this cycle.)

Hypothesis p=0.65Severity-Based Fatigue Subgroups Exist and Depend on Case Definition

Evidence: K-means clustering of the Australian registry (n=2,873 patients vs n=797 controls) identified four severity subgroups with significantly impaired HRQoL; ICC-defined patients had worst outcomes; cluster structure depended on case definition (Eaton-Fitch and Marshall-Gradisnik 2026).
Citations: (Eaton-Fitch and Marshall-Gradisnik 2026)
Mechanism: Fatigue severity is not a single number; the observed subgroup structure changes with the diagnostic criteria applied, so severity classification is partly an artifact of case definition.
Chapter ref: ch03 sec-00-fatigue: Severity-Based Fatigue Subgroups Exist and Depend on Case Definition
Prediction: Replication of k-means in independent cohort (n ≥ 500, same case definition) must recover ≥3 of 4 original subgroups (silhouette ≥ 0.25), with significant between-cluster differences (p < 0.05 corrected) on external measure — e.g. 7-day actigraphy step count or MFI-20 subscales. Refuted if all pairwise contrasts p > 0.05 after Holm–Bonferroni or if different case definition dissolves the 4-cluster solution.
Limitation: Self-selected registry volunteers; cross-sectional; k-means clusters descriptive, may not capture true latent subtypes; single registry not yet replicated. Severity: ambulatory registry; severe/very-severe under-represented. (ch03, this cycle.)

Open Question p=N/ADo Subjective Fatigue and Objective Fatigability Dissociate in ME/CFS?

Evidence: Subjective fatigue and objective fatigability may not correlate closely: actigraphy shows objectively reduced activity (Liu et al. 2025) and neuroimaging shows impaired central motor drive (Bedard et al. 2026), while subjective scales capture experienced exhaustion; no large study has correlated both within the same ME/CFS cohort.
Citations: (Liu et al. 2025), (Bedard et al. 2026)
Mechanism: Open question: whether patients reporting severe fatigue but normal performance (or vice versa) are a meaningful subtype or measurement dissociation is unresolved for fatigue specifically, by contrast with related autonomic findings.
Chapter ref: ch03 sec-00-fatigue: Do Subjective Fatigue and Objective Fatigability Dissociate?
Prediction: A study pairing a subjective fatigue instrument with objective fatigability (actigraphy + performance test) in the same cohort will establish whether the two orders dissociate and what any dissociation predicts clinically.
Limitation: No dedicated fatigue discordance study exists; related evidence from other autonomic measures not directly transferable. Severity not stratified. (ch03, this cycle.)

Speculation p=0.42Acute CNS-Parenchymal Inflammation Predicts Long-Term Neurodegeneration — A Cross-Disease Template for ME/CFS

Evidence: A large US electronic-health-record cohort (TriNetX, ~129 M patients, 72 organizations) found encephalitis predicts long-term dementia with composite risk ratios of 2.11 (>60 y) and 5.16 (40–60 y) over ten years; strongest for non-infectious/post-infectious inflammatory (autoimmune) etiologies (RR 3.93), non-significant for bacterial (RR 1.35, 0.97–1.87) (Aditi et al. 2026). An independent UK primary-care cohort reproduced elevated post-encephalitis cognitive/dementia sequelae (Granerod et al. 2017). CNS-inflammation → neurodegeneration mechanism is established in Alzheimer’s disease (microglial/astroglial activation (Heneka et al. 2025); causal NLRP3 roles in tau (Ising et al. 2019) and amyloid (Heneka et al. 2013)); ME/CFS grouped with chronic neuroimmune-dysfunction disorders (Cohen et al. 2024). Cross-disease inference: same acute-CNS-inflammation → long-term-neurodegeneration axis is the template by which sustained ME/CFS neuroinflammation (TSPO-PET (Nakatomi et al. 2014); exosome-driven microglial IL-1$\beta$ (Tsilioni, Natelson, and Theoharides 2022)) could, if it persists, contribute to progressive cognitive decline.
Citations: (Aditi et al. 2026) (Granerod et al. 2017) (Heneka et al. 2025) (Ising et al. 2019) (Heneka et al. 2013) (Cohen et al. 2024) (Javonillo et al. 2026) (Nakatomi et al. 2014) (Tsilioni, Natelson, and Theoharides 2022)
Mechanism: Acute, direct CNS parenchymal inflammation (encephalitis) — especially autoimmune/post-infectious-inflammatory — initiates a self-sustaining neuroinflammatory process (microglial/astroglial activation, NLRP3) that measurably precedes and predicts later neurodegeneration. By analogy, sustained ME/CFS neuroinflammation could, if it persists, be a progressive pathology driver rather than a reversible symptom correlate; inflammation quality (autoimmune/post-infectious) matters more than mere presence (bacterial encephalitis not significant).
Chapter ref: ch14d-cross-disease: encephalitis dementia precedent; ch19 causal hierarchy: chronicity criterion longitudinal temporal upgrade; ch08 subsec-05 neuroinflammation: Hundreds of Blood Biomarkers Distinguish ME/CFS, Independent of Inactivity; ch18 sec-14 long-term consequences: Chronic Glymphatic Impairment as a Risk Factor for Accelerated Neurodegeneration in ME/CFS
Prediction: If sustained ME/CFS neuroinflammation drives neurodegeneration through the encephalitis/Alzheimer axis, then longitudinal ME/CFS cohorts (n $>=$ 200, ≥ 5-yr follow-up) will show (i) elevated baseline plasma NfL/GFAP or CSF neuroinflammation markers predicting worsening objective cognitive performance over time, and (ii) a dose–response between TSPO-PET signal (or equivalent glial marker) and rate of cognitive decline. Falsified if ME/CFS patients show stable cognition over ≥ 5 years despite elevated neuroinflammation markers, or if no neuroinflammation marker predicts any cognitive trajectory (indicating non-degenerative, reversible neuroinflammation).
Treatment: No clinical treatment recommendation (cross-disease, indirect). If confirmed, would shift rationale toward earlier anti-neuroinflammatory intervention and long-term cognitive monitoring of ME/CFS patients — not currently actionable, no ME/CFS dementia data.
Limitation: Entirely analogical — no ME/CFS dementia cohort exists; the ME/CFS neuroinflammation premise itself is contested (single TSPO-PET study null (Raijmakers et al. 2021), Neuroinflammation Measurement Challenges). Etiology specificity (bacterial null) and animal preprint cautioning that acute viral neuroinflammation does not uniformly accelerate amyloid (Javonillo et al. 2026) temper over-generalization. Severity applicability unknown (general-population cohort, not ME/CFS, not severity-stratified). (Origin: /integrate-topic encephalitis-dementia-risk.)

Speculation p=0.25Anellovirus Burden as an Immune-State Signature Rather Than a Viral Cause in ME/CFS

Evidence: Briese et al. multicenter virome study found no consistent group-specific viral nucleic acid differences in ME/CFS except a lower prevalence of anelloviruses in cases (30.5% vs 54.1%, $p$ < 0.001) (Briese et al. 2023); Maguire et al. (IMPACC, n=1154) found elevated Anelloviridae transcript reads in the long-COVID physical-disability PRO group (Maguire et al. 2026), and noted prior anellovirus links to chronic fatigue syndrome and multiple sclerosis. Neither study measured anellovirus burden and immune markers in the same ME/CFS patients; immune-state-marker reading is an inference, not a finding.
Citations: (Briese et al. 2023), (Maguire et al. 2026)
Mechanism: Anellovirus (TTV/TTMV) burden may reflect immune-competence/dysregulation state rather than productive viral infection. Directional form: innate-hyperactivation-dominant subgroups → low TTV; adaptive-exhaustion-dominant → high TTV (see Constitutive Immune Activation Without Secretory Output). The apparent direction difference across cohorts may be a method artifact (Briese binary prevalence vs Maguire transcript abundance).
Chapter ref: ch08 sec-06-other-implicated-viruses: Anellovirus Burden as an Immune-State Signature Rather Than a Viral Cause in ME/CFS (non-directional form) and Constitutive Immune Activation Without Secretory Output (directional form); ch14d-cross-disease long-COVID overlap
Prediction: Using the directional map (innate-hyperactivation→low TTV; adaptive-exhaustion→high TTV), a harmonized TTV assay in one cohort will show innate-hyperactivation-dominant subgroups with lower TTV and adaptive-exhaustion-dominant subgroups with higher TTV than controls, and TTV tracking an immune marker rather than symptom severity alone. Falsified if TTV is a stable, immune-independent, disease-defining feature across all subgroups, or if harmonization eliminates the direction difference (both non-significant).
Limitation: Briese full text abstract-level (in-document numbers from the sibling spec used); Maguire is a hospitalized COVID-19 cohort (translation gap, population-discounted 0.85; hospitalization/steroid confound uneliminated); cross-sectional; competing explanations include method incommensurability, redundant-bystander, technical bias, and cohort-composition effects. Severity not stratified. Certainty lowered to 0.25 per adversarial review (was 0.40). (ch08, this cycle.)

Open Question p=N/ADoes Viral-Gene-Product Surveillance Have Any Diagnostic or Mechanistic Role in ME/CFS?

Evidence: Multicenter PCR + high-throughput sequencing surveillance of blood, feces, and saliva found no consistent group-specific viral differences in ME/CFS except lower anelloviruses, leading the authors to recommend focusing future investigations on adaptive immune responses rather than viral-gene-product surveillance (Briese et al. 2023).
Citations: (Briese et al. 2023)
Mechanism: Open question: whether viral-gene-product surveillance adds diagnostic or mechanistic value in ME/CFS, or whether the productive-replication arm of the viral-persistence model is unsupported and should yield to adaptive-immune investigation.
Chapter ref: ch08 sec-06-other-implicated-viruses: No Productive Viral Infection Detected by Nucleic-Acid Surveillance of Non-Tissue Compartments in ME/CFS
Prediction: A study correlating anellovirus burden and adaptive-immune markers in the same ME/CFS cohort will determine whether the null surveillance result reflects absence of productive infection (supporting adaptive-immune focus) or a surveillance technical limitation.
Limitation: Balancing null presented as a research-direction question, not an assertion; single null surveillance study; full cohort-stratification details not retrieved. Severity not stratified. (ch08, this cycle.)

Hypothesis p=0.52The Luteal Phase May Confer Relative Orthostatic Protection in POTS

Evidence: Ten women with POTS tolerated upright posture longer with less symptomatic orthostatic decompensation in the mid-luteal phase (higher presyncope rate in early follicular) than in the early follicular phase, independent of medication (Fu et al. 2010). A same-group follow-up found no cycle-phase effect on presyncope incidence, baroreflex sensitivity, or muscle sympathetic nerve activity (Stickford et al. 2015) — an unresolved contradiction on the orthostatic-tolerance outcome.
Citations: (Fu et al. 2010) (Stickford et al. 2015)
Mechanism: Luteal-phase hormonal milieu (progesterone vasodilation/volume retention, estradiol NO-mediated vasodilation) may improve peripheral vascular/volume response to orthostasis in a condition defined by maladaptive vasodilation and venous pooling — but the effect is unconfirmed (single small positive vs single small same-group null on the same outcome).
Chapter ref: ch21 sec-01 menstrual cyclicity: The Luteal Phase May Confer Relative Orthostatic Protection in POTS, Is the Luteal-Phase Orthostatic Effect Real? — An Unresolved Contradiction
Prediction: A prospective cycle-phase study in women with ME/CFS + comorbid POTS (n ≥ 20, LH-surge-confirmed phase, head-up tilt/active stand + symptom scoring) should reproduce a luteal-phase improvement in orthostatic tolerance and symptom burden. Falsified if luteal-phase tolerance is not better than early-follicular in the same women — which is what the same-group Stickford study found.
Treatment: Cycle phase is a confounder to control in orthostatic treatment trials; phase-aware planning of orthostatic symptom burden. No new drug. The treatment implication is conditional on resolving the unconfirmed effect.
Limitation: Unresolved contradiction: Fu (n=10) positive vs Stickford (n=10, same lab) null on presyncope/orthostatic tolerance; not independently replicated; POTS population, not a ME/CFS cohort (population weight 0.80); not severity-stratified.

Hypothesis p=0.40Postpartum Immune Reconstitution as an ME/CFS Trigger or Relapse Driver

Evidence: Pregnancy induces a tolerogenic, Th2-shifted immune state (elevated Tregs, reduced NK cytotoxicity) that collapses rapidly postpartum as estrogen and progesterone fall; postpartum relapse at 3–6 months is a documented clinical pattern (Slack et al. 2023); pregnancy reported as ME/CFS trigger in a minority of cases (Thomas et al. 2022). Immune-reconstitution mechanism extrapolated from HIV-IRIS precedent, not measured directly in ME/CFS.
Citations: (Slack et al. 2023) (Thomas et al. 2022) (Schacterle and Komaroff 2004)
Mechanism: Postpartum collapse of pregnancy-induced immunotolerance + rapid steroid/allopregnanolone withdrawal precipitates immune reconstitution against persistent antigens and unmasks autonomic/sleep instability in the 3–6 month window.
Chapter ref: ch21 sec-02 pregnancy & postpartum: Postpartum Immune Reconstitution as an ME/CFS Trigger or Relapse Driver
Prediction: Women developing ME/CFS within 12 months postpartum should show steeper Treg decline between 3rd trimester and 3 months postpartum and evidence of EBV/HHV-6 reactivation in the postpartum window vs healthy postpartum controls. Falsified if immune-reconstitution markers do not differ between postpartum-onset ME/CFS and healthy postpartum women.
Treatment: Postpartum window becomes a monitoring + early-intervention target in high-risk women; no current treatment recommendation.
Limitation: Relapse timing documented but mechanism extrapolated (HIV-IRIS analogy), not directly measured in ME/CFS; not severity-stratified.

Hypothesis p=0.26Estradiol and Progesterone Directly Modulate Mast Cell Activation

Evidence: In-vitro and animal work establishes estradiol primes/sensitises mast cells toward activation and degranulation while progesterone modulates (often suppresses) them (Zaitsu et al. 2007) (Muñoz-Cruz et al. 2015) (F. Jensen et al. 2010); environmental estrogens modulate mast cells (Narita et al. 2007); ovariectomised-mouse model confirms ovarian-hormone-withdrawal effects on mast cells (F. Jensen et al. 2010).
Citations: (Zaitsu et al. 2007) (Muñoz-Cruz et al. 2015) (F. Jensen et al. 2010) (Narita et al. 2007)
Mechanism: Estradiol primes mast cell activation; progesterone modulates/suppresses it; ovarian-hormone state (cycle phase, pregnancy, menopause, HRT) therefore modulates mast-cell-dependent symptoms — the mechanistic bridge connecting female predominance of ME/CFS to its MCAS comorbidity.
Chapter ref: ch21 sec-04 HRT: Estradiol and Progesterone Directly Modulate Mast Cell Activation
Prediction: In women with ME/CFS + MCAS symptoms, serum tryptase/histamine should vary across the menstrual cycle and change with menopause or HRT in a direction consistent with model-system findings. Falsified if mast cell mediators in ME/CFS patients do not track hormonal state.
Treatment: Hormonal state is a candidate modifiable lever on mast-cell-dependent symptoms; direction of effect is individual (double-edged). No blanket recommendation.
Limitation: Model-system evidence only (in vitro/animal, population weight 0.40–0.50) — translation gap to patients not confirmed in ME/CFS; not severity-stratified.

Hypothesis p=0.58Endometriosis Is Associated with a ~2.8-Fold Higher Risk of ME/CFS

Evidence: Systematic review + meta-analysis (13 studies, n from 84 to 134,805): endometriosis associated with OR 2.79 (95% CI 2.00–3.89) for ME/CFS; pooled OR 2.52 for the ME/CFS–endometriosis association; association heterogeneity I² = 0.0% (Compton et al. 2025). Consistent with earlier population-based finding (36% vs 17% endometriosis in cases vs controls (Boneva, Lin, and Unger 2011)).
Citations: (Compton et al. 2025) (Boneva, Lin, and Unger 2011)
Mechanism: Shared mast-cell/neuroinflammatory axis: endometriosis lesions produce IL-1β, TNF-α, NGF; both conditions feature mast cell hyperactivation, peripheral nerve sensitisation, estrogen-driven inflammation; bidirectional causation plausible (endometriosis inflammation seeds central sensitisation; ME/CFS immune dysregulation permits ectopic implant survival).
Chapter ref: ch21 sec-05 endometriosis & PCOS: Endometriosis Is Associated with a ~2.8-Fold Higher Risk of ME/CFS, Shared Mast Cell and Neuroinflammatory Axis Underlies the Endometriosis–ME/CFS Association
Prediction: In a prospective cohort, incidence of ME/CFS in surgically-confirmed endometriosis should exceed that in matched controls, and ME/CFS severity should correlate with endometriosis disease activity. Falsified if association reflects only ascertainment.
Treatment: Screening for endometriosis in ME/CFS women with pelvic symptoms and vice versa; treating one condition may be relevant to the other. No claim that treating endometriosis treats ME/CFS.
Limitation: Cross-sectional designs predominate; 54% self-reported endometriosis; US-centric; prevalence heterogeneity extreme (I² > 98%). Not severity-stratified.

Open Question p=N/ADoes PCOS Co-occur with ME/CFS? — No Direct Evidence Found

Evidence: Targeted search found no direct, adequately powered PCOS–ME/CFS co-occurrence study; the only hit (n=37, no control, null fatigue correlation, low-tier journal) fails inclusion criteria. PCOS association remains unestablished.
Mechanism: Open question: PCOS is an estrogen/androgen-dominant endocrine-metabolic condition in premenopausal women, so a mechanistic rationale for overlap exists, but no evidence supports an association.
Chapter ref: ch21 sec-05 endometriosis & PCOS: Does PCOS Co-occur with ME/CFS? — No Direct Evidence Found
Prediction: A proper prevalence study of PCOS in ME/CFS (and vice versa) is required before any association claim.
Treatment: No association to act on; manage any coexisting PCOS and ME/CFS as distinct conditions.
Limitation: Absence of evidence, not evidence of absence; treated as genuinely open.

Speculation p=0.25Cyclic Luteal→Menstrual Allopregnanolone Withdrawal as a Monthly PEM Amplifier

Evidence: Allopregnanolone (progesterone metabolite, GABA-A positive allosteric modulator) rises across the luteal phase and crashes at the luteal→menstrual transition. The postpartum version of this withdrawal is documented as a vulnerability window (Slack et al. 2023); the monthly version is unexamined. No ME/CFS study has measured allopregnanolone across the cycle or linked it to PEM timing.
Citations: (Slack et al. 2023)
Mechanism: Each cycle’s premenstrual allopregnanolone drop unmask the same GABAergic-autonomic vulnerability the postpartum drop produces in a single large event, amplifying PEM and autonomic instability at the luteal→menstrual boundary.
Chapter ref: ch21 sec-01 menstrual cyclicity: Cyclic Luteal→Menstrual Allopregnanolone Withdrawal as a Monthly PEM Amplifier
Prediction: PEM episode frequency and severity are significantly higher in the 3 days after luteal allopregnanolone peaks than at other phases, reduced by luteal micronized progesterone. Falsified if PEM does not cluster at the withdrawal boundary or progesterone does not blunt it.
Treatment: Luteal-phase micronized progesterone as a repeatable, non-pregnancy probe of the GABAergic-autonomic axis; interaction data not individually checked — manual review.
Limitation: Extrapolation from postpartum analogue; no ME/CFS cycle allopregnanolone data; not severity-stratified. (Origin: brainstorm.)

Speculation p=0.20Menopause as an Iron-Accumulation / Ferroptosis Transition in ME/CFS

Evidence: Reproductive axis is usually framed as iron-deficiency (heavy menses, pregnancy). Cessation of menstrual blood loss at menopause halts monthly iron excretion; chronic iron overload drives Fenton-chemistry oxidative stress and ferroptosis linked to mitochondrial dysfunction. No ME/CFS menopause-iron study exists.
Mechanism: Menopausal iron accumulation → ferroptosis → further mitochondrial/energy failure explains a non-hormonal, non-mast cell reason for menopause-transition worsening in a subgroup; predicts iron reduction (phlebotomy) rather than supplementation may help.
Chapter ref: ch21 sec-03 menopause transition: Menopause as an Iron-Accumulation / Ferroptosis Transition — the Reverse of Deficiency
Prediction: Post-menopausal ME/CFS women with prior menorrhagia show elevated ferritin/transferrin saturation and ferroptosis markers (4-HNE, MDA) vs controls, correlating with fatigue severity. Falsified if iron stores and ferroptosis markers are not elevated or do not track severity.
Treatment: Iron reduction (phlebotomy, iron-restricted diet) — speculative; contraindicated in iron-deficient patients.
Limitation: Novel, mechanistically coherent but entirely unexamined in ME/CFS; menopause-transition evidence base itself indirect. (Origin: brainstorm.)

Prediction p=0.40Mast-Cell-Stabilizer Trials Should Be Stratified by Hormonal Phase and E2:P4 Ratio

Evidence: Estrogen-mast cell axis established in model systems (Zaitsu et al. 2007) (Muñoz-Cruz et al. 2015) (F. Jensen et al. 2010); mast-cell-stabilizer trials in ME/CFS are analysed without any hormonal covariate.
Citations: (Zaitsu et al. 2007) (Muñoz-Cruz et al. 2015) (F. Jensen et al. 2010)
Mechanism: E2:P4 ratio (not absolute levels) predicts mast cell reactivity — high E2 with low P4 = unopposed estradiol priming; high P4 = suppression. Stabilizer efficacy may concentrate in high-E2/low-P4 states.
Chapter ref: ch21 sec-04 HRT: Mast-Cell-Stabilizer Trials Should Be Stratified by Hormonal Phase and E2:P4 Ratio
Prediction: ≥30% greater stabilizer benefit in high-E2/low-P4 phase than high-P4 phase or post-menopause. Falsified if stabilizer efficacy is uniform across hormonal states.
Treatment: Research direction only (re-analysis/trial design); no treatment recommendation.
Limitation: Ratio-specific claim untested; research design. (Origin: brainstorm.)

Prediction p=0.45The Postpartum 3–6 Month Window Is an Ideal Prospective Onset-Cohort Target

Evidence: Postpartum window is a defined, time-bounded event with an identifiable trigger state (collapse of pregnancy hormonal/immune milieu); postpartum relapse timing 3–6 months documented (Slack et al. 2023) (Schacterle and Komaroff 2004).
Citations: (Slack et al. 2023) (Schacterle and Komaroff 2004)
Mechanism: Recruiting at-risk primiparous women before/during pregnancy and following through the 3–6 month window with biobanking captures onset prospectively — a direct test of the immune-reconstitution hypothesis.
Chapter ref: ch21 sec-02 postpartum: The Postpartum 3–6 Month Window Is an Ideal Prospective Onset-Cohort Target
Prediction: ~2% of ~150 at-risk primiparous women develop new/relapsed ME/CFS in the 3–6 month window, with steeper Treg decline and higher EBV/HHV-6 reactivation vs postpartum-healthy controls.
Treatment: Enables early biomarkers and early intervention; research design.
Limitation: Onset signal real but immune mechanism unproven; recruitment feasibility. (Origin: brainstorm.)

Prediction p=0.40A Longitudinal AMH Slope Panel Predicts Early Menopause Before the Event

Evidence: AMH is stable across the menstrual cycle and reflects remaining follicular pool; early menopause over-represented in ME/CFS (Boneva et al. 2015); four candidate mechanisms for accelerated ovarian aging documented Accelerated Ovarian Aging: An Inflammatory or Mitochondrial Driver.
Citations: (Boneva et al. 2015)
Mechanism: Serial AMH + FSH/estradiol use the rate of AMH decline as marker of accelerated follicular attrition, flagging early menopause years before the event.
Chapter ref: ch21 sec-03 early menopause: A Longitudinal AMH Slope Panel Predicts Early Menopause Before the Event
Prediction: AMH decline slope >1.5× steeper in ME/CFS women aged 25–40 vs controls over 24 months; steep-slope subgroup reaches menopause earlier.
Treatment: Early bone/cardiovascular protection and HRT planning; biomarker.
Limitation: No longitudinal AMH data exist in ME/CFS. (Origin: brainstorm.)

Prediction p=0.35Pregnancy Response as a Natural Subtype-to-Treatment Diagnostic Probe

Evidence: Tripartite pregnancy split documented (Schacterle and Komaroff 2004) (Slack et al. 2023); candidate subtype axes (immune/volume/metabolic) proposed Which Pathophysiological Subtypes Improve Versus Worsen in Pregnancy?.
Citations: (Schacterle and Komaroff 2004) (Slack et al. 2023)
Mechanism: A woman’s pregnancy response assigns her to a treatment-relevant subtype: improved (immune/volume dominant), worsened (metabolic dominant), unchanged (uncertain) — convertible to a stratification input for trials and care.
Chapter ref: ch21 sec-02 pregnancy tripartite: Pregnancy Response as a Natural Subtype-to-Treatment Diagnostic Probe
Prediction: Baseline measures (autoantibodies, tilt-test, CPET) predict pregnancy trajectory with concordance >0.6, and reported direction predicts response to the corresponding treatment class.
Treatment: Stratification input; no standalone treatment claim.
Limitation: Tripartite observation established but subtype-probe use novel/untested. (Origin: brainstorm.)

Open Question p=0.40Crash-Phase Near-Syncopal “Shutdown” Quality: Distinct Phenomenon or Energy-Collapse Metaphor?

Evidence: Patients sometimes describe severe crashes in near-syncopal terms (sinking, approaching loss of consciousness without losing consciousness). Orthostatic syncope/pre-syncope is common in ME/CFS on upright tilt (Bou-Holaigah et al. 1995) (Stewart et al. 1998) and correlates with worse PEM (L. A. Jason, McGarrigle, and Vermeulen 2024), but no study names a distinct near-syncopal quality intrinsic to the post-exertional crash. Cerebral hypoperfusion under orthostatic/exertional stress is common (Novak et al. 2026) but contested (Razumovsky et al. 2003); a submaximal-exercise provocation reports reduced global CBF with post-exertional worsening (Rayhan and Baraniuk 2021).
Citations: (Bou-Holaigah et al. 1995) (Stewart et al. 1998) (L. A. Jason, McGarrigle, and Vermeulen 2024) (Novak et al. 2026) (Razumovsky et al. 2003) (Rayhan and Baraniuk 2021)
Mechanism: If real, a crash-phase near-syncopal quality would reflect exertional/orthostatic cerebral hypoperfusion distinct from upright-posture-only orthostatic syncope; the substrate (reduced cerebral blood flow) is plausible but mechanistically contested.
Chapter ref: ch03 sec-01-pem physiological basis: Do Some PEM Crashes Carry a Near-Syncopal “Shutdown” Quality Distinct From Upright Orthostatic Intolerance?
Prediction: A two-day CPET in patients reporting near-syncopal crashes should produce orthostatic-BP or cerebral-perfusion changes during/shortly after exertion (not only on upright tilt), and the near-syncopal description should co-occur with objective orthostatic findings. Falsified if no orthostatic/perfusion change is found in either post-exertional or upright settings.
Treatment: Orthostatic testing of patients who describe near-syncopal crashes; no new treatment claim.
Limitation: Feature under test has no direct published support (0 PubMed hits for crash-phase near-syncope terms); severity applicability unknown. (Origin: integrate-topic crash-phase-near-syncope-pem, PARTIAL decision.)

ID / Label	Details	Phase / Cert
hyp virtual-hypoxia-brain-lactate	Elevated resting brain lactate with a blunted rise under hypoxia supports intrinsic mitochondrial/bioenergetic inefficiency (“virtual hypoxia”): patients show a ~27% higher baseline thalamic Lac/tCr (0.171 vs 0.135; $\beta$=0.035, $p$=0.021) and fail to raise it under two hypoxic challenges where controls do (H1 $p$=0.028, H2 $p$=0.039 vs patients H1 $p$=0.38, H2 $p$=0.46) (Bader et al. 2026). Consistent with Trapp’s virtual-hypoxia proposal (Trapp and Stys 2009) and bioenergetic-inefficiency accounts (Tomas et al. 2017). Replication: not yet — single preprint. Limitations: proof-of-concept, modest sample, cross-sectional, hypoxic challenge ≠ exertion. Falsifiability: resting lactate stays elevated or response stays blunted in a second independent cohort, and normalises with an intervention that restores mitochondrial ATP production. Consequence: gives a concrete brain-imaging readout that ME/CFS fatigue is linked to a measurable failure to ramp up energy production on demand. (ch07 lactate accumulation: Virtual Hypoxia: Elevated Resting Brain Lactate with Blunted Metabolic Reactivity; origin: integrate-topic experimental-hypoxia-virtual-hypoxia-brainstem.)	Phase 3 / 0.40
hyp brainstem-volume-open-question	Brainstem volume direction is unresolved: Vienna reports reduced brainstem/pons volume ($\beta$=-0.10% eTIV, $p$=0.013, FDR 0.039) (Bader et al. 2026), Griffith reports larger brainstem/pons (pons $p$=0.003, whole brainstem $p$=0.005) (Thapaliya et al. 2023). Thapaliya2023/Barnden2018/Thapaliya2022 are the same Griffith lab cluster — not independent replications (Thapaliya et al. 2023) (Barnden et al. 2018) (Thapaliya et al. 2022). Net independent clusters = 2, opposite directions. Replication: neither direction replicated by an independent lab. Falsifiability: multi-site harmonised segmentation gives a consistent direction across independent labs. Consequence: the true direction would localise a structural site of pathology and help explain brainstem-related symptoms (autonomic dysfunction, dyspnoea, sleep disturbance). (ch09 sec-01: Brainstem Volume Direction Contradicts Across Cohorts; origin: integrate-topic experimental-hypoxia-virtual-hypoxia-brainstem.)	Phase 3 / N/A
spec cbf-reactivity-variability	Gross cerebrovascular reactivity to a controlled hypoxic stressor is preserved on average (whole-brain CBF +4.8±13.0% controls vs +3.7±11.7% patients, no group-mean difference), but patient-to-control variance ratios are elevated at the first challenge (H1: 2.27–6.94, significant in 3/4 ROIs after FDR), absent at H2 (Bader et al. 2026) (Biswal, Kunwar, and Natelson 2011) (He et al. 2013). Replication: not yet — exploratory variance analysis, single preprint. Limitations: variance-based, exploratory; hypoxic challenge ≠ exertion. Falsifiability: a second cohort reproduces elevated patient CBF-response variance at an initial challenge and correlates it with severity. Consequence: hints the difficulty may be unstable, person-to-person and moment-to-moment CBF adaptation rather than a fixed inability to change blood flow — unproven as a biomarker. (ch09 sec-05: Preserved Gross Cerebrovascular Reactivity but Greater Inter-Individual Variability; origin: integrate-topic experimental-hypoxia-virtual-hypoxia-brainstem.)	Phase 3 / 0.35
spec ms-virtual-hypoxia-bridge	The “virtual hypoxia” concept from multiple sclerosis — chronically failing tissue despite normal arterial oxygen due to impaired mitochondrial ATP + increased demand (Trapp and Stys 2009) (Mahad, Trapp, and Lassmann 2015) — is applied to ME/CFS as a mechanistic bridge, not identity, on the strength of the Vienna finding of elevated resting brain lactate and blunted metabolic reactivity despite normal arterial oxygenation (Bader et al. 2026). ME/CFS lacks MS demyelination; the shared element is bioenergetic inefficiency under normal oxygen. Replication: not independently replicated in ME/CFS; single-site preprint. Limitations: transfers bioenergetic-diagnostics logic only, not MS-specific interventions; no direct demonstration ME/CFS neurons fail under normal oxygen. Falsifiability: MS-like bioenergetic imaging signatures are abnormal in an ME/CFS virtual-hypoxia subgroup in the same direction as MS while NAA remains normal — refuted if ME/CFS shows no such signature despite elevated lactate. Consequence: lets ME/CFS research reuse bioenergetic imaging tools and diagnostic logic developed for MS. (ch16: Multiple Sclerosis “Virtual Hypoxia” as a Mechanistic Template — Not an Identity; origin: brainstorm.)	Phase 5 / 0.45
spec long-covid-virtual-hypoxia-trajectory	A shared virtual-hypoxia mechanism (elevated brain lactate, reduced metabolic flexibility) may underlie both ME/CFS and long COVID as a post-infectious metabolic signature, with trajectory distinguishing them: normalising in early long COVID recovery vs fixed in ME/CFS (Bader et al. 2026). Replication: not yet. Limitations: hypothesis-generating; the Vienna study did not measure long COVID; trajectory difference is inferred. Falsifiability: the bioenergetic marker (brain lactate / metabolic reactivity) normalises with recovery in early long COVID but stays fixed in ME/CFS — refuted if the marker is equally fixed in both. Consequence: a shared post-infectious metabolic mechanism would unify ME/CFS and long COVID research and allow cross-trial learning. (ch16: Shared “Virtual Hypoxia” Mechanism in Long COVID and ME/CFS — Different Trajectory; origin: brainstorm.)	Phase 5 / 0.40
spec thalamic-lactate-stratification	Elevated resting thalamic lactate (Lac/tCr) is a candidate stratification biomarker for trials of tissue-oxygenation or mitochondrial interventions (e.g. HBOT) in ME/CFS (Bader et al. 2026) (Hadanny et al. 2024). NOT a validated diagnostic; hypothesis-generating. Replication: not yet. Limitations: single preprint; requires prospective validation; peripheral-vs-brain lactate proxy unproven. Falsifiability: fails if it does not reach discriminating power (e.g. AUC ≥0.70) and test–retest reproducibility, and does not predict differential treatment response in a prospective trial. Consequence: if validated, gives trial designers a mechanism-anchored imaging readout to select the “virtual-hypoxia” subgroup most likely to respond. (ch36: Thalamic Lac/tCr as a Candidate Stratification Biomarker for Energy-Metabolism Trials; origin: brainstorm.)	Phase 5 / 0.40

ID / Label	Details	Phase / Cert
oq pots-cardiac-emergency-threshold	Open question: no ME/CFS-specific study defines when a POTS-related collapse becomes a cardiac emergency, and no ME/CFS data quantify sudden-cardiac risk in POTS; the cardiac-vs-neurally-mediated syncope distinction is transferred from general cardiology. Falsifiability: if cardiac syncope were specifically prevalent in ME/CFS, a cohort with ambulatory ECG monitoring or cardiac-syncope outcome would detect a higher-than-expected arrhythmic-event rate — untested. Consequence: the safe default (apply general-cardiology cardiac-syncope criteria) rests on transferred evidence; this is a genuine research priority, not resolved. (ch24 sec-12: Is There an ME/CFS-Specific Cardiac-Emergency Threshold in POTS?; origin: integrate-topic adult-red-flags-safety-netting-protocol.)	Phase 3 / N/A
oq syncope-recovery-time-discriminator	Open question: because ME/CFS has chronic orthostatic instability, the usual prodrome-based cardiac-vs-NMH syncope distinction is unreliable; recovery time and recovery quality after collapse is a candidate ME/CFS-appropriate discriminator (neurally-mediated recovers quickly supine with full orientation; cardiac/sustained-arrhythmia has slower, incomplete, or lingering recovery). Falsifiability: among ME/CFS patients with recurrent collapse, recovery-to-baseline within 5 min of supine positioning with full orientation and no chest pain/palpitations has near-zero true-cardiac rate; recovery >15 min, persistent confusion, chest pain, or palpitations has significantly higher identified cardiac/sustained-arrhythmia rate on urgent evaluation — untested. Consequence: if validated, gives a concrete rule for when a faint warrants urgent assessment. (ch24 sec-12: Can Recovery Time After Collapse Discriminate Cardiac From Neurally-Mediated Syncope in ME/CFS?; origin: brainstorm.)	Phase 5 / N/A
oq research-priorities-safety-netting	Research-priority statement: four ME/CFS-specific studies would move the safety protocol from transferred to measured risk — (1) ambulatory cardiac monitoring cohort for syncope, (2) severe-ME mortality/cachexia registry, (3) prospective incidence cohort for refeeding/VTE/pressure-ulcer sepsis in severe bedbound ME/CFS, (4) ED-recognition intervention study. Consequence: identifies the highest-impact research that would quantify medical-emergency risk specifically in severe ME/CFS. (ch24 sec-12: Research Priorities: Quantifying Medical-Emergency Risk in Severe ME/CFS; origin: brainstorm.)	Phase 5 / N/A

ID / Label	Details	Phase / Cert
spec long-covid-reversibility	Speculation: long COVID immune dysregulation (PD-1/TIM-3 exhaustion, elevated nucleocapsid IgG, neutralizing capacity) may be partly reversible within ~24 months, in contrast to the durable, epigenetically locked exhaustion of ME/CFS. Falsifiability: recovering post-infectious patients normalize PD-1/TIM-3 and reconstitute naive T/B within 24 months, while ME/CFS-progressors retain exhaustion markers with no reconstitution — tested by longitudinal immune profiling; falsified if recovering patients show no normalization or ME/CFS-progressors fully reconstitute. Consequence: an early abnormal immune profile in post-infectious illness is not necessarily permanent exhaustion; argues for early-intervention trials within a potentially reversible window. (ch08 sec-02: Long COVID Immune Dysregulation as a Time-Limited Precursor State; evidence Phetsouphanh 2024 cert 0.60; origin: literature synthesis.)	Phase 3 / 0.40
oq cortisol-postinfectious-contested	Open question: Klein et al. 2023 (Nature) reported lower morning cortisol in long COVID, convergent with ME/CFS HPA blunting; Fleischer et al. 2024 found no peripheral IL-1β/IL-6/TNFα or cortisol difference in PASC at a single timepoint. The discrepancy (morning multi-site vs single-timepoint peripheral) is unresolved; peripheral cytokines/cortisol are timepoint-sensitive and not stable stand-alone biomarkers. Falsifiability: dynamic (repeated/ACTH-stimulated) or severity-stratified cortisol measures would resolve whether hypocortisolism is consistent in the post-infectious phenotype. Consequence: clinicians/researchers should not rely on a single cortisol or cytokine measurement to confirm or exclude post-infectious immune/HPA dysregulation. (ch08 sec-02: Cortisol in Post-Infectious Illness: A Contested Peripheral Biomarker; evidence Klein 2023, Fleischer 2024; origin: literature synthesis.)	Phase 3 / N/A
lim no-immunotherapy-mecfs-basis	Limitation: no ME/CFS evidence base exists for borrowing oncology immunotherapy (checkpoint blockade anti-PD-1/anti-CTLA-4, CAR-T) to reverse T-cell exhaustion. Unselected IVIG/rituximab/TPE failed (RituxME phase III, post-COVID TPE phase II). Checkpoint blockade carries substantial immune-related-adverse-event/autoimmune risk in a population already predisposed. Consequence: patients/clinicians should not expect oncology immunotherapy for ME/CFS; plausible use is restricted to biomarker-defined, carefully selected subgroups as a research question, not a recommendation. (ch08 sec-12: No Evidence Base for Borrowing Oncology Immunotherapy in ME/CFS; evidence Kaplan 2026; origin: literature synthesis.)	Phase 3 / N/A
lim lc-convergence-cohort-overlap	Limitation: the ME/CFS–long-COVID convergence must not be read as independent corroboration — Petrov 2025 (convergent lymphocytes/cytokines) and Petrov 2026 (divergent monocyte/DC) are drawn from the same Plovdiv research group with shared authorship and overlapping recruitment (sample sizes n=190 vs n=207, precise overlap not fully documented); Phetsouphanh 2024 is a longitudinal extension of the same ADAPT biobank as Phetsouphanh 2022. Convergence + reversibility are analyses of overlapping patient populations, not independent replications. Consequence: the convergence and reversibility signals are weaker than their headline certainties imply; independent-cohort replication must precede any firm cross-disease claim. (ch08 sec-02: Cohort Overlap Constrains the Convergence Claim; origin: brainstorm.)	Phase 5 / 0.70
lim lc-reversibility-mild-bias	Limitation: long-COVID reversibility and EBV-null findings come from mild cohorts (Phetsouphanh mild-moderate, Hoeggerl mild/asymptomatic, Lorenz retrospective PCS); the severe or ME/CFS-phenotype subgroup — where persistence, epigenetic locking, and reactivation matter most — is under-sampled. Consequence: reversibility in mild cohorts should not be extrapolated to severe or ME/CFS-phenotype patients, where persistence is the documented pattern. (ch08 sec-02: Mild-Disease Sampling Bias in the Long-COVID Reversibility Evidence; origin: brainstorm.)	Phase 5 / 0.65
lim checkpoint-blockade-gate	Limitation/protective gate: cancer exhaustion-stage biology supplies a concrete selection criterion if checkpoint blockade is ever considered — anti-PD-1 rescues progenitor/transcriptionally-exhausted (chromatin-plastic) T cells but fails on terminally, epigenetically-fixed ones (established in the oncology exhaustion-lineage/checkpoint-response literature). Established ME/CFS exhaustion is epigenetically locked; early long-COVID exhaustion is transcriptionally reversible (resolves by 24 months (Phetsouphanh et al. 2024)). Gate: checkpoint blockade has rationale only in the transcriptionally-exhausted, PD-1-high, non-epigenetically-locked subset — present in early long COVID, absent in established ME/CFS. Consequence: if ME/CFS T cells show the terminally-exhausted epigenetic signature, anti-PD-1 lacks mechanistic rationale and would likely fail with autoimmune risk — a testable rule to prevent futile/harmful unselected immunotherapy. (ch08 sec-12: Checkpoint Blockade: A Protective Selection Gate Before Any Consideration; origin: brainstorm.)	Phase 5 / 0.35
syn lc-mecfs-convergence-reversible	Synthesis: long-COVID immune dysregulation is convergent with ME/CFS (same T-cell exhaustion, exhausted B cells, exaggerated EBV/VZV antibody responses (Klein et al. 2023); no significant lymphocyte/CD8/NK/cytokine difference (Petrov et al. 2025)) but partly reversible — resolving by ~24 months in mild cohorts, in contrast to the durable, epigenetically-locked exhaustion of established ME/CFS. Constrained by cohort-overlap (same Plovdiv group; Cohort Overlap Constrains the Convergence Claim) and mild-cohort sampling bias (Mild-Disease Sampling Bias in the Long-COVID Reversibility Evidence). Most defensible claim: the shared convergent pattern’s persistence — not its presence — distinguishes ME/CFS. Consequence: the key discriminator is whether immune dysregulation persists and is epigenetically locked, pointing to longitudinal designs and independent-cohort replication, and cautioning against treating reversible long-COVID changes as permanent exhaustion. (ch08 sec-02: Long COVID as a Convergent but Partly Reversible Immune State; origin: integrate-topic immune-checkpoint-tolerance-immunotherapy.)	Phase 10a / N/A

ID / Label	Details	Phase / Cert
hyp episwitch-3d-chromatin-biomarker	Hypothesis: 3D chromosome-conformation architecture in blood differs between severe ME/CFS and healthy controls, forming a blood-based diagnostic signal orthogonal to DNA methylation and miRNA markers (Hunter et al. 2025). Reported 92% sensitivity / 98% specificity / 96% accuracy in a single retrospective vendor-affiliated study (Oxford BioDynamics; n=47 severe ME/CFS vs 61 controls). Falsifiability: an independent cohort (n ≥ 100 ME/CFS vs matched controls) failing to reproduce sensitivity/specificity within ±5 percentage points, or EpiSwitch failing to distinguish ME/CFS from fibromyalgia/depression beyond chance, refutes specificity to ME/CFS. Consequence: if independently replicated, a blood-based objective diagnostic for ME/CFS becomes plausible — but today it is an unvalidated research finding; the reported specificity is against healthy controls only and cross-condition discrimination (fibromyalgia, depression, long-COVID fatigue) is untested. (ch36 sec: 3D Chromatin Architecture Is an Independent ME/CFS Biomarker Layer; evidence Hunter 2025 cert 0.50; severity severe/very-severe; origin: literature integration.)	Phase 3 / 0.50
hyp 3d-chromatin-IL2-jakstat-axis	Hypothesis: the IL-2/JAK-STAT pathway enrichment reported in the EpiSwitch ME/CFS panel is biologically mechanistically grounded — 3D chromatin topology is coupled to IL-2 and JAK-STAT signalling (TRIM28 organizes chromatin to control IL-2 in T cells (Wei et al. 2025); IL-2 restructures the 3D genome of CD4+ T cells (Ward et al. 2025); CTCF-coordinated 3D enhancer architecture shapes immune gene expression and CTCF depletion rewires the CD4+ T-cell response to JAK inhibitors by reprogramming the STAT5 enhancer network (E.-C. Lee et al. 2025)). This bridges the 3D-genomic biomarker finding to the documented T-cell and cytokine dysregulation in ME/CFS. Falsifiability: in an independent cohort, Hi-C or Capture-C in ME/CFS CD4+ T cells shows the IL-2-responsive loop contacts at the STAT5 enhancer loci (Lee 2025) shifted in the same direction as IL-2 stimulation, versus unstimulated healthy T cells. Refuted if these loop contacts are statistically indistinguishable (P ≥ 0.05) from healthy resting T cells after controlling for activation markers (CD69/CD25). Consequence: if the IL-2/3D-chromatin link holds, it strengthens the case for IL-2 as a therapeutic target and links an epigenetic biomarker to a testable immune mechanism; the current evidence is non-ME/CFS-specific (human/mouse T cells) and the translation to ME/CFS is unvalidated. (ch36 sec: The IL-2/JAK-STAT Axis Is Mechanistically Coupled to 3D Chromatin Architecture; evidence Wei/Ward/Lee 2025, cert 0.45; origin: literature integration.)	Phase 3 / 0.45
hyp trim28-unifying-node	Hypothesis: impaired TRIM28 (KAP1) chromatin maintenance unifies the 3D-genomic (EpiSwitch), HERV-expression, and IL-2 findings — loss of TRIM28-mediated silencing/loop-maintenance simultaneously derepresses HERVs (Giménez-Orenga, HERV activation coincides with TRIM28/SETDB1 sites (Giménez-Orenga et al. 2025)) and destabilises the 3D architecture constraining IL-2/JAK-STAT loci (Wei: TRIM28→IL-2 (Wei et al. 2025); Hunter: IL-2/JAK-STAT hub (Hunter et al. 2025)). Falsifiability: ME/CFS immune cells with normal TRIM28 occupancy and SETDB1-dependent H3K9me3 at the implicated loop-anchor/HERV loci refute the unifying-node hypothesis. Consequence: two “competing” epigenetic fingerprints may trace to one upstream chromatin-maintenance defect, a natural focus for mechanistic study and eventual drug targeting. (ch36 sec: TRIM28 as a Unifying Node Across 3D-Genomic, HERV, and IL-2 Findings; evidence Wei 2025 + Giménez-Orenga 2025, cert 0.30; origin: brainstorm.)	Phase 5 / 0.30
hyp frozen-chromatin-state	Hypothesis: the ME/CFS EpiSwitch signature reflects a loop conformation frozen in the activated position — the chromatin-architectural residue of an immune-activation program triggered but never resolved — rather than active IL-2 signalling. IL-2 restructures the CD4+ 3D genome dynamically (Ward (Ward et al. 2025)), so a static snapshot may capture a “trapped” state; this resolves the paradox of normal-to-low circulating cytokines despite immune-dysfunction phenotypes. Falsifiability: ME/CFS CD4+ T-cell Hi-C at IL-2-pathway loci indistinguishable from healthy cells after normalising for activation status, or loops relaxing to baseline within hours of rest, refute the frozen-chromatin hypothesis. Consequence: if immune cells are stuck in the “on” position at the DNA-folding level, it explains persistence after the trigger clears and points to therapies that reset folding rather than only adjusting signals. (ch36 sec: A “Frozen” Chromatin State as Trapped Immune-Activation Memory; evidence Ward 2025, cert 0.30; origin: brainstorm.)	Phase 5 / 0.30
lim episwitch-caveats	Critical caveats limiting the EpiSwitch diagnostic claim: (1) generic-illness confound — severe/housebound ME/CFS differs from healthy controls on deconditioning, stress, polypharmacy, sleep; the signature may reflect being severely ill, not ME/CFS-specific pathology; (2) overfitting risk — 200 markers fitted in n=47 with only internal validation is a textbook discovery-cohort overfitting hazard; (3) vendor COI — every EpiSwitch clinical paper is Oxford BioDynamics-authored, with a proprietary closed platform and no independent replication; (4) severity selection — housebound-only cohort, generalisability to mild/moderate unshown. Consequence: the 92%/98% figures should be treated as unconfirmed discovery-cohort numbers until a neutral, severity-representative external replication; the test is research-stage, not a clinical diagnostic. (ch36 sec: 3D Chromatin Architecture Is an Independent ME/CFS Biomarker Layer; origin: brainstorm critical categories.)	Phase 5 / n/a
syn episwitch-epigenetic-convergence	Synthesis: several independent epigenetic measurement layers — 3D chromosome-conformation (EpiSwitch), the IL-2/JAK-STAT pathway it reports, endogenous-retrovirus expression, and conventional DNA-methylation and miRNA markers — converge on a common theme of immune-regulatory dysregulation. The IL-2/JAK-STAT axis is the shared hub, and a single chromatin-maintenance mechanism (TRIM28/KAP1) plausibly underlies both the 3D-genomic and HERV signatures. This convergence strengthens a mechanistic hypothesis about shared immune dysregulation but not a validated clinical diagnostic — the EpiSwitch specificity is against healthy controls only and cross-condition testing (fibromyalgia, depression, long-COVID) is untested. Consequence: multiple independent epigenetic fingerprints appear to point at one underlying immune-regulatory defect, which is encouraging for understanding the disease but does not yet justify using any of them as a clinical test. (ch36 sec: Convergent Epigenetic Layers Point Toward a Shared Immune-Regulatory Defect; origin: Phase 10a synthesis.)	Phase 10a / n/a

ID / Label	Details	Phase / Cert
spec irisin-signaling-resistance	Hypothesis: circulating thrombospondin-1 (TSP-1) antagonises irisin signaling through the HSP90_$\alpha$/αvβ5 integrin axis, producing “irisin signaling resistance” that contributes to the impaired metabolic adaptation and PEM of ME/CFS. A 2026 cross-sectional cohort (92 ME/CFS, CCC) found lower baseline plasma irisin and a blunted irisin response to a 90-min mechanical stress challenge (p=0.034); functional cellular-dielectric-spectroscopy assays showed TSP-1 inhibits irisin signaling concentration-dependently and that irisin requires αvβ5 and extracellular HSP90_$\alpha$, while TSP-1 remains a dominant antagonist (Souma et al. 2026) (Boström et al. 2012) (Kim et al. 2018) A et al. (2023; N. M. Rogers et al. 2014) (D. D. Roberts, Kaur, and Isenberg 2017). Falsifiability: patient-derived PBMC or muscle cells from ME/CFS patients with elevated TSP-1 showing normal irisin-stimulated signalling, or TSP-1 reduction restoring no metabolic phenotype, refute the mechanism. Consequence: if confirmed it reframes PEM as a failure of a specific exercise-induced signalling pathway, offering a TSP-1/irisin biomarker and testable targets (TSP-1 reduction, NO restoration, HSP90_$\alpha$ stabilization) — all hypothesis-stage with no human dosing/safety data. (ch07 sec-15: Irisin Signaling Resistance via TSP-1 as a Mechanism of Impaired Metabolic Adaptation in PEM; evidence Souma 2026 cert 0.45 discounted (raw 0.55, single cohort); severity mild+moderate-severe; translation gap in vitro→human; origin: literature integration.)	Phase 3 / 0.45
oq irisin-tsp1-severity-paradox	Open question: baseline irisin independently predicts fatigue severity (β=0.67, p=0.021) and moderate-to-severe patients show elevated both irisin and TSP-1 — a paradox (lower group-level irisin yet higher in the most severe). Competing models: (a) compensatory-but-ineffective response overridden by TSP-1, vs (b) a distinct severity-associated regulatory programme, possibly sex-specific (irisin-PEM correlation held in males R=0.53, p=0.01, not females) (Souma et al. 2026). Falsifiability: a longitudinal study across PEM episodes discriminates: compensation predicts irisin+TSP-1 rise together before recovery; distinct-biology predicts a stable severity set-point not tracking acute PEM. Consequence: resolving this determines whether TSP-1-suppressing or irisin-restoring therapies could help or chase a compensatory signal — currently unknown, requires longitudinal biomarker study. (ch07 sec-15: The Irisin–TSP-1 Severity Paradox: Compensation or Distinct Biology?; evidence Souma 2026; cert 0.30; severity mild+moderate-severe; cohort overlap Moreau-group biobank; origin: literature integration.)	Phase 3 / 0.30
spec tsp1-vascular-metabolic-convergence	Hypothesis: TSP-1 is a vascular↔︎metabolic convergence node — the same molecule already documented as an endothelial-activation marker in ME/CFS (Heng 2025 panel; broader secretome vasculature dysregulation in Hoel 2026 proteomics) may also be the dominant antagonist of irisin signaling at the HSP90_$\alpha$/αvβ5 axis, thereby simultaneously driving vascular dysfunction and impaired metabolic adaptation via CD47-mediated NO suppression and irisin-axis antagonism (Heng et al. 2025) (Hoel et al. 2026) (Souma et al. 2026) (N. M. Rogers et al. 2014) (D. D. Roberts, Kaur, and Isenberg 2017). Falsifiability: plasma TSP-1 will correlate with BOTH endothelial markers AND the blunted exertional irisin response in one cohort, with the top TSP-1 quartile showing the worst perfusion AND metabolic adaptation; falsified if TSP-1 elevation segregates with vascular but not metabolic dysfunction. Consequence: if one molecule underlies both poor circulation and post-exertional crash, it becomes a far more attractive single drug target; currently a unifying hypothesis resting on two separate single-source observations. (ch07 sec-15: TSP-1 as a Vascular–Metabolic Convergence Node; evidence Heng/Hoel/Souma, cert 0.40; severity mild+moderate-severe; translation gap in vitro→human; origin: brainstorm.)	Phase 5 / 0.40

ID / Label	Details	Phase / Cert
spec theanine-caffeine-attention-differential	Speculation: in a double-blind placebo-controlled three-way crossover trial in 21 adolescents with ADHD, a high-dose L-theanine-caffeine combination and methylphenidate both reduced selective-attention false alarms (P=0.038, P=0.035) and increased P3b amplitude while decreasing P3b latency (P $< 0.05$), but ONLY methylphenidate improved behavioral reaction time (43.89 ms, P=0.018) (Nawarathna et al. 2026). The pattern is NOT a clean selection-vs-speed dissociation: P3b latency is itself a processing-speed index, and the non-dopaminergic combination improved it, so the divergence is specifically between two speed measures (neural P3b latency improved in both; behavioral RT improved only with methylphenidate). Route-separability is further undercut by convergent FPN-DMN effects of both caffeine and methylphenidate (M. Becker et al. 2022) and a non-dopaminergic theanine choice-RT effect in healthy adults (Gerolymos et al. 2026); the finding rests on a single small trial from one research group (n=21, acute single-dose, ADHD cohort, no ME/CFS data, not independently replicated) (Kahathuduwa et al. 2020). Related speculation: the same caffeine-adenosine axis underlies the proposed A2A-density caffeine paradox in ME/CFS Elevated A2A Receptor Density Underlies ME/CFS Caffeine Paradox. Falsifiability: an adequately-powered ADHD crossover (n≈60+) showing theanine-caffeine also shortens behavioral RT would confirm the RT null was underpowering; if it replicates RT-null with improved P3b latency, it supports a motor/premotor vs perceptual speed dissociation; three nulls bound the claim — L-theanine null for fatigue (Gerolymos et al. 2026), caffeine no ME/CFS association (Palacios et al. 2023), caffeine monotherapy null (Al Shahab et al. 2025) — so the adenosine route’s causal role in ME/CFS is currently unsupported and probably null. Consequence: cross-disease mechanism context only — NOT an ME/CFS treatment recommendation; caffeine carries withdrawal-fatigue/tolerance harm (Carbone et al. 2025) and the entry only informs the PFC catecholamine-inverted-U model (ch07 sec-28: Catecholamine vs Non-Dopaminergic Mechanisms: Attention Selection vs Processing Speed; raw cert 0.55, ADHD disease-model → discounted 0.41; severity unknown; origin: literature integration).	Phase 3 / 0.41
oq slow-but-accurate-mecfs-signature	Open question: which cognitive pattern does ME/CFS show on a selective-attention task — “slow-but-accurate” (preserved error rate, selectively slowed reaction time, if the non-dopaminergic selection route is spared while the dopaminergic speed route fails) OR “slow-and-inaccurate” (increased false alarms from ATP-expensive inhibitory networks failing first, per the PFC-energy framework) (Nawarathna et al. 2026). The two readings point opposite ways and both are testable; the question is unresolved. Falsifiability: an objective selective-attention task in ME/CFS (vs matched healthy and depressed controls) showing proportionally impaired errors+reaction time, or normal reaction time, falsifies the slow-but-accurate reading; preserved accuracy with selective speed loss supports it; an accuracy deficit supports the impulse-inhibition-first reading — the three outcomes are mutually exclusive and testable. Consequence: whichever pattern replicates, it provides an objective cognitive-behavioral readout of PFC energy/catecholamine state cheaper than imaging/EEG, and sharpens the ME/CFS brain-fog vs uniformly-slowed primary-depression distinction — both candidate phenotypes currently predictions from one ADHD trial, not established findings (ch07 sec-28: Does ME/CFS Show Preserved Accuracy With Selective Speed Loss, or a General Accuracy Deficit?; cert 0.20; cross-disease extrapolation; severity unknown; origin: brainstorm).	Phase 5 / 0.20

ID / Label	Details	Phase / Cert
oq neurogenic-vs-muscular-cramp	Open question: whether spontaneous, exertion-independent cramps in ME/CFS are fully explained by the muscular (sarcolemmal Na⁺/K⁺-ATPase depolarization) mechanism — the prior-favored account, being the only published ME/CFS-specific explanation and consistent with documented muscle sodium overload and subsarcolemmal mitochondrial pathology ((Wirth and Steinacker 2025) (Petter et al. 2022) (Bizjak et al. 2024)) — or whether a small neurogenic (motor-nerve-terminal hyperexcitability; HCN channel, spinal reflex — (Czesnik et al. 2015) (Maughan and Shirreffs 2019)) contribution exists in addition. The neurogenic mechanism is the general-population default for cramps but has no direct ME/CFS-specific support. Falsifiability: EMG/nerve-conduction showing ectopic discharges or raised HCN inward rectification would establish a neurogenic route; normal nerve function with sarcolemmal abnormality would support the muscular mechanism being complete; no ME/CFS study has yet performed this discrimination. Consequence: the muscular mechanism should be treated as the working default and no current treatment change is indicated; whether a neurogenic contribution exists is an unanswered, testable question. (ch04 sec-03: Neurogenic vs Muscular Origin of Spontaneous Cramps; evidence Czesnik 2015 0.70/Wirth 2025 0.35; severity unknown; origin: literature integration.)	Phase 3 / n/a
spec motor-nerve-hyperexcitability-cramp	Hypothesis: ME/CFS spontaneous cramps partly reflect motor-nerve-terminal hyperexcitability (ion-channel driven: HCN) rather than only the energy-deficit model; small-fiber neuropathy present in ME/CFS could excite intramuscular nerves. Falsifiability: threshold-tracking axonal excitability showing elevated HCN-driven inward rectification in cramp ME/CFS patients vs cramp-free controls; falsified if axonal excitability is normal or purely sarcolemmal. Consequence: if replicated, cramps could become a treatable nerve-excitability problem — but the mechanism has no direct ME/CFS-specific support and should not change practice. (ch04 sec-03: Motor-Nerve Hyperexcitability as a Contributor to ME/CFS Spontaneous Cramps; evidence Czesnik 2015 cert 0.70, transferability-discounted to 0.30; severity unknown; origin: brainstorm.)	Phase 3 / 0.30
spec single-pump-failure-cramp-unification	Hypothesis: the Na⁺/K⁺-ATPase maintains resting membrane potential in both muscle fibres and motor-nerve terminals, so the muscular and neurogenic cramp mechanisms may be ONE pump failure expressed in two compartments, dissolving the dichotomy. Falsifiability: paired sarcolemmal + motor-axonal excitability in the same limb showing correlated abnormalities; falsified if one is normal while the other is abnormal. Consequence: if true, clinicians need not choose between two rival explanations — a single energy/ion defect causes both. (ch04 sec-03: Single Pump Failure as a Unifying Explanation for Both Cramp Mechanisms; cert 0.15; severity unknown; origin: brainstorm.)	Phase 5 / 0.15

ID / Label	Details	Phase / Cert
spec metabolic-circadian-entrainment	Speculation: the metabolic hormone leptin can influence the speed of circadian re-entrainment to an external schedule, complementing light and melatonin as circadian levers. Molecular substrate: DMH LepR neurons project to the SCN and shift clock phase ((Q. Tang et al. 2023) (Faber et al. 2021)); the clock reciprocally gates leptin signalling ((Osorio-Mendoza et al. 2025)). An encapsulated-cell therapy delivering constitutively-produced (non-phasic) leptin accelerated re-entrainment to phase advances and delays in mice and cynomolgus macaques without impairing sleep ((Fleury et al. 2026)). Falsifiability: selective ablation of DMH LepR neurons or SCN LepR innervation abolishes the entrainment-accelerating effect of systemic leptin elevation in an animal model; in humans, a timed metabolic challenge against a light pulse measurably alters phase-shift magnitude. Consequence: if the coupling holds in humans, it expands the toolbox for re-stabilising ME/CFS sleep-wake timing beyond light and melatonin — but it is a single unreplicated preclinical lead, the lever is non-phasic, and it is untested in the ME/CFS circadian system. (ch10 sec-06: Metabolic-Circadian Entrainment Coupling: Leptin as a Modulator of Phase Re-alignment; evidence animal-model, raw cert 0.55–0.75 discounted to 0.28–0.38, set at 0.30; translation gap animal→human; severity unknown; origin: literature integration.)	Phase 3 / 0.30
oq leptin-therapeutic-direction	Open question: whether raising leptin could ever aid ME/CFS circadian disruption, or whether that direction is wrong. ME/CFS-specific data caution against it: circulating leptin is elevated in ME/CFS ((Domingo et al. 2024)) or unremarkable ((Anthony J. Cleare, O’Keane, and Miell 2001)), light rather than metabolic signalling is the dominant circadian lever ((Cambras et al. 2026)), and even direct circadian interventions (melatonin, phototherapy) produced no CFS symptom benefit ((Williams et al. 2002)). Consequence: treat the metabolic-circadian coupling as a research/diagnostic direction, not a treatment target — the existing data point toward light and scheduling, and away from elevating an already-elevated or normal hormone. (ch10 sec-06: Could Raising Leptin Ever Help ME/CFS Circadian Disruption—or Would It Be the Wrong Direction?; severity unknown; origin: literature integration.)	Phase 3 / n/a
oq leptin-timing-amplitude	Open question: whether ME/CFS leptin is altered in circadian timing/amplitude rather than amount — a reconciliation of the Cleare ((Anthony J. Cleare, O’Keane, and Miell 2001)) and Domingo ((Domingo et al. 2024)) readings, which point numerically the same way (higher in CFS) and are single-timepoint snapshots of a circadian hormone. A 24-hour serial profile (phase-referenced to DLMO) would reveal phase-shift/flattening even where spot means are indistinguishable. Falsifiability: serial profiling shows a normal rhythm within a pre-specified effect-size margin (amplitude ratio ≥1.5-fold or phase offset ≥2 h) — falsifies timing hypothesis; confirms flattening/phase-shift beyond the threshold — supports it. Consequence: if confirmed, the relevant ME/CFS variable may be when-not-how-much, giving a feasible measurement protocol — but it is a research hypothesis with no current clinical application. (ch10 sec-06: Is ME/CFS Leptin Altered in Timing Rather Than Amount—the Cleare-vs-Domingo Reconciliation?; severity unknown; origin: brainstorm.)	Phase 5 / 0.15

ID / Label	Details	Phase / Cert
oq t1w-t2w-myelin-substrate	Open question: whether the elevated T1w/T2w myelin-signal seen in long COVID and recovered-COVID brains reflects remyelination, neuroinflammatory gliosis, or iron/water content changes. A multimodal MRI study (n=47: 19 long COVID, 12 COVID-recovered, 16 never-infected) found altered T1w/T2w signal across all three group comparisons (long COVID > controls in precentral/middle temporal gyrus; recovered > long COVID in pons/midbrain/cerebellar tonsil/SLF; recovered > controls in precentral/posterior cingulate), correlating with physical (r=0.56) and cognitive (r=-0.64) function (Thapaliya et al. 2025); the authors read it as possible remyelination but concede it “may also indicate inflammation or gliosis.” Falsifiability: a study combining T1w/T2w with myelin-water-fraction or quantitative-susceptibility mapping in the same cohort resolves whether the signal tracks myelin, gliosis, or iron. Consequence: whether the scan reflects healing (remyelination) or ongoing damage (inflammation/gliosis) changes what the finding means clinically — but the substrate is currently unresolved. (ch09 sec-01: Elevated T1w/T2w Myelin Signal: Remyelination, Gliosis, or Iron?; evidence Thapaliya 2025, cert n/a; severity unknown; origin: literature integration.)	Phase 3 / n/a
spec multimodal-mri-biomarker	Hypothesis: a multimodal MRI panel (T1w/T2w myelin-signal + DTI microstructure + MRS neurochemistry) is a candidate research-stage biomarker for post-infectious brain involvement, discriminating long COVID / recovered COVID / never-infected and correlating with physical and cognitive function (Thapaliya et al. 2025), supported by independent-lab microstructural findings in ME/CFS and post-COVID (cingulum FA at 85% diagnostic accuracy (K. Wu et al. 2026); diffusion-NII edema/infiltration in ME/CFS (Yu2026diffusion?)-neuroinflammation; post-COVID DWI review (Jahanshahi et al. 2026)). Falsifiability: multi-site harmonised replication failing to reproduce a stable multimodal signature, or the signal tracking non-specific factors (age, deconditioning, mood) as strongly as post-infectious status, falsifies the claim. Consequence: if validated, it gives an objective non-invasive way to detect and track brain involvement — but it is a research tool, not a clinical test today. (ch36 sec: Multimodal MRI Panel (T1w/T2w + DTI + MRS) as a Candidate Post-Infectious Biomarker; evidence Thapaliya 2025 0.47 discounted→0.40 + Wu 0.70 + Yu 0.65; severity unknown; origin: literature integration.)	Phase 3 / 0.40
spec postinfectious-bidirectional-microstructure	Hypothesis: two cross-disease observations from post-infectious neuroimaging cohere — (1) a shared brainstem–cerebellar–thalamic–cingulate limbic-cortical vulnerability circuit (recurring regions: pons, midbrain, cerebellar tonsil, SLF, cingulum (Thapaliya et al. 2025) (Thapaliya et al. 2021) (K. Wu et al. 2026)), and (2) a bidirectional diffusion-microstructure signature (elevated FA/restricted diffusion = remyelination/repair; reduced FA/elevated diffusivity = demyelination/edema/injury) that reframes the “inconsistent findings” complaint as a predictable damage-to-repair continuum (Maksoud et al. 2020) (Yu2026diffusion?)-neuroinflammation (Lu et al. 2020). Falsifiability: circuit-specificity fails if alteration is no stronger in this circuit than control tracts; bidirectional synthesis fails if diffusion direction is random with respect to recovery state. Consequence: focuses imaging on a shared circuit and on measuring the damage-vs-repair balance rather than arguing over direction — a research-direction change, not clinical. (ch14d: Shared Brainstem-Cerebellar-Limbic Vulnerability and the Bidirectional Microstructure Signature in Post-Infectious Fatigue; cert 0.28; severity unknown; origin: brainstorm.)	Phase 5 / 0.28

ID / Label	Details	Phase / Cert
oq ldn-fm-pain-meta-divergence	Open question: whether low-dose naltrexone improves pain in fibromyalgia, given divergent meta-analyses — a 2025 meta-analysis found no between-group superiority vs placebo ((Ologunowa et al. 2025)) while a 2024 meta-analysis reported significant pain reduction (MD −0.86) and higher pressure pain threshold ((Vatvani et al. 2024)); the discrepancy reflects differing trial sets and methods, the positive analysis carries an erratum, and the certainty difference (0.56 vs 0.52 discounted) is within measurement uncertainty so neither is weighted. Secondary-outcome 30%-responder analysis of the FINAL trial was null on all six non-pain outcomes (Nielsen, Vaegter, and Due Bruun 2026), leaning toward the null meta-analysis, but the pain-specific question remains open. Falsifiability: a pre-registered pooled responder analysis of LDN in fibromyalgia demonstrating a significant ≥30% pain-responder advantage over placebo would resolve the conflict toward the positive meta-analysis; conversely, a pooled responder analysis finding no ≥30% advantage (or equivalence) would resolve it toward the null meta-analysis. Consequence: clinicians cannot rely on a consistent evidence verdict for LDN in fibromyalgia pain, so expectations should be modest and uncertain. (ch31: Does Low-Dose Naltrexone Improve Pain in Fibromyalgia? Divergent Meta-Analyses; severity unknown; origin: literature integration.)	Phase 3 / n/a
spec ldn-tlr4-occupancy-gap	Speculation: the null fibromyalgia LDN responder results may be mechanistically expected because clinical-dose LDN never reaches meaningful TLR4 occupancy. (+)-naltrexone is a weak TLR4 antagonist; the optimized derivative CIAC101 required ~6200× potency gain to reach nanomolar TLR4 antagonism ((Jingwei Gao et al. 2025)). Clinical LDN is racemic at 4.5–6 mg, so the TLR4-blocking species may need concentrations orders of magnitude above achieved levels — a dose-to-target translation failure, meaning the TLR4 rationale was never actually tested at clinical dose. Consistent with the alternative OGFr/enkephalin-rebound mechanism ((Zagon and McLaughlin 2018)) that may require longer duration or a fatigue-specific subtype (Nielsen, Vaegter, and Due Bruun 2026). Falsifiability: a dose-occupancy study showing clinical-dose LDN achieves below 10% TLR4 occupancy (no downstream NF-κB suppression) in patient PBMCs supports the below-threshold reading; measurable TLR4 antagonism at these doses falsifies it. Consequence: the FM null does not disprove the TLR4/microglial rationale — it may mean the dose never reached the target, shifting research from “does the mechanism work” to “was it ever tested at an engaging dose.” (ch31: Low-Dose Naltrexone: The TLR4 Mechanism May Never Be Engaged at Clinical Dose; cert 0.30; severity unknown; origin: brainstorm.)	Phase 5 / 0.30

ID / Label	Details	Phase / Cert
cf gastroparesis-delayed-emptying-mecfs	Clinical finding: delayed gastric emptying is a documented, measurable feature of chronic fatigue syndrome — a controlled radionuclide study found delayed liquid emptying in 23/32 (72%) and delayed solid emptying in 12/32 (38%) with the delay significantly correlated with symptom severity (p $<$ 0.001) (Burnet and Chatterton 2004). Direct ME/CFS evidence (raw certainty 0.60, no population discount); not yet replicated in a modern-IOM/ICC-criteria cohort. Falsifiability: a modern-criteria ME/CFS cohort showing no difference in scintigraphic gastric emptying versus matched controls would weaken this finding; replication of the delay would confirm it. Consequence: stomach emptying delay is a real, testable problem in ME/CFS, so it can be assessed with a standard gastric-emptying test rather than dismissed as a vague symptom. (ch04: Delayed Gastric Emptying Is Documented in a ME/CFS Cohort, Correlating With Symptom Severity; cert 0.60; severity not stratified; origin: literature integration.)	Phase 3 / 0.60
oq gastroparesis-enteric-icc-loss-mecfs	Open question: whether ME/CFS gastroparesis shares the structural enteric-neuron and interstitial-cell-of-Cajal (pacemaker-cell) loss documented in diabetic and idiopathic gastroparesis. Human antral tissue shows ICC and enteric-nerve loss in diabetic/idiopathic gastroparesis (Grover et al. 2011), and an animal model confirms cell/nerve loss accompanies slowed emptying (X.-Y. Wang et al. 2009); no ME/CFS gastric histology exists, so the ME/CFS form could be functional vagal failure, autoimmune ganglionic blockade, or structural degeneration. Falsifiability: normal ICC and enteric-nerve density in ME/CFS gastric specimens with documented gastroparesis would falsify the structural-shared-pathology reading; evidence of cell/nerve loss would support it. Consequence: if ME/CFS gastroparesis involves structural pacemaker-cell loss, prokinetics that stimulate surviving nerves would have limits and treatment would need to target cell-loss prevention or replacement. (ch04: Does ME/CFS Gastroparesis Share the Enteric-Nerve and Pacemaker-Cell Loss Seen in Diabetic and Idiopathic Gastroparesis?; cert 0.40; severity unknown; origin: literature integration.)	Phase 3 / 0.40
spec gastroparesis-autoimmune-ganglionic-blockade	Speculation: a subset of ME/CFS gastroparesis could reflect autoimmune ganglionic (α3 nicotinic acetylcholine-receptor) blockade — modeled on a post-SARS-CoV-2 autoimmune GI dysmotility case that responded to immunotherapy (Montalvo et al. 2022) — a potentially reversible mechanism distinct from structural nerve loss. Speculative for ME/CFS: no ME/CFS cohort has been screened for ganglionic AChR antibodies; the autoimmune GI dysmotility literature in ME/CFS is a single post-viral case. Falsifiability: measuring ganglionic AChR antibodies in ME/CFS patients with documented gastroparesis — a meaningful antibody-positive subset whose emptying improves with immunotherapy would support the claim; titers that do not track emptying delay or immunotherapy response would falsify it. Consequence: a reversible autoimmune subset of ME/CFS gastroparesis would have a real immunotherapy treatment path, testable with an existing blood antibody panel. (ch04: Autoimmune Ganglionic Acetylcholine-Receptor Blockade Could Be a Reversible Cause of Some ME/CFS Gastroparesis; cert 0.30; severity unknown; origin: brainstorm.)	Phase 5 / 0.30
lim gastroparesis-evidence-anchor-and-alternatives	Limitation: the direct ME/CFS gastroparesis evidence rests on a single 2004 cohort (n=32) predating modern diagnostic criteria (Burnet and Chatterton 2004) with one n=1 null (Corrado et al. 1998) and no modern-IOM/ICC replication; non-specific alternatives (medication side-effects, functional dyspepsia/visceral hypersensitivity (Debourdeau et al. 2024), deconditioning, reporting bias) could produce the same pattern without a distinct ME/CFS lesion, and cross-disease histology (Grover et al. 2011) (X.-Y. Wang et al. 2009) is population-discounted. Consequence: clinicians should exclude the common non-specific causes (drug effects, dyspepsia) with objective testing before attributing symptoms to a ME/CFS-specific emptying defect. (ch04: Gastroparesis Evidence in ME/CFS Is Anchored on One Old, Unreplicated Cohort — and Non-Specific Alternative Explanations Exist; severity across all; origin: literature integration.)	Phase 5 / n/a

ID / Label	Details	Phase / Cert
oq adult-onset-adhd-mix-status	Open question: whether the “adult-onset ADHD is a distinct syndrome” reading (from the Dunedin birth cohort (Moffitt et al. 2015)) still holds. Evidence to date favors a mix, but the question remains open: repeated comprehensive assessments of an MTA comparison cohort (n=239, ages 10 to 25) excluded roughly 95% of those who screened positive on careful assessment, with the remainder adolescent-onset or explained by substance use or comorbidity (Sibley et al. 2018); a dedicated review concluded most late-onset cases develop between ages 12–16, rarely without childhood precursors, while cautioning the data cannot yet clarify whether early and late onset share underlying neural mechanisms (Asherson and Agnew-Blais 2019); and retrospective recall of childhood ADHD symptoms is unreliable (accuracy about 55%) (Breda et al. 2020). The strongest reading is that much apparent adult onset is adolescent-onset or missed childhood ADHD, so the acquired-catecholamine interpretation applies, if at all, to a narrow residual subset — but the evidence base is a small US comparison cohort plus a self-referential research cluster, not settled consensus, so the question is open. Falsifiability: a prospective cohort with repeated childhood assessments that confirmed a substantial de novo (no childhood precursors, no substance use) adult-onset ADHD group would partially revive the distinct-syndrome reading; current MTA-derived evidence weighs against it but does not settle it. Consequence: for the ADHD–ME/CFS framing, ADHD is best treated as a largely childhood/adolescent-onset predisposing trait; the shared-metabolic-reserve hypothesis applies most strongly to childhood/adolescent-onset ADHD. (ch14d Architectural Uncertainty: Architecture A Cannot Be Ruled Out; cert 0.47 — discounted from Sibley2018 0.65, Asherson2019 0.62, Breda2020 0.58 each ×0.75 cross-disease weight; severity unknown; origin: literature integration.)	Phase 2 / 0.47
hyp adhd-nrf2-pathway-measured	Hypothesis (updated claim): NRF2 pathway activity in ADHD has now been measured once — a 2026 cross-sectional study of 60 adults with ADHD versus 60 controls found serum NRF2 and HO-1 protein significantly reduced in ADHD and negatively correlated with symptom severity (all p $<$ 0.001; ROC AUC 0.756 for NRF2, 0.797 for HO-1) (Gürbüzer, Ozkaya, and Mercantepe 2026). This replaces the earlier “no study has measured NRF2 in ADHD” claim and is consistent with — but does not prove — the dopamine-NRF2-NLRP3 axis: it measured circulating serum protein, not NRF2 nuclear translocation or NQO1 target-gene expression, and covers adult ADHD only in a single unreplicated study. Falsifiability: an ADHD PBMC study showing no difference in NRF2 nuclear translocation or NQO1 expression between ADHD and controls would weaken the axis claim; replication of reduced NRF2 in a second independent ADHD cohort would strengthen it. Consequence: a measurable (if preliminary) NRF2 deficit in ADHD adds plausibility to the dopamine-NRF2-NLRP3 axis, and suggests serum NRF2/HO-1 as a candidate exploratory biomarker — but because the measurement was serum protein, not NRF2 nuclear translocation or NQO1 expression, it does not yet establish which NRF2-dependent endpoint an intervention should target. (ch14d sec:x1-adhd-nrf2-nlrp3: ADHD Dopamine-NRF2-NLRP3 Axis as Shared Pathophysiology with ME/CFS; cert 0.41; adult-only, severity unknown; origin: literature integration.)	Phase 3 / 0.41

ID / Label	Details	Phase / Cert
oq lc-adhd-acquired-causal	Open question: whether SARS-CoV-2 infection acquires new-onset AD(H)D or merely reveals preexisting neurodivergence. A translational viewpoint proposes a bidirectional Long Covid–AD(H)D link via convergent neuroimmune dysfunction (frontal-striatal-hippocampal circuits, catecholamine neuroimmune dysregulation, tryptophan-kynurenine overactivation, mitochondrial bioenergetic defects) and an acquired, infection-triggered neuroimmune-mediated dopaminergic/noradrenergic dysfunction (Spanoghe et al. 2026). The causal claim is unresolved: post-COVID ADHD symptom onset is reported and stimulant prescribing rose post-COVID (Koonce and Martin 2024) (Gimbach et al. 2024), with AD(H)D as a risk factor for Long Covid (Merzon et al. 2022); but a 20-year national cohort found no independent COVID effect on AD(H)D diagnosis/treatment rates (Shkalim Zemer et al. 2024), and the prescribing rise is consistent with catch-up diagnosis (Gimbach et al. 2024). Because the certainty gap between the strongest null (0.70 raw) and the positive epidemiology is ≤0.15, both sides are presented without weighting. Falsifiability: a prospective post-infectious cohort with validated AD(H)D scales plus an executive/immune-biomarker profile at enrollment and longitudinally would distinguish de novo onset from unmasking of preexisting traits; reliance on retrospective diagnosis or prescription records cannot settle it. Consequence: determines whether post-viral attention difficulties are interpreted as newly acquired brain dysfunction requiring investigation or as unmasked preexisting neurodivergence — changing clinical interpretation and management while not altering the need to address the symptoms. (ch14d Shared Neuroimmune Substrate Linking Long Covid, AD(H)D, and ME/CFS Executive Dysfunction; cert 0.40; severity unknown; origin: literature integration.)	Phase 2 / 0.40
hyp lc-adhd-neuroimmune-shared-substrate	Hypothesis: a shared neuroimmune substrate links Long Covid, AD(H)D, and ME/CFS executive dysfunction — specifically kynurenine-pathway overactivation disrupting dopamine/noradrenaline transmission and intersecting mitochondrial bioenergetics (Spanoghe et al. 2026). Direct human evidence: kynurenine-pathway activation correlates with post-acute COVID cognitive impairment (Cysique et al. 2023); kynurenic acid lowers striatal dopamine at nanomolar concentrations in animals (Rassoulpour et al. 2005); kynurenine intersects mitochondrial function (M. Tanaka et al. 2022) and mitochondrial complex suppression accompanies COVID cognitive decline (Xu et al. 2025); dopamine is immunomodulatory (Sarkar et al. 2010) (Y. Feng and Lu 2021). Precision-immunopsychiatry (immune/neurocognitive endophenotype stratification) and off-label AD(H)D-targeted pharmacotherapy in Long Covid are proposed, with autonomic dysregulation and PEM as limiting factors (Krishnan et al. 2022) (Fesharaki-Zadeh, Lowe, and Arnsten 2023) (Spanoghe et al. 2026). Falsifiability: in ME/CFS patients with executive dysfunction, plasma KYN/TRP ratio will correlate positively with objective executive deficit and negatively with a dopaminergic-function proxy, persisting after controlling for fatigue severity; falsified if KYN/TRP does not track executive dysfunction independent of overall fatigue. Consequence: provides a mechanism-specific target (kynurenine-to-dopamine signaling) and a caution that AD(H)D-type medication for cognitive symptoms is observational only, not a validated ME/CFS treatment. (ch14d Shared Neuroimmune Substrate Linking Long Covid, AD(H)D, and ME/CFS Executive Dysfunction; cert 0.35; severity unknown; origin: literature integration.)	Phase 2 / 0.35
hyp mito-kynurenine-nad-sink	Hypothesis: the kynurenine and mitochondrial arms of the LC-AD(H)D framework converge on a single prefrontal bioenergetic bottleneck — chronic kynurenine-pathway activation draws down the NAD⁺ pool that mitochondria require, so the energetically expensive prefrontal cortex shows the first ATP deficit, producing the shared executive/inattentive phenotype. Kynurenine-pathway overactivation diverts tryptophan toward the kynurenine-NAD⁺ catabolic arm (Cysique et al. 2023); mitochondrial complex suppression accompanies COVID cognitive decline (Xu et al. 2025); the kynurenine pathway intersects mitochondrial function (M. Tanaka et al. 2022). Unifies mechanisms (3) and (4) of the Spanoghe framework into one molecular mechanism (Spanoghe et al. 2026). Falsifiability: in ME/CFS patients with executive dysfunction, prefrontal ATP/phosphocreatine (MRS) will correlate inversely with kynurenine-pathway load and executive-deficit severity, independent of general fatigue; falsified if prefrontal bioenergetic markers do not track kynurenine-pathway load after controlling for fatigue. Consequence: gives a single druggable target (kynurenine-to-NAD⁺ balance) behind the separate kynurenine and mitochondrial hypotheses, and suggests shifting the pathway away from the NAD⁺-consuming arm might preserve prefrontal energetics. (ch14d Mitochondrial-Kynurenine NAD⁺ Sink: A Unifying Executive-Dysfunction Mechanism; cert 0.40; severity unknown; origin: brainstorm idea 1.4.)	Phase 2 / 0.40

ID / Label	Details	Phase / Cert
oq depression-neurogenesis-stall-mechanism	Open question: whether adult human hippocampal neurogenesis stalls in major depressive disorder. A single-cell + spatial + proteomic atlas of ~500,000 hippocampal nuclei reported a stalled neurogenic lineage in MDD (more quiescent neural stem cells, fewer neuroblasts, reduced DCX/BDNF, elevated interferon and stress signaling) and argued for molecular reclassification of depression (Peng et al. 2026). The premise that adult human hippocampal neurogenesis persists at a measureable level is directly contested: one group reports persistence into the ninth decade (Boldrini et al. 2018) (Boldrini et al. 2019), building on the foundational BrdU demonstration (Eriksson et al. 1998); the opposing group concludes very few, if any, new neurons are born in the adult human hippocampus (Sorrells et al. 2018) (Sorrells et al. 2021). The certainty gap between the strongest positive (0.80) and strongest null (0.68) is ~0.12, within the ±0.10 band — both sides are presented without weighting. Falsifiability: a pre-registered multi-method (single-nucleus RNA + multiple antibody markers) post-mortem study in independent cohorts that either reproduces the MDD neurogenesis stall or demonstrates undetectable neurogenesis at any adult age would settle the controversy; agreement between methods is required to exclude fixation/antibody artifacts. Consequence: determines whether stalled hippocampal neurogenesis can serve as a molecular feature of primary depression at all — if the underlying process is not reproducibly measureable in adults, it cannot be used to distinguish depression from ME/CFS or to guide any intervention. (ch04 Is Molecular Depression Distinct From Reactive Depression in ME/CFS?; ch16 Molecular Reclassification of Depression and the Depression-ME/CFS Boundary; cert 0.40 — controversy unresolved, both sides discounted; severity unknown; origin: literature integration.)	Phase 2 / 0.40
spec postinfectious-neurogenesis-suppression-mecfs	Speculation: a post-infectious or HPA-axis process could suppress adult hippocampal neurogenesis in ME/CFS, paralleling the depression-axis finding. Mechanism bridges are indirect: (a) HPA-axis dysfunction is documented in CFS (Papadopoulos and Cleare 2012) and adult hippocampal neurogenesis buffers stress/HPA negative feedback in animal models (Snyder et al. 2011); (b) mild SARS-CoV-2 infection suppresses hippocampal neurogenesis and oligodendrogenesis via microglial reactivity and elevated CCL11, linked to cognitive symptoms in long COVID (Fernández-Castañeda et al. 2022). There are zero direct studies of adult hippocampal neurogenesis in ME/CFS — the extrapolation is unsupported by direct data and additionally contingent on the unresolved adult-human-neurogenesis controversy (Sorrells et al. 2018) (Sorrells et al. 2021). Falsifiability: direct measurement of dentate-gyrus neurogenic markers (DCX, nestin, Ki67) in ME/CFS post-mortem or surgical hippocampal tissue showing density comparable to controls would falsify suppression; a replicated reduction below matched controls would support it — but no such ME/CFS tissue study exists. Consequence: if replicated, a neurogenesis deficit could link the post-infectious onset of ME/CFS to a measurable hippocampal consequence and eventually offer a target — but current evidence is too thin for any clinical or diagnostic use. (ch16 Molecular Reclassification of Depression and the Depression-ME/CFS Boundary; cert 0.30 — indirect links only, zero direct ME/CFS data; severity unknown; origin: literature integration.)	Phase 3 / 0.30

ID / Label	Details	Phase / Cert
spec angelman-ube3a-cross-disease	Speculation: Angelman syndrome (UBE3A/E6AP E3 ubiquitin-ligase loss) may model ME/CFS-relevant bioenergetic and GABAergic vulnerability. Ube3a loss impairs mitochondrial oxidative phosphorylation and lowers neuronal ATP in a mouse model (Su et al. 2011); in silico work links UBE3A-dependent mitochondrial transcripts to energy metabolism (Panov et al. 2020); AS models show elevated reactive-oxygen species (Simchi et al. 2023) and reduced tonic GABAergic (GABA-A) inhibition (Watanabe et al. 2026), alongside fragmented sleep (Qu et al. 2024) — each domain independently proposed in ME/CFS (Le et al. 2025) (Shankar et al. 2025) (L. Davis et al. 2025) (Wirth and Scheibenbogen 2026) (Liu et al. 2025). No direct AS–ME/CFS co-occurrence or mechanistic link exists (0 PubMed hits), so this is hypothesis-generating, not established shared pathophysiology, and two structural disanalogies limit extrapolation: AS is congenital/monogenic/neurodevelopmental while ME/CFS is adult-onset/polygenic, and UBE3A dosage sensitivity (loss and duplication both yield overlapping outcomes) limits specificity (Kurmashev 2026). Falsifiability: if the analogy holds, ME/CFS should show impaired mitochondrial oxidative phosphorylation/ATP and reduced tonic GABA-A inhibition consistent with the AS-like phenotype regardless of acquired-vs-genetic origin; UBE3A-pathway variant enrichment is an optional corroborating (not falsifying) test, since an acquired deficit would show pathway pathology without genetic enrichment. Where such carriers are identified, iPSC-derived neurons should recapitulate the reduced tonic inhibition and lower ATP seen in AS models, though an iPSC null is non-decisive given technical recapitulation limits. Consequence: a research hypothesis only — no current clinical or diagnostic application; if it holds, it points toward UBE3A/ubiquitin-pathway and GABAergic tonic-inhibition circuits as a shared vulnerability underlying energy and brain-fog symptoms, giving a concrete biochemical test. (ch14d Angelman Syndrome (UBE3A) as a Cross-Disease Model for ME/CFS Mitochondrial and GABAergic Vulnerability; cert 0.20 — cross-disease analogy, animal/in-silico/overview evidence discounted for non-ME/CFS population and developmental stage; severity unknown; origin: literature integration.)	Phase 5 / 0.20

ID / Label	Details	Phase / Cert
spec guanfacine-nac-bridge	Speculation: low-dose guanfacine (PFC-selective α2A-adrenoceptor agonism) plus N-acetylcysteine (Nrf2 antioxidant) as an inter-cluster bridge spanning the ch15 catecholamine cluster and Nrf2 cluster — a deliberate fixed combination, distinct from either single agent. Reported as open-label intervention for cognitive deficits in Long COVID (Fesharaki-Zadeh, Lowe, and Arnsten 2023) and TBI (Khasnavis et al. 2024) (Fesharaki-Zadeh et al. 2025). Mechanistic rationale is parallel non-overlapping targets (PFC α2A signaling + glutathione/Nrf2 redox support (Cherneva et al. 2025)); no direct α2A→Nrf2 molecular cross-talk is established (0 PubMed hits). Zero ME/CFS data; open-label case series only. Falsifiability: in ME/CFS with documented oxidative stress and PFC-dependent cognitive deficits, guanfacine+NAC improves n-back/Stroop more than either alone at 8 weeks; falsified if no added benefit over monotherapy, or if augmentation is oxidative-burden-independent, or if orthostatic intolerance exceeds guanfacine monotherapy (Okamoto et al. 2024). Consequence: hypothesis-generating — if confirmed, gives a tolerability-favorable combination targeting both the prefrontal noradrenergic deficit and oxidative stress implicated in ME/CFS brain fog; not a current treatment. (ch15 Speculation (clinical-finding:guanfacine-nac-postinfectious?); cert 0.30; severity unknown; origin: literature integration + user-directed.)	Phase 2 / 0.30

ID / Label	Details	Phase / Cert
spec saffron-serotonergic-kynurenine-modulator	Speculation: saffron bioactives (crocin, safranal, crocetin) inhibit SERT at the fluoxetine binding site (human ex vivo (Wauquier et al. 2022); animal (Mohammadi et al. 2023)) and inhibit MAO-A/B (in vitro (De Monte et al. 2014)), reduce serum kynurenine and raise melatonin (rodent (De la Fuente Muñoz et al. 2023)), downregulate neurotoxic kynurenine components (murine (Monchaux de Oliveira et al. 2026)), and shift gut-microbial tryptophan toward tryptamine and indole-acetic acid (ex vivo (Horvath et al. 2026)). Zero primary-ME/CFS trials; a null RCT on fatigue in healthy subclinical-depression adults (Amadieu et al. 2025); fatigue RCTs are only comorbid-secondary (COPD (Dastan et al. 2025); sarcoidosis (Moghimi Dehkordi et al. 2026)) or cross-disease (Parkinson’s (Hajhashemy, Bagherniya, and Sadeghi 2026)). Combined SERT + MAO inhibition carries serotonin-syndrome risk with SSRIs/SNRIs/MAOIs (Căuș, Lupoae, and Chițescu 2026), directly relevant to the SSRI-paradox (hyper-serotonergic) model in which serotonergic agents may worsen ME/CFS. High-dose saffron has uterine-stimulation risk in pregnancy (Alshdefat et al. 2026). Falsifiability: if the hyper-serotonergic model holds, saffron worsens fatigue in ME/CFS (reverse of its reported comorbid-fatigue benefit) and should be avoided with serotonergic antidepressants; falsified if saffron improves ME/CFS fatigue in a primary-ME/CFS RCT despite SERT inhibition. Consequence: do not recommend saffron for ME/CFS fatigue or mood; treat it as an unproven, potentially-worsening serotonergic agent that also touches the kynurenine and gut-tryptophan axes. (ch18 Saffron as an Unproven Serotonergic-Kynurenine Modulator; cert 0.20; severity unknown; origin: literature integration — external-contact proposal.)	Phase 2 / 0.20

ID / Label	Details	Phase / Cert
hyp ahr-gut-microbiome-axis	Hypothesis: the aryl-hydrocarbon receptor (AhR), a ligand-activated transcription factor sensing gut-microbial tryptophan and indole metabolites (indole-3-acetic acid, indole-3-propionic acid, indole-3-aldehyde, tryptamine) and kynurenine-pathway metabolites, is an altered gut-microbiome–immune axis in ME/CFS. AhR activation drives IL-22-producing ILC3 and gamma-delta T cells and reinforces the intestinal barrier while restraining Th17 inflammation (Rothhammer and Quintana 2019) (Thirugnanam et al. 2026). Direct ME/CFS evidence found altered stool AhR-agonist activity that was elevated specifically in the neurocognitive-symptom subgroup rather than uniformly (Esteban et al. 2026), with supporting comorbid IBS+CFS urinary tryptophan-metabolite data (Chojnacki et al. 2025). The mechanism extends (not replaces) the already-integrated kynurenine pathway, since kynurenine metabolites are themselves AhR ligands (Torrelli-Diljohn, Kulkarni, and Vitturi 2026). Falsifiability: ME/CFS patients with neurocognitive symptoms should show elevated stool/plasma AhR-agonist activity and altered indole metabolites vs cognitively-unaffected ME/CFS and healthy controls, correlating with cognitive performance and barrier markers; falsified if the association does not track the neurocognitive subgroup or fails to correlate with barrier/immune readouts. Consequence: if confirmed, gut-microbial tryptophan metabolism and AhR signaling become a shared upstream mechanism linking intestinal and cognitive features of ME/CFS, pointing toward dietary/microbial restoration of a neuroprotective (transient) AhR ligand balance — no intervention established for ME/CFS today. (ch12 AhR-Gut-Microbiome Axis: Tryptophan-Derived Ligands Modulate Intestinal Barrier and Immune Tone Direct Evidence: Altered Gut-Microbial AhR Agonists Track Neurocognitive Symptoms in ME/CFS AhR Evidence Is Subgroup-Specific and Lacks Immune-Readout Confirmation; cert 0.40; severity unknown — anchor not stratified; origin: literature integration — external-contact proposal, pre-identified gap.)	Phase 2 / 0.40

ID / Label	Details	Phase / Cert
hyp fibromyalgia-cns-shared-genetic-vulnerability	Hypothesis: fibromyalgia shows brain/neural-enriched genetic architecture, and its co-occurrence with ME/CFS may reflect a partially shared central genetic vulnerability. The largest fibromyalgia GWAS (Kerrebijn 2026, Nature Medicine, 2.56M individuals, 54,629 cases) identified 26 risk loci (strongest: coding variant in HTT, plus GPR52, DCC, DRD2/NCAM1, MDGA2, CELF4) and found heritability exclusively enriched in brain tissues and neural cell types, with no immune/glial enrichment — consistent with, not proof of, a central-nervous-system (nociplastic) role (Kerrebijn et al. 2026). This parallels the brain-enriched architecture independently established for ME/CFS by DecodeME (DecodeME Consortium, Ponting, et al. 2025) and the Maccallini meta-GWAS (Maccallini 2026). Confirmed shared risk regions are OLFM4 and RABGAP1L (DCC in the earlier overlap); GPR52/HTT are fibromyalgia-risk genes whose presence in ME/CFS is untested. Falsifiability: a cross-trait GWAS/PRS analysis between fibromyalgia and ME/CFS will show significant genetic correlation and shared brain-enriched heritability; fibromyalgia PRS will be elevated in ME/CFS patients with comorbid fibromyalgia; whether HTT/GPR52 appear in ME/CFS GWAS is a testable prerequisite; falsified if the two conditions show near-zero genetic correlation or divergent enrichment patterns. Consequence: if confirmed, a shared central genetic vulnerability would make central-sensitization mechanisms a research priority to test across both conditions — but the direct genetic correlation remains to be measured, so no clinical change follows from the genetics alone. (ch14 Kerrebijn 2026 Fibromyalgia GWAS: 26 Risk Loci and Brain-Enriched Heritability Fibromyalgia as CNS Disorder with Shared Genetic Vulnerability with ME/CFS; cert 0.55; severity unknown — GWAS not stratified by severity; origin: literature integration.)	Phase 2 / 0.55
hyp shared-transdiagnostic-cns-genetic-factor	Hypothesis: fibromyalgia shares substantial genetic variance with chronic pain and psychiatric/somatic traits, and ME/CFS shows overlapping genetic correlations (the transdiagnostic CNS-factor interpretation is one candidate formalization, not established). Fibromyalgia showed rg > 0.7 with low back pain, PTSD, and IBS, and pervasive positive genetic correlation with depression, somatoform/dissociative disorders, and migraine (Kerrebijn et al. 2026); GenomicSEM analyses report a shared nociplastic-pain heritable factor (Johnston, Signer, and Huckins 2025) and a shared latent genetic liability across fibromyalgia-psychiatric traits (Lin et al. 2026). ME/CFS already shows high genetic correlation with IBS (rg 0.75) and depression (rg 0.60) (DecodeME Consortium, Ponting, et al. 2025). Falsifiability: a transdiagnostic genomic-SEM factor across these conditions will show significant heritability and shared neural cell-type enrichment, and nociplastic-pain PRS will predict severity across ME/CFS, fibromyalgia, and IBS cohorts; falsified if the shared factor explains negligible variance, if enrichment is condition-specific, or if correlations are attributable to population structure. Consequence: high pairwise genetic correlation across fibromyalgia, ME/CFS, IBS, and chronic pain is a real finding that argues for shared genetic underpinnings, but a single central-sensitization mechanism is unproven and transdiagnostic treatment strategies remain a hypothesis to test. (ch14 High Fibromyalgia Genetic Correlation With Chronic Pain and Psychiatric Traits; cert 0.42; severity unknown; origin: literature integration.)	Phase 2 / 0.42
hyp shared-central-genetic-architecture-fibro-mecfs	Hypothesis: the ME/CFS-fibromyalgia comorbidity may be underlain by a shared central (brain/neural) genetic architecture. Both conditions independently show brain/neural-cell-type heritability enrichment (fibromyalgia: (Kerrebijn et al. 2026); ME/CFS: (DecodeME Consortium, Ponting, et al. 2025) (Maccallini 2026)), consistent with — but not proof of — a shared central vulnerability; the co-occurrence figure is inflated by overlapping diagnostic criteria (Wolfe 2016) and the decisive direct fibromyalgia × ME/CFS genetic correlation is untested. This is a cross-reference to the detailed ch14 analysis, not an independent claim. Falsifiability: a direct cross-trait genetic-correlation analysis of fibromyalgia and ME/CFS will be significantly positive, and fibromyalgia PRS will be elevated in ME/CFS patients with comorbid fibromyalgia; falsified if the two conditions show near-zero genetic correlation despite clinical overlap. Consequence: if confirmed, a shared central genetic basis would make central-sensitization mechanisms a research priority to test across both conditions — but genetic architecture does not by itself determine effective treatment, so this is a research direction, not a current management change. (ch04 Shared Central Genetic Architecture May Underlie the ME/CFS-Fibromyalgia Comorbidity Three-Line Genetic Convergence on Neuronal Biology; cert 0.52; severity unknown; origin: literature integration.)	Phase 2 / 0.52
hyp fibromyalgia-autoimmune-reconciliation	Hypothesis: the no-MHC GWAS finding and the passive-IgG autoantibody evidence in fibromyalgia are not contradictory because germline genetic architecture and acquired antibodies are independent layers. The Kerrebijn 2026 GWAS found no MHC signal and no immune/glial heritability enrichment (genetic evidence against a primarily autoimmune/HLA-linked architecture) (Kerrebijn et al. 2026), yet passive-transfer evidence shows fibromyalgia/long-COVID IgG reproduces symptoms in mice (acquired humoral autoimmunity) (Goebel et al. 2021). An absence of HLA-linked germline risk does not rule out acquired pathogenic antibodies; whether a fibromyalgia antibody subset maps onto the non-HLA-linked central-sensitization architecture is a framing device, not an independently tested hypothesis. Falsifiability: fibromyalgia patients responding to immunomodulation will cluster in an autoantibody-positive subset without enrichment for the 26 GWAS loci; falsified if the GWAS loci and the autoantibody subset fully overlap (arguing for a unified HLA-linked mechanism). Consequence: fibromyalgia need not be forced into an “is/isn’t autoimmune” binary — the no-MHC finding and the passive-IgG evidence can both be true because germline genetics and acquired antibodies are separate layers. (ch14 Fibromyalgia Autoimmunity Is Not Contradicted by the Absence of an HLA Signal; cert 0.30; severity unknown; origin: literature integration + brainstorm.)	Phase 2 / 0.30

ID / Label	Details	Phase / Cert
oq lc-trajectory-chronicity	Open question: post-infectious syndromes (Long COVID) can become self-sustaining rather than resolving — 85% of those symptomatic at 2 months remained symptomatic at 1 year, and cognitive-impairment prevalence increased from 16% (2 mo) to 26% (12 mo), with neurological symptoms often delayed in onset and parosmia showing ~3-month onset (H. E. Davis et al. 2023). The increasing cognitive trajectory argues that at least a subset of Long COVID converges on an ME/CFS-like chronic state (ME/CFS is generally considered lifelong). This temporal evidence is integrated into ch14d as a counterfactual-validity argument for post-infectious chronicity. Consequence: if post-infectious syndromes commonly become self-sustaining or worsen over the first year, this argues for early mechanism-targeted intervention before chronicity locks in — relevant to both Long COVID and post-infectious ME/CFS onset. (ch14d Long COVID Trajectory: Temporal Evidence for Post-Infectious Chronicity; cert 0.60; severity unknown; origin: literature integration.)	Phase 3 / 0.60
oq lc-vaccine-heterogeneity	Open question: vaccination has heterogeneous effects on established Long COVID symptoms — 16.7% relief, 21.4% worsening, remainder unchanged (Tsuchida et al. 2022) (H. E. Davis et al. 2023), even though vaccination reduces the risk of new Long COVID (~15-41% partial protection). The heterogeneous response indicates Long COVID (and by extension ME/CFS) is not a single uniform process; the same intervention can improve or worsen depending on underlying mechanism. Consequence: a treatment that helps some post-infectious patients and harms others signals mechanistic heterogeneity, arguing for individualised titration and mechanism-based stratification rather than uniform protocols. (ch14d Vaccination and Established Long COVID: Heterogeneous Symptom Response; cert 0.45; severity unknown; origin: literature integration.)	Phase 3 / 0.45
oq insufficient-early-antibody-chronicity	Open question: a low/absent acute SARS-CoV-2 antibody response predicts Long COVID at 6-7 months in both hospitalized and non-hospitalized patients (García-Abellán et al. 2021) (Augustin et al. 2021) (H. E. Davis et al. 2023), suggesting an insufficient adaptive response (not only an overactive one) can gate post-infectious chronicity. Causal direction unestablished — low antibody could be a marker of viral persistence, a severity confound, or genuine immunocompromise. Consequence: if a weak early antibody response helps predict who develops a chronic post-infectious illness, it could support early immune-supportive intervention in the acute-to-subacute window, relevant to both Long COVID and post-infectious ME/CFS. (ch08 Does Insufficient Early Antibody Response Gate Post-Infectious Chronicity?; cert 0.55; severity unknown; origin: literature integration.)	Phase 3 / 0.55
lim seroreversion-control-bias	Limitation: a substantial proportion of SARS-CoV-2-infected patients never seroconvert or serorevert within months (more commonly in women, children, mild infections) (Van Elslande et al. 2021) (H. E. Davis et al. 2023), so serology-based control classification misassigns seroreverted symptomatic patients into control groups, biasing comparisons toward null and under-ascertaining post-infectious illness. The same testing-bias risk applies to ME/CFS research whenever serology defines infection exposure. Consequence: researchers should not rely on serology alone to assign infection-exposure status; symptom-based and clinically-diagnosed cohorts strengthen validity — a methodological correction transferable to post-infectious ME/CFS study design. (ch08 Seroreversion Creates Control-Group Bias in Post-Infectious Research; cert 0.55; severity unknown; origin: literature integration.)	Phase 3 / 0.55
lim lc-extrapolation-limits	Limitation: extrapolating Long COVID findings to ME/CFS carries limits that temper the confidence of the overlap argument — (1) the Davis 2023 review is a narrative (not systematic) synthesis with some preprint/small-cohort findings (H. E. Davis et al. 2023); (2) Long COVID is mostly studied in the first 1-2 years (acute-to-chronic extrapolation) while ME/CFS is lifelong, and many Long COVID symptoms resolve while ME/CFS persists; (3) the ~50% criteria overlap may partly reflect self-referential symptom criteria rather than shared biology, and the 2-day CPET divergence argues against full mechanistic equivalence; (4) several Long COVID findings derive from overlapping cohorts, inflating apparent certainty. Consequence: Long COVID evidence is a strong suggestive bridge and hypothesis source for ME/CFS, not proof of mechanistic identity — direct head-to-head mechanistic and longitudinal studies are still required. (ch14d Long COVID-ME/CFS Extrapolation: Limits of the Counterfactual; cert 0.55; severity unknown; origin: brainstorm critical category.)	Phase 5 / 0.55

ID / Label	Details	Phase / Cert
hyp central-motor-drive-cascade	Hypothesis: the earlier fatigability in ME/CFS reflects a failure to up-regulate central motor drive under sustained effort — a ≥4-step neurophysiological cascade (cortical motor output → corticospinal drive → spinal motoneuron recruitment → neuromuscular transmission → muscle-fiber recruitment → afferent feedback) rather than peripheral muscle exhaustion (Bedard et al. 2026). Multimodal neuroimaging (grip force + EMG + EEG + fMRI + corticomuscular coherence) in n=15 ME/CFS vs n=19 controls showed equal maximum voluntary force but earlier fatigue onset, with healthy controls steadily increasing muscle (Dimitrov index) and brain (EEG power, BOLD) activity until fatigue then declining slowly, whereas ME/CFS showed minimal fluctuation — an absent up-regulation of motor-cortical and subcortical output. This is an association, not established causation (the authors explicitly disclaim a causal link; only TMS or a drug-intervention study could establish direction). The differential-diagnostic value lies in which central-acting interception drug restores motor-drive up-regulation: dopaminergic/effort-node probes (modafinil/armodafinil (Minzenberg and Carter 2008), methylphenidate, amphetamines, bromocriptine/rotigotine) and glutamatergic (amantadine) agents, each targeting a distinct cascade node (see Section Systemic inflammation is downstream of a more upstream cause). Falsifiability: if central motor drive is the bottleneck, ME/CFS patients whose motor-drive up-regulation is restored by a central-acting agent should show later fatigue onset and a normalized Dimitrov-index/EEG/BOLD trajectory on the same grip-force protocol, while peripheral ergogenic aids should not; falsified if a peripheral intervention alone restores performance, or if no central-acting agent alters the motor-drive trajectory. Consequence: reframing ME/CFS fatigability as a brain-level failure to drive muscle — rather than a purely peripheral muscle problem — directs research and treatment toward the central nervous system (dopaminergic/effort circuits) and gives a testable drug-response framework, though no drug is recommended on this evidence alone. (ch03 Central Neural Origin of Fatigability; ch34 Systemic inflammation is downstream of a more upstream cause; cert 0.45 — the 0.60 association discounted for its associational (non-causal) design and single small sample; severity unknown — NIH intramural cohort not stratified; origin: literature integration — gap-fill of prior fatigue-cycle ch30-tier NONE.)	Phase 3 / 0.45

ID / Label	Details	Phase / Cert
spec muscle-nav14-cramp-mecfs	Speculation: skeletal-muscle sodium-channel (Nav1.4, SCN4A) or potassium-channel (Kv7/KCNQ) dysfunction may contribute to the cramps and myalgia reported in ME/CFS, overlapping the muscle-ion-channel arm of the energy-ratchet model. Primary Nav1.4 dysfunction directly produces painful cramps and myotonic discharges in a channelopathy family (Torbergsen et al. 2015), and quinine’s anti-cramp action is proposed to involve membrane stabilization of skeletal-muscle sodium (Nav1.4) and potassium (Kv7/KCNQ) channels — a pharmacologic attribution of low confidence (the Kv7/KCNQ component is uncited in the cited reviews) (El-Tawil et al. 2010). The link to ME/CFS is indirect and low-certainty — the channel evidence is a primary channelopathy, not ME/CFS, and zero studies of skeletal-muscle excitability in ME/CFS exist. Quinine itself is not a safe probe for this hypothesis: FDA-boxed-warned (2006) and restricted (2010) against off-label leg-cramp use because serious adverse events (immune thrombocytopenia, drug-induced thrombotic microangiopathy (Liles et al. 2016), QT prolongation (Haeusler et al. 2018), hemolysis in G6PD deficiency) outweigh its marginal, publication-bias-inflated efficacy (El-Tawil et al. 2010) (Man-Son-Hing, Wells, and Lau 1998). Falsifiability: if muscle ion-channel/membrane-excitability disturbance contributes to ME/CFS cramps, ME/CFS patients should show altered skeletal-muscle excitability on electromyography or nerve-excitability testing, and cramps should respond to safer sodium-channel modulation (e.g. mexiletine/carbamazepine in specialist myotonia care (Spillane et al. 2025)) rather than to quinine; falsified if ME/CFS cramps are absent, are purely centrally mediated, or respond to no ion-channel modulator. Consequence: if confirmed, leg cramps and muscle pain in ME/CFS would point to a measurable muscle-membrane-excitability disturbance and toward safer ion-channel treatments — but today this is only a hypothesis, and quinine is not the way to test it. (ch34 Skeletal-muscle sodium-channel dysfunction may link cramps to ME/CFS muscle-excitability; cert 0.25; severity unknown — no ME/CFS studies; origin: literature integration — off-label treatment caution.)	Phase 3 / 0.25
spec mecfs-cramp-metabolic-substrate	Speculation: ME/CFS cramps may be driven primarily by an energetic/post-exertional state rather than a fixed channelopathy. Magnesium ((Garrison et al. 2020)) and non-drug therapies ((Hawke et al. 2021)) fail to show solid benefit for cramps, and quinine’s benefit is marginal and publication-bias-inflated ((El-Tawil et al. 2010)); sodium-channel blockers reduce myotonia (a distinct condition) per Cochrane but are not validated as a cramp probe (Spillane et al. 2025). If the cramp substrate were a fixed ion-channel hyperexcitability, one might expect at least one channel modulator to show a signal; the uniformly weak response across classes raises the possibility (without proving it — trial failures can reflect small effect sizes, heterogeneous causes, or design) of an energetic driver (depleted ATP, altered calcium handling in fatigued muscle). This competes with the Nav1.4 channel speculation (spec-muscle-nav14-cramp-mecfs) as an alternate interpretation. Falsifiability: if ME/CFS cramps are primarily metabolic/post-exertional, cramp frequency should correlate with PEM severity and cluster in the post-exertional window, and should not respond to any channel-active drug, while pacing and post-exertional recovery should help; falsified if cramps are independent of exertion and respond to a channel modulator. Consequence: if cramps in ME/CFS are a symptom of exertion-related energy failure rather than a treatable muscle-electrical problem, pacing and recovery matter more than any cramp medication — consistent with the observation that no cramp drug works well for ME/CFS patients. (ch34 ME/CFS cramps may be metabolic/post-exertional rather than a fixed channelopathy; cert 0.25; severity unknown — no ME/CFS studies; origin: brainstorm idea 1.3.)	Phase 5 / 0.25

ID / Label	Details	Phase / Cert
spec deconditioning-osteoporosis	Speculation: sustained reduced mobility in moderate-to-severe ME/CFS raises osteoporosis risk via disuse bone loss. Mechanical unloading reduces osteocyte mechanosensing, raising sclerostin and shifting the RANKL/OPG balance toward resorption, producing bone loss in immobilization and bed rest (Rolvien and Amling 2022); muscle contraction opposes this via muscle–bone RANKL/OPG coupling (Qin et al. 2013), and chronic deconditioning degrades muscle and bone together (Hart 2021). Moderate–severe ME/CFS involves sustained activity reduction and, in the most affected, prolonged recumbency, so patients face the same unloading stimulus that causes disuse bone loss in immobilization, bed rest, and spinal-cord injury. No direct ME/CFS osteoporosis or BMD study exists (research gap). Falsifiability: in ME/CFS stratified by mobility, hip/lumbar BMD (DXA) will be lower in more immobile strata after adjusting for age, sex, menopause, vitamin D, and calcium; falsified if BMD does not track mobility. Consequence: bone health is worth monitoring in moderate–severe ME/CFS even absent ME/CFS-specific fracture data, because reduced mobility is a well-documented cause of bone loss in other immobilizing conditions — a DXA-screening discussion is reasonable. (ch30 Sustained Reduced Mobility Raises Osteoporosis Risk in Moderate–Severe ME/CFS Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.40; severity moderate/severe/very-severe; origin: literature integration.)	Phase 3 / 0.40
oq-vitamind-housebound	Open question: whether housebound/low-sunlight ME/CFS patients are vitamin-D deficient is unresolved — the premise conflicts with the one direct cohort. A cohort (ME/CFS n=92 vs controls n=94, LC-MS/MS) found ME/CFS patients had higher 25(OH)D (60.2 vs 47.3 nmol/L, p=0.001, supplement-driven) and no vitamin D–fatigue association (Earl et al. 2017), contradicting the assumption of deficiency; however that cohort was not severity-stratified, and housebound/very-severe patients may be underrepresented, so the question is not settled. The correct stance is to measure serum 25(OH)D when deficiency is suspected, not assume it. Falsifiability: a severity-stratified cohort of housebound/very-severe ME/CFS patients (vs mild) will show whether 25(OH)D tracks sunlight/mobility; falsified if housebound patients show no deficiency relative to ambulant patients. Consequence: clinicians should not assume vitamin D is deficient in a given ME/CFS patient; a serum 25(OH)D measurement resolves it, and supplementation for fatigue alone is not supported by direct evidence. (ch30 Vitamin D Deficiency in Housebound ME/CFS: Premise vs. Direct Evidence Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.30; severity housebound/very-severe (unverified); origin: literature integration.)	Phase 3 / 0.30
spec-calcium-deficiency	Speculation: restricted and avoidant diets in ME/CFS risk calcium deficiency. ME/CFS patients often restrict food intake (sensitivities, low-histamine/elimination diets, reduced appetite/preparation capacity in severe disease), which can lower calcium-rich-food intake (dairy, broccoli/cabbage, soya, tofu, nuts, fortified bread, fish with edible bones) below requirement; low calcium intake combined with the resorption state of disuse could worsen bone loss. No direct calcium-status study in ME/CFS exists (research gap). Falsifiability: a dietary-restriction/elimination history will associate with lower calcium intake and higher PTH (or lower adjusted calcium) after adjusting for vitamin D; falsified if restriction does not track calcium status. Consequence: patients on long-term elimination or restrictive diets should have calcium intake checked — bone can be lost silently for years, and restricted diets are a preventable contributor. (ch30 Restricted and Avoidant Diets Risk Calcium Deficiency in ME/CFS Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.25; severity moderate/severe/very-severe; origin: literature integration.)	Phase 3 / 0.25
oq-weightbearing-infeasible	Open question: weight-bearing exercise for bone health is not feasible in moderate–severe ME/CFS, and the trade-off is unresolved. Standard osteoporosis-prevention advice to do weight-bearing activity is unachievable for moderate–severe ME/CFS (it reliably triggers PEM), yet weight-bearing protects bone while the reduced-mobility state promotes bone loss (Sustained Reduced Mobility Raises Osteoporosis Risk in Moderate–Severe ME/CFS). Safe-movement approaches in ch30 (recumbent, seated, aquatic, isometric, bed-based) preserve some musculoskeletal loading without full weight-bearing stimulus, but whether they provide enough mechanical stimulus to protect BMD in ME/CFS is unknown. Falsifiability: non-weight-bearing recumbent/seated movement at sub-AT intensity will slow hip/lumbar BMD decline (DXA) vs no programmed movement without increasing PEM; falsified if non-weight-bearing loading fails to preserve BMD. Consequence: patients with moderate–severe ME/CFS should not be pressured into weight-bearing exercise for bone health — it is often impossible and can harm — but the resulting loss of bone-protective loading is a real, unmeasured risk clinicians should weigh against the cost of PEM. (ch30 Weight-Bearing Exercise for Bone Health Is Not Feasible in Moderate–Severe ME/CFS — Unresolved Trade-Off Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.40; severity moderate/severe (not feasible bedbound); origin: literature integration.)	Phase 3 / 0.40
lim osteoporosis-treatment-mecfs	Limitation: osteoporosis treatment options exist but carry ME/CFS-specific caveats. Calcium and/or vitamin D supplementation is first-line only if serum levels are low; antiresorptives (alendronate, risedronate) reduce turnover but are associated with atypical femur fracture on long-term use (~100/100k person-yrs) (Shane et al. 2014) and have strict administration requirements (upright posture, empty stomach) that are difficult or impossible for many bedbound patients (Arjunan et al. 2023); calcium-only supplements raise MI risk (HR 1.31) (Bolland et al. 2010); anabolic therapies (teriparatide) carry adverse effects (nausea, insomnia, flushing, myalgias, hypercalcemia) overlapping ME/CFS symptoms (Taylor and Saag 2019). No ME/CFS or bedbound safety data exist; drug choice is likely to require trial and error under clinician supervision. Consequence: if a person with ME/CFS develops osteoporosis, treatment is available but not a simple off-the-shelf choice — side-effect overlap with ME/CFS symptoms and bedbound administration difficulty mean drug choice often requires trial and error under clinical supervision. (ch30 Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert n/a; severity all; origin: literature integration.)	Phase 3 / n/a
spec-severity-gradient-boneloss	Speculation: bone loss in ME/CFS follows a severity gradient detectable by circulating turnover markers, refining the flat “all patients at risk” claim (origin: brainstorm idea 1.1). The disuse chain (Sustained Reduced Mobility Raises Osteoporosis Risk in Moderate–Severe ME/CFS) predicts a dose–response: mildly affected, still-ambulant patients show little/no bone loss, while severe/bedbound patients (sustained unloading) show measurable decline (Rolvien and Amling 2022); serum sclerostin/CTX/P1NP change on weeks–months before BMD falls on months–years. If correct, the moderate–severe/bedbound subset — not all ME/CFS — needs bone surveillance. Falsifiability: across a severity-stratified cohort, sclerostin and CTX rise and P1NP falls monotonically mild→severe and correlate with objective immobility and BMD decline, independent of age/sex/vitamin D. Consequence: if true, only severely affected, largely bedbound patients would need bone-density checks, sparing most patients unnecessary testing. (ch30 Bone Loss in ME/CFS Follows a Severity Gradient Detectable by Circulating Turnover Markers Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.30; severity moderate/severe/very-severe; origin: brainstorm idea 1.1.)	Phase 5 / 0.30
spec-inflammation-driven-boneloss	Speculation: chronic inflammation — not just mechanical unloading — may drive bone loss in a subset of ambulatory ME/CFS patients (origin: brainstorm idea 1.2). Inflammatory cytokines (IL-6, TNF-α) promote RANKL and suppress osteoblast activity, causing bone loss independently of loading, so bone loss could occur even in ambulant patients with normal activity — the opposite prediction to Bone Loss in ME/CFS Follows a Severity Gradient Detectable by Circulating Turnover Markers. Distinguishable by turnover profile (disuse: high sclerostin; inflammation: high RANKL/low P1NP with high IL-6/TNF-α). Falsifiability: a subgroup of ambulatory ME/CFS patients will show elevated CTX and suppressed P1NP with high IL-6/TNF-α but normal sclerostin and normal immobility, indicating an inflammatory rather than disuse driver. Consequence: if true, some patients with near-normal activity could still lose bone, so activity level alone would not indicate bone risk and a mobility-based screening rule would miss them. (ch30 Chronic Inflammation — Not Just Unloading — May Drive Bone Loss in a Subset of Ambulatory ME/CFS Patients Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.20; severity all; origin: brainstorm idea 1.2.)	Phase 5 / 0.20
oq-boneloss-threshold	Open question: whether ME/CFS mobility actually crosses the bone-loss threshold is unresolved (origin: brainstorm ideas 2.1/10.1). The bed-rest/SCI precedent behind Sustained Reduced Mobility Raises Osteoporosis Risk in Moderate–Severe ME/CFS involves continuous near-total unloading; moderate–severe ME/CFS retains some weight-bearing, and it is unresolved whether unloading is continuous enough to cross the bone-loss threshold. Decisive test: a severity-stratified cross-sectional DXA + sclerostin/CTX/P1NP + 25(OH)D + calcium + objective immobility study, filling the “no direct ME/CFS BMD study” gap. Falsifiability: if ME/CFS BMD is preserved across severity strata and sclerostin does not track immobility, H1’s threshold is not crossed and the disuse mechanism is clinically irrelevant. Consequence: one non-invasive study would tell patients and doctors which — if any — patients actually lose bone, instead of assuming risk from bed-rest in other diseases. (ch30 Does ME/CFS Mobility Actually Cross the Bone-Loss Threshold? Unresolved Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert 0.45; severity moderate/severe/very-severe; origin: brainstorm ideas 2.1/10.1.)	Phase 5 / 0.45
lim osteoporosis-external-validity	Limitation: every mechanistic anchor for the osteoporosis-risk claim is non-ME/CFS and mostly animal/review (Rolvien 2022 review, Hart 2021 review, Qin 2013 SCI rat model (Rolvien and Amling 2022) (Hart 2021) (Qin et al. 2013)), so external validity to ME/CFS is unestablished; the only direct ME/CFS study (Earl 2017) is not severity-stratified and supplement-exposed (Earl et al. 2017). Even where disuse bone loss occurs, low fall risk may keep fracture risk low, and treatments carry their own harms — so the osteoporosis angle could be net-irrelevant to clinical care despite a correct mechanism. All bone-loss claims in ME/CFS are low-certainty speculation until severity-stratified data exist. Consequence: everything currently said about osteoporosis risk in ME/CFS is borrowed from bed-rest and animal studies, not measured in ME/CFS patients — read as hypothesis, not established risk; no supplementation or treatment should start without a demonstrated deficiency. (ch30 Positioning and Postural Management Prevents Contractures and Pressure Injuries in Bedbound ME/CFS; cert n/a; severity all; origin: brainstorm categories 10/11/12.)	Phase 5 / n/a

ID / Label	Details	Phase / Cert
cf prolactin-buspirone-exaggerated	Clinical finding: ME/CFS patients mount an exaggerated prolactin response to an oral buspirone challenge despite normal baseline prolactin, replicated by an independent group (Bakheit 1992 (Bakheit et al. 1992); Sharpe 1996 (Sharpe et al. 1996) from a separate team) plus a small case series (Sharma 2001 (Sharma et al. 2001)). The reported ME/CFS-vs-depression contrast (lowest in depression, highest in CFS) comes from a d-fenfluramine study, not buspirone (A. J. Cleare et al. 1995) — the buspirone-vs-depression comparison has never been performed. Null fenfluramine results (Yatham et al. 1995) (Bearn et al. 1995) argue against a purely global serotonergic mechanism without localizing the defect to D2; Sharpe’s CFS group reported more nausea (a prolactin-raising confound not separable in that design). Small studies (n=11-20/arm), all pre-2010, line abandoned and never re-tested with modern criteria; replication needed. Falsifiability: a modern pre-registered buspirone challenge in IOM-criteria ME/CFS vs depression vs healthy shows no prolactin-AUC difference, the effect is an artifact of pre-Fukuda criteria/uncontrolled confounders, or it disappears after adjustment for nausea; falsified if ME/CFS no longer differs from controls. Consequence: if replicated with the nausea confound excluded, a controlled buspirone challenge could become a candidate test separating ME/CFS from healthy physiology — and, if the never-tested depression contrast holds, eventually from depression too. (ch10 Exaggerated Prolactin Response to Buspirone in ME/CFS, Replicated by an Independent Group; cert 0.62 for the vs-control finding — the vs-depression discriminator is untested on buspirone; severity unknown — not stratified; origin: literature integration.)	Phase 3 / 0.62
oq prolactin-buspirone-mechanism	Open question: what explains the exaggerated prolactin response is unresolved. Competing candidates: (a) reduced tuberoinfundibular dopaminergic tone (dopamine-D2 brake on lactotroph (Ben-Jonathan and Hnasko 2001) (Fitzgerald and Dinan 2008) (Qi-Lytle, Sayers, and Wagner 2023)) — constrained-in but not confirmed by the null fenfluramine results (Yatham et al. 1995) (Bearn et al. 1995), and consistent with ME/CFS dopaminergic findings (basal-ganglia reward deficits; OSU6162 trial (Haghighi et al. 2021)) which concern other, anatomically distinct dopaminergic systems; (b) serotonergic 5-HT1A hypersensitivity — constrained by null growth-hormone response (Sharpe et al. 1996); (c) estradiol modulation — links to ~80% female predominance (though the key replication studied men only) but no direct ME/CFS prolactin-by-sex study exists; (d) differential emetic (nausea) response — Sharpe’s CFS group reported more nausea, and nausea itself raises prolactin; the hormonal signal could be a drug-tolerability epiphenomenon. Not mutually exclusive; decisive test is a modern study with both ME/CFS and depression arms, sex/menstrual-phase stratification, cortisol measurement ((Dinan et al. 2001)), and recorded nausea. Falsifiability: each mechanism makes a distinct, testable prediction about whether the exaggerated response is dopamine-, serotonin-, estradiol-, or nausea-dependent. Consequence: which mechanism is correct determines whether this peripheral test reflects a dopaminergic deficit, another endocrine effect, or a tolerability artifact — a prerequisite for using the challenge as a meaningful biomarker. (ch10 What Explains the Exaggerated Prolactin Response — Dopamine, Serotonin, Estradiol, or Nausea?; cert n/a; severity unknown; origin: literature integration.)	Phase 3 / n/a
spec fra-estradiol-prolactin-link	Speculation: estradiol modulation of the prolactin response may partly explain the ~80% female predominance of ME/CFS. Estradiol regulates prolactin by modulating the dopaminergic brake and lactotroph synthesis (Ben-Jonathan and Hnasko 2001) (Qi-Lytle, Sayers, and Wagner 2023), and the exaggerated buspirone-prolactin response (Exaggerated Prolactin Response to Buspirone in ME/CFS, Replicated by an Independent Group) could be sex-hormone-modulated and therefore female-skewed. No direct ME/CFS prolactin-by-sex or estradiol-stratified study exists (gap). Falsifiability: if estradiol-dependent modulation drives female predominance, the buspirone-stimulated prolactin response should be larger in premenopausal women and track estradiol status; falsified if the exaggerated response is sex-independent or does not vary with estradiol. Consequence: if confirmed, part of the female excess of ME/CFS could trace to a measurable endocrine mechanism, arguing for sex-stratifying the challenge test. (ch21 Estradiol Modulation of the Prolactin Response as a Female-Predominance Link; cert 0.30; severity unknown; origin: literature integration.)	Phase 3 / 0.30
pred buspirone-challenge-replication	Prediction/proposed study: a modern pre-registered buspirone-challenge replication will show a reproducible prolactin-AUC gradient (ME/CFS > healthy > depression) under standardized conditions (overnight fasting, same hour, medication washout, sex/menstrual-phase stratification), testing the historical finding with modern criteria. Falsified if a modern IOM-criteria ME/CFS cohort shows no difference from depression or healthy controls, or the effect disappears with confounder control. Feasible and cheap — one visit, standard endocrine assays, drug already in clinical use. Consequence: this one study would revive or retire a notable but abandoned biomarker line; either outcome is scientifically informative. (ch47 Buspirone Challenge Replication: A Modern Test of the Exaggerated Prolactin Response Reproducible Prolactin AUC Gradient: ME/CFS > Healthy > Depression; cert n/a — proposed study; origin: literature integration.)	Phase 3 / n/a
spec prolactin-dopamine-window	Speculation: the peripheral prolactin response to a D2-brake-lifting challenge acts as an indirect read-out of tuberoinfundibular (hypothalamic) dopaminergic tone, because prolactin is tonically inhibited by dopamine at the lactotroph D2 receptor (Ben-Jonathan and Hnasko 2001) (Fitzgerald and Dinan 2008). If the exaggerated buspirone→prolactin response reflects reduced dopaminergic inhibitory tone, this blood test becomes a candidate non-invasive proxy — specific to the hypothalamic/pituitary system, not a measure of dopamine elsewhere in the brain — for a deep-brain dopamine signal that would otherwise need PET or CSF. Falsifiability: if the exaggerated response tracks a hypothalamic dopamine deficit, it should correlate with other dopamine-function markers (anhedonia/motivation, DAT/VMAT2) and be reproduced by a pure D2-antagonist challenge; falsified if abolished by 5-HT1A blockade or uncorrelated with dopamine-linked symptoms. Consequence: a simple blood test could give researchers a cheap, candidate proxy for one deep-brain dopamine signal, making a central hypothesis more testable without imaging — once validated. (ch10 Prolactin as a Peripheral Window on Hypothalamic Dopamine Tone; cert 0.45; severity unknown; origin: brainstorm idea 1.2.)	Phase 5 / 0.45
spec prolactin-msn-link	Speculation: the exaggerated prolactin response and dopamine-related motor/reward symptoms in ME/CFS could share a common dopaminergic deficit spanning both the tuberoinfundibular system (lactotroph) and the dopamine-regulated medium spiny neurons of the basal ganglia. A weak convergence hypothesis — it does not claim the pituitary finding causes striatal symptoms, nor even that the (anatomically distinct, independently regulated) systems co-vary; it proposes co-variation as plausible and testable, and explanatory if present. Connects to nigrostriatal dopaminergic findings in Long COVID (Long COVID Dopamine Bridge: Converging Independent Dopaminergic Evidence). Falsifiability: the exaggerated prolactin response should correlate with striatal-dopamine or dopamine-linked measures (anhedonia, effort cost, motor fatigue); falsified if unrelated to all such measures. Consequence: if co-variation is confirmed, a peripheral test could serve as a candidate research proxy for one dopaminergic deficit otherwise requiring brain imaging — remaining hypothalamic-system-specific until then. (ch10 Medium-Spiny-Neuron Dopaminergic Link as a Broader Dopamine-Axis Sign; cert 0.30; severity unknown; origin: brainstorm idea 1.3.)	Phase 5 / 0.30
spec prolactin-subtype-marker	Speculation: if the exaggerated prolactin response reflects a dopaminergic (D2-brake) abnormality present in only a subset of ME/CFS patients, the buspirone challenge could act as a biological subtype marker identifying a dopamine-dominant subgroup within the heterogeneous population (Exaggerated Prolactin Response to Buspirone in ME/CFS, Replicated by an Independent Group). A dopaminergic subtype would differ on dopamine-linked symptoms (motivation, anhedonia, motor slowing) and possibly response to dopamine-modulating interventions. Falsifiability: within an ME/CFS cohort, exaggerated responders should cluster with higher dopamine-linked symptom burden and differ from non-responders on dopamine-sensitive measures; falsified if responders and non-responders are indistinguishable on all dopamine-linked measures. Consequence: if true, a single challenge test could separate a meaningful biological subgroup, pointing them toward dopamine-focused research and treatment. (ch10 The Challenge Test as a Dopaminergic Subtype Marker; cert 0.35; severity unknown; origin: brainstorm idea 1.4.)	Phase 5 / 0.35
spec long-covid-dopamine-bridge	Speculation: the dopaminergic interpretation of the prolactin finding is mechanistically compatible with independent dopaminergic evidence from Long COVID — VMAT2-PET striatal dopamine-terminal loss (see ch09 neurological) and an open-label OSU6162 trial in ME/CFS (Haghighi et al. 2021). If reduced dopaminergic activity underlies both the exaggerated prolactin response and Long COVID striatal dopamine loss, the two lines would support a shared dopamine-deficit account of post-infectious fatigue states — but they measure anatomically distinct nodes (tuberoinfundibular vs nigrostriatal) whose co-variation is an untested assumption of the bridge, and CSF dopamine indices are normal in PI-ME/CFS (ch09). Falsifiability: comorbid post-infectious ME/CFS/Long COVID patients with exaggerated prolactin responses should also show dopaminergic changes (DAT/VMAT2, dopamine-linked symptoms); falsified if prolactin response and striatal dopamine measures are independent. Consequence: if the lines converge, dopamine dysregulation would be a shared, targetable feature of post-infectious ME/CFS and Long COVID, and the low-cost prolactin challenge could screen for that subgroup. (ch10 Long COVID Dopamine Bridge: Converging Independent Dopaminergic Evidence; cert 0.40; severity unknown; origin: brainstorm idea 8.1.)	Phase 5 / 0.40

ID / Label	Details	Phase / Cert
lim lifelong-onset-criterion	Limitation (case-definition, ch05): the “new or definite onset (not lifelong)” criterion shared by Fukuda, Canadian Consensus, and IOM assumes a categorical boundary between a constitutional lifetime energy profile and acquired ME/CFS. That boundary is difficult to operationalize and risks excluding the mild, lifelong-energy-constrained patient. Sunnquist et al. (2015) found the lifelong-fatigue exclusion hard to operationalize, rarely met even by patients describing long-standing fatigue, and non-discriminating on symptoms/function, and recommended removing it (Sunnquist et al. 2015). Implication: a patient who has always run on low reserve can still acquire ME/CFS with a definite new PEM component; PEM — not premorbid energy baseline — is the discriminating feature. Severity applicability: mild / lifetime-energy presentations; moderate-to-severe transition not addressed. Falsifiable resolution: an IOM-criteria cohort stratified by self-reported lifelong fatigue showing no symptom/functional or biomarker differences between lifelong-fatigue and non-lifelong subgroups (supporting removal). Consequence: clinicians should not dismiss ME/CFS because the patient was always tired; the decisive feature is new-onset PEM. (ch05 The “Not Lifelong Onset” Criterion May Incorrectly Exclude Mild and Constitutional Cases; origin: literature integration — Sunnquist 2015.)	Phase 3 / n/a
spec mild-constitutional-energy-spectrum	Speculation (severity continuum, ch06 sec-02): a minority of ME/CFS patients report lifetime low/constrained energy — “always more tired than expected,” ADHD/autism energetic profile, or constitutionally low reserve (Sunnquist et al. 2015) (Leonard A. Jason et al. 2010) — raising whether mild/constitutional energy constraint is a distinct predisposing state on a continuous spectrum or the mild end of the same illness. Severity clustering identifies a low-severity subset functioning within a constrained daily energy envelope (Leonard A. Jason et al. 2010), while in more-impaired patients the limiting factor is a low daily energy envelope rather than discrete crashes (A. A. Brown, Evans, and Jason 2013); a childhood-onset chronic disabling fatigue trajectory (1.9% at 13), defined without PEM, is a measurable persistent low-functioning state (Collin et al. 2015). Competing explanation: population data show the clearest premorbid predictor of later fatigue is excessive childhood energy, not low baseline (Harvey et al. 2009) — reconcilable if the predisposing factor is a low reserve ceiling ($R_\text{headroom}$), an open question. Falsifiability: if children with objectively low baseline energy capacity develop ME/CFS at no higher rate than peers with normal capacity, the low-reserve-ceiling predisposing model is unsupported. Not yet replicated — no longitudinal cohort has tracked mild/constitutional low-reserve individuals into ME/CFS onset; transition to moderate/severe unmeasured. Severity applicability: mild / lifetime-energy; moderate-to-severe unknown. Consequence: mild constitutional patients warrant the same pacing education and PEM screening, but the label must not withhold diagnosis — PEM, not baseline energy, distinguishes disease. (ch06 Mild and Constitutional Energy Constraint as a Continuous Predisposing Spectrum; origin: brainstorm.)	Phase 3 / 0.45
oq mild-lifetime-case-recognition	Open question (case-finding, ch05 sec-08): whether to recognize mild/lifetime-energy ME/CFS by treating baseline energy level and PEM as two independent axes. The shared “not lifelong” onset criterion (Sunnquist et al. 2015) excludes by assumption patients who have always been tired, yet lifelong fatigue is hard to operationalize and non-discriminating (Sunnquist et al. 2015). Proposed recognition pathway: (1) screen lifetime energy and PEM separately; (2) drop “lifelong fatigue” as exclusion; (3) use PEM presence as the gate to full assessment; (4) offer low-harm pacing to all low-energy patients (access). Research questions: would pre-registered removal of the exclusion change diagnosis without diluting the syndrome? Does a prospective cohort of lifetime-low-energy individuals tracking PEM onset show a mild→moderate/severe transition (Collin et al. 2015)? Critical caveat: broadening risks over-diagnosing tired-but-well people; PEM — not baseline energy — is the discriminator, and the mild end may lack a detectable PEM signature (Harvey et al. 2009). Severity applicability: mild / lifetime-energy; transition unmeasured. Consequence: separating baseline energy from PEM could stop clinicians dismissing the “always tired” patient and instead look specifically for a new, definite PEM component. (ch05 Recognizing Mild and Lifetime-Energy ME/CFS — Separating the Tired Constitution from the Disease; origin: brainstorm P1.1/C6.1/D9.1/R2.2.)	Phase 5 / n/a
lim mild-lifetime-critical-caveats	Limitation (epistemic, ch05 sec-08): four concerns bound the mild/lifetime recognition proposal. (1) Removing the “not lifelong” exclusion could over-broaden ME/CFS to tired-but-well people — counter-guard is PEM as the gate, but PEM is self-report-bias-prone at the mild end. (2) PEM may not be detectable/reportable at the mild end, so a PEM-gated pathway could miss the target population. (3) The premorbid signal may be high energy, not low (Harvey OR 2.63 for excessive childhood energy) — if this null holds, lifelong low energy is a red herring and only a recognition/access gap survives (Harvey et al. 2009). (4) Evidence-quality limits: Sunnquist is single-cohort; 3 of 6 Phase-1 papers share the DePaul/Jason group; no longitudinal mild→moderate/severe data; severity self-reported (Sunnquist et al. 2015) (Leonard A. Jason et al. 2010) (A. A. Brown, Evans, and Jason 2013) (Collin et al. 2015). Severity applicability: mild / lifetime-energy. Consequence: treat the recognition proposal as a testable hypothesis and access pathway, not an established diagnostic expansion — over-application could do more harm than the under-recognition it tries to fix. (ch05 Critical Caveats on the Mild / Lifetime-Energy Recognition Proposal; origin: brainstorm C10.1/C10.2/C11.1/C12.1.)	Phase 5 / n/a
pred mild-lifetime-transition-cohort	Prediction/proposed study (ch06 sec-03): a prospective cohort of individuals with documented lifelong low-energy constitution (low baseline, no PEM at baseline) will, after a triggering stressor, acquire a new definite PEM and functional decline (i.e., ME/CFS) at a higher rate than matched normal-baseline peers, with the transition marked by emergence of PEM rather than change in resting energy (Sunnquist et al. 2015). Falsified if lifetime-low-energy individuals develop ME/CFS at no higher rate than normal-baseline peers, or if later decline is explained entirely by cumulative overactivity (high-energy premorbid route (Harvey et al. 2009)) rather than baseline reserve. Measurement: baseline energy + PEM screen, prospective 2-day CPET where feasible, functional capacity, and measured severity transitions (A. A. Brown, Evans, and Jason 2013). Severity applicability: mild → moderate/severe transition. Consequence: resolves whether lifelong low-energy patients are a predisposed population needing early PEM screening/pacing or the visible tail of normal tiredness. (ch06 Prospective Cohort: Do Mild / Lifetime-Energy Individuals Transition to Moderate or Severe ME/CFS?; origin: brainstorm R2.1.)	Phase 5 / n/a
syn mild-lifetime-recognition	Synthesis (ch05 sec-08): converges the cycle’s environments — lifelong energy constraint is continuous with ME/CFS at the mild end, the mild/lifetime patient is excluded by a non-discriminating “not lifelong” onset criterion (Sunnquist et al. 2015), and the low-reserve mild-end population is the causal-hierarchy model’s near-separatrix subthreshold reservoir (Leonard A. Jason et al. 2010) (A. A. Brown, Evans, and Jason 2013) (Collin et al. 2015). Claim is conditional: PEM, not baseline energy, is the discriminator; over-broadening risk and the high-energy premorbid alternative bound it (Harvey et al. 2009). Clinical path: do not exclude on lifelong fatigue; screen baseline-energy and PEM as two axes; reserve the diagnosis for those with new PEM; offer low-harm pacing to all. Open question: whether mild/lifetime patients transition to moderate/severe (Prospective Cohort: Do Mild / Lifetime-Energy Individuals Transition to Moderate or Severe ME/CFS?). Overall certainty 0.45 for continuous-spectrum; recognition construct ~0.30. Severity applicability: mild / lifetime-energy. Consequence: clinicians should stop dismissing the “always tired” patient and look specifically for new-onset PEM. (ch05 Lifetime Low-Energy Patients Are a Recognition Gap and a Near-Separatrix Population, Not a Categorically Distinct Disease — But PEM, Not Baseline Energy, Is the Discriminator; origin: synthesis of this cycle.)	Phase 10a / 0.45

ID / Label	Details	Phase / Cert
cf neurodivergent-diagnostic-overshadowing	Clinical finding (ch09 subsec:social-emotional-dysfunction): in a Dutch register survey of autistic adults (n=1,211), 24.6% reported at least one prior psychiatric diagnosis perceived as a misdiagnosis; personality, anxiety, and mood disorders were most common, and chronic fatigue syndrome / burnout-related disorders were also explicitly listed, judged a misdiagnosis by 4% of the total sample (Kentrou et al. 2024). The study captures diagnostic confusion / overshadowing around fatigue and energy symptoms in autistic adults, particularly women; it does not demonstrate that clinicians dismiss genuine ME/CFS as “just autism.” Falsifiability: prospective cohort of autistic adults with new fatigue shows longer time-to-ME/CFS-diagnosis and lower PEM-assessment rates than non-autistic controls after adjusting for severity/access. (ch09 Perceived Misdiagnosis of Fatigue-Related Conditions in Autistic Adults; cert 0.55; origin: literature integration.)	Phase 3 / 0.55
spec neurodivergent-masking-double-burden	Speculation (ch09 subsec:social-emotional-dysfunction): autistic/ADHD camouflaging is a metabolically expensive self-regulation demand. Research with healthy participants shows sustained self-control depletes glucose and impairs subsequent self-regulation (Gailliot et al. 2007); (Baumeister et al. 1998). In ME/CFS, where prefrontal metabolic reserves are already constrained, the same social encounter may require energy both for masking ME/CFS symptoms (hiding pain, flat affect, exhaustion) and for neurodivergent camouflaging (hiding autistic/ADHD traits). The two demands draw on the same prefrontal resource pool. Based on Grant 2026 (camouflaging predicts central-sensitivity symptoms) (Grant, Norton, and Hoekstra 2026) and Hodge 2026 (camouflaging–mental-health association) (Hodge and Meltzoff 2026). Falsifiability: ND+ME/CFS patients report higher social-exertion cost and longer recovery than neurotypical ME/CFS patients matched for severity, correlating with masking burden; falsified if no dose–response between masking score and social-exertion cost across ND/NT × ME/CFS groups. (ch09 Neurodivergent Masking as a Compounding Energy Cost; cert 0.40; origin: literature integration.)	Phase 3 / 0.40
oq neurodivergent-identity-loss	Open question (ch09 subsec:social-emotional-dysfunction): whether ME/CFS energy loss produces an ND-specific identity injury — loss of access to special interests and the focused activities that organize autistic/ADHD selfhood, regulation, and social connection — is unresolved. No study has compared autistic/ADHD people with and without ME/CFS on this dimension. Falsifiability: mixed-methods cohort of autistic/ADHD adults with ME/CFS shows greater special-interest disruption and identity-grief scores than neurotypical ME/CFS controls after adjusting for overall severity. (ch09 Loss of Special Interests and Identity Disruption; cert n/a; origin: literature integration.)	Phase 3 / n/a
lim neurodivergent-evidence-directness	Limitation (ch09 subsec:social-emotional-dysfunction): the ND-specific psychological-consequence claim rests on indirect evidence only — autistic-burnout phenomenology, camouflaging cost in central-sensitivity syndromes, diagnostic overshadowing in autism, and ADHD–CFS transdiagnostic comparisons. No published study prospectively assesses psychological outcomes in a cohort meeting both formal ME/CFS criteria and formal autism/ADHD criteria. Phenomenological overlap must not be presented as condition equivalence; masking-cost extrapolation is plausible but unmeasured in ME/CFS; identity-loss and diagnostic-overshadowing claims are hypothesis-generating. (ch09 Indirect Evidence: No Direct ME/CFS × Neurodivergence Cohort; cert n/a; origin: literature integration.)	Phase 3 / n/a
cf neurodivergent-differential-diagnosis	Clinical finding (ch05 sec:differential): the presentations of autistic and ADHD patients overlap with ME/CFS across fatigue, sleep disturbance, social withdrawal, and cognitive slowing, creating diagnostic confusion. In a Dutch register survey of autistic adults (n=1,211), 24.6% reported at least one prior psychiatric diagnosis perceived as a misdiagnosis; chronic fatigue syndrome / burnout-related disorders were among the listed categories, judged a misdiagnosis by 4% of the total sample (Kentrou et al. 2024). That pattern shows energy-related diagnoses figure in the diagnostic confusion autistic adults experience before autism recognition; it does not mean clinicians dismiss ME/CFS as “just autism.” Adults with ADHD and chronic fatigue syndrome show overlapping psychological profiles (functional impairment, mood, self-efficacy) (D. C. Rogers et al. 2017). The differential discriminator is PEM regardless of comorbid ADHD/ASD. Falsifiability: prospective diagnostic study shows PEM distinguishes ME/CFS from ADHD/ASD presentations even when comorbid. (ch05 Autism and ADHD as Differential-Diagnostic Confounders for ME/CFS; cert 0.55; origin: literature integration.)	Phase 3 / 0.55
compat note	Compatibility (Phase 7): these claims reinforce the existing ADHD/ASD metabolic-reserve-predisposition hypotheses in ch07/ch36 by adding a compounding psychological/social layer and a recognition barrier (diagnostic overshadowing). No conflicts identified. They are independent of immune, viral, and pharmacological hypotheses but feed into the broader “energy cost” model. (origin: synthesis of this cycle.)	Phase 7 / n/a

ID / Label	Details	Phase / Cert
spec combination-antiviral-longcovid-pilot	Speculation (ch37 Combination Antiviral + Anti-Inflammatory Therapy: First Uncontrolled Data Point in Long COVID (Pridgen & Putrino 2026)): Pridgen & Putrino (2026) reported, in an open-label case series of 24 Long-COVID patients, that adding a 15-day Paxlovid course to a 120-day valacyclovir+celecoxib (IMC-2) backbone was associated with larger self-reported fatigue, brain-fog, and dysautonomia improvement than IMC-2 alone (PGIC fatigue +2, ($p < 0.0001$); VAS 55.3% greater reduction), reported durable to ~600 days (Pridgen and Putrino 2026). This is the first data point — uncontrolled, in Long COVID not ME/CFS — on the combination antiviral + anti-inflammatory strategy identified as an untested gap in the antiviral-trial history; it does not contradict the negative results for brief Paxlovid courses (NIH STOP-PASC trial; observational data (Durstenfeld et al. 2024)). Critical caveats: no placebo/control, subjective endpoints only, small n (12/arm), single site, and a major inventor conflict of interest (author is patent-holder/company founder for IMC-2); for-profit IRB. No ME/CFS-specific data; severity applicability unknown (Long-COVID cohort, not stratified); no pregnancy/lactation data. Must not be read as efficacy or a clinical recommendation — hypothesis-generating pilot warranting a randomized, double-blind, placebo-controlled trial in a virus-selected ME/CFS subgroup. Falsifiability: a controlled trial in an ME/CFS cohort selected for elevated herpesvirus antibody titers and/or viral-persistence biomarkers shows no difference between the combination regimen and placebo at 6 months on fatigue/functioning → combination antiviral + anti-inflammatory is not an effective anti-reservoir strategy. (ch37 Combination Antiviral + Anti-Inflammatory Therapy: First Uncontrolled Data Point in Long COVID (Pridgen & Putrino 2026); cert 0.21 — discounted 0.85 for Long-COVID population; severity unknown; origin: literature integration.)	Phase 3 / 0.21

ID / Label	Details	Phase / Cert
spec cross-condition-3d-convergence	Speculation (ch36 Cross-Condition Network Convergence of 3D-Genomic Architecture Is a Hypothesis, Not Demonstrated Shared Biology): when GWAS loci for ME/CFS (DecodeME), Long COVID, PTSD, RA, and MS are re-rendered as 3D genomic anchors (proprietary EpiSwitch Orion) and connected into protein-interaction networks, the gene sets share few individual genes yet interconnect into a dense landscape with candidate hubs (RUNX1, PPARGC1A/PGC-1alpha, STAT1, CDH2, NRP1, PLCG2) and enriched immune/cytokine, interferon, mitochondrial-biogenesis, metabolic, and neuroendocrine pathways (Hunter et al. 2026). Offered as a unifying explanation for shared symptoms across fatigue-associated syndromes. Entirely in silico; no experimental validation; no permutation testing (self-admitted); vendor-funded (11/14 authors are Oxford BioDynamics employees); no independent replication. Falsifiability: an independent, non-proprietary re-analysis (open-source 3D-genome maps or Hi-C-derived anchors, permutation testing against a degree-matched null) failing to reproduce cross-condition convergence beyond chance would refute the shared-regulatory-architecture claim. (ch36 Cross-Condition Network Convergence of 3D-Genomic Architecture Is a Hypothesis, Not Demonstrated Shared Biology; cert 0.20 — discounted 0.30 for computational-only population; severity unknown; origin: literature integration.)	Phase 3 / 0.20
lim cross-condition-convergence-evidence	Limitation (ch36 Cross-Disease Convergence Claim Rests on Unvalidated, Vendor-Dependent Analysis): the cross-disease network-convergence claim is not established biology — it rests on an in-silico, vendor-dependent analysis with no permutation test, no hub validation, and no independent replication (Hunter et al. 2026). Independent evidence supports the constituent biology (CD8+ T-cell exhaustion (Iu et al. 2024); T-cell-exhaustion markers in post-COVID/ME-CFS (Shahbaz2026single?)-cell-immune; energy-metabolism-immune-exhaustion interface (Van Campenhout et al. 2025); elevated soluble LAG-3 in persistent post-COVID (Ueland et al. 2025)) but corroborates exhaustion mechanisms within single diseases, not cross-disease convergence. ME/CFS and Long COVID GWAS loci are essentially disjoint (DecodeME), cautioning against over-reading network “shared biology” as shared genetics. (ch36 Cross-Disease Convergence Claim Rests on Unvalidated, Vendor-Dependent Analysis; cert n/a; origin: literature integration.)	Phase 3 / n/a
compat note	Compatibility (Phase 7): cross-condition-3d-convergence weakly reinforces the CD8+ mitochondrial-exhaustion hypothesis (hyp:cd8-mito-fragmentation-exhaustion) via its PPARGC1A/mitochondrial + immune-exhaustion content, and feeds into the EpiSwitch diagnostic and frozen-chromatin nodes — but as in-silico cert 0.20 evidence it does NOT strengthen any single-disease mechanism (no bumps; existing evidence independent). The convergence caveat (lim:cross-condition-convergence-evidence) extends the vendor-COI limitation (lim:episwitch-caveats). No conflicts, no circular deps. Compat audit at tmp/compat-audit-episwitch-crosscondition-hunter2026-2026-09-04.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
spec rashless-vzv-mecfs	Speculation (ch08 Rash-Less VZV Reactivation as an Underrecognized Neurovascular Mode within the Poly-Herpesvirus Pattern): VZV—the third member of the documented poly-herpesvirus co-reactivation serologic pattern in ME/CFS (Palomo et al. 2026); VZV can cause systemic and neurovascular disease without a rash (zoster sine herpete / “internal shingles”) (Bubak et al. 2026). Asymptomatic VZV reactivation drives arterial ischemic stroke in the general population (VIPS II: 9.8% vs 3.1% controls; up to one-third of childhood arterial ischemic strokes attributed to VZV) (Fullerton et al. 2025) (Bubak et al. 2026), and this rash-less mode mechanistically parallels the paper’s abortive-lytic-replication (ALR) theme—immune activation and tissue consequences without detectable classical productive infection. However, no empirical cohort links rash-less VZV reactivation to ME/CFS symptom flares; static VZV serology does not discriminate ME/CFS (VZV IgG among six herpesviruses poorly distinguished ME/CFS subgroups in a UK Biobank re-analysis (Domingues et al. 2023)); HSV-family nulls (Koelle et al. 2002) (Buchwald et al. 1996) leave the temporal-reactivation question open; and the ME/CFS application rests on a conceptual hypothesis of peripheral-ganglion VZV involvement in chronic fatigue (Shapiro 2009). The mechanism is well established; its ME/CFS application is hypothesis-level. Falsifiability: longitudinal VZV reactivation sampling (saliva/blood VZV PCR or paired anti-VZV dUTPase IgG) timed to PEM/symptom flares will detect transient VZV reactivation events correlating temporally with exacerbation beyond the poly-herpesvirus co-reactivation baseline; falsified if no temporal clustering around flares is found. (ch08 Rash-Less VZV Reactivation as an Underrecognized Neurovascular Mode within the Poly-Herpesvirus Pattern; cert 0.30 — ME/CFS application discounted for general-population mechanism evidence; severity unknown; origin: literature integration.)	Phase 3 / 0.30
compat note	Compatibility (Phase 7): rashless-vzv-mecfs feeds into the ch08 poly-herpesvirus ALR model (Poly-Herpesvirus ALR Cascade) and the viral-reactivation-as-consequence/bidirectional framings — adding VZV as a virus whose rash-less mode parallels ALR. No certainty bumps (new spec cert 0.30 is below the feed-in ≥0.50 and independent-≥0.60 thresholds; mutual reinforcement of low-certainty speculation does not bump). Independent of viral-reactivation-as-cause and the LC combination-antiviral pilot. Compat audit at tmp/compat-audit-vzv-reactivation-2026-09-04.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
spec thin-slice-mecfs-dismissal	Speculation (ch40 Thin-Slice First Impressions: Presentational Style as a Mechanism of ME/CFS Dismissal): cross-disease evidence (autism anchor Sasson2017, meta-analysis Wanigasekera2025, Parkinson Hemmesch2014, schizophrenia Parisi2026, facial-affect Foster2025) establishes that negative first impressions are driven by presentational style (reduced facial affect, expressivity, movement) independently of conversational substance, an effect robust within seconds and stable over exposure (Sasson et al. 2017) (Wanigasekera et al. 2025). ME/CFS patients frequently present with the implicated style (flat affect, reduced expressivity from neurometabolic dysfunction), so clinician dismissal of ME/CFS may be partly a thin-slice first-impression bias toward style before the reported medical substance (PEM, orthostatic intolerance) is weighed — a mechanism-level complement to the descriptive Bontempo invalidation model A Causal Model of Healthcare Invalidation and diagnostic-delay hypothesis. No direct ME/CFS-cohort study exists — mechanism transfer by analogy (all source studies: general-population perceivers of another condition, population weight 0.75). Falsifiability: a thin-slice study in which clinicians rate ME/CFS patients vs controls, with and without audio-visual presentation, will show less favorable first impressions and reduced care-intent for the audio-visual condition that attenuates or disappears when only the consultation transcript is provided. (ch40 Thin-Slice First Impressions: Presentational Style as a Mechanism of ME/CFS Dismissal; cert 0.45 — discounted, by analogy; severity unknown; origin: /integrate-topic autism-social-perception-stigma.)	Phase 3 / 0.45
oq disclosure-attenuation	Open question (ch40 Does Diagnostic Disclosure Attenuate First-Impression Dismissal of Contested-Illness Patients?): diagnostic disclosure reduces first-impression bias in some contexts but is condition-specific — autism disclosure improved employment-interview ratings (Norris et al. 2024); schizophrenia disclosure worsened first impressions while mislabeling as autistic improved them (Parisi et al. 2026); autistic raters did not gain from disclosure (DeBrabander et al. 2019); diagnostic awareness moderated cross-neurotype rapport without behavioral mediation (Efthimiou et al. 2025). Whether disclosing an ME/CFS diagnosis to a clinician/adjudicator/employer attenuates thin-slice dismissal is untested; for a contested illness long associated with psychosomatic framing, the label could trigger a pre-formed negative stereotype rather than attenuate. (ch40 Does Diagnostic Disclosure Attenuate First-Impression Dismissal of Contested-Illness Patients?; cert n/a — research question; origin: /integrate-topic autism-social-perception-stigma.)	Phase 3 / n/a
spec autism-firstimpression-mecfs-invisible	Speculation (ch14d Invisible-Illness First-Impression Dismissal as a Relational Phenomenon: A ME/CFS Parallel by Analogy): the autism double-empathy / relational model (Jones2024, Edey2016 double mind-blindness, Morrison2020 same-neurotype rapport, Morrison2019 rater-locus variability) frames cross-neurotype social difficulty as co-produced, not an intrinsic target deficit. Transferred by analogy to invisible illness: ME/CFS patients whose disease produces flat affect / reduced expressivity (ch09) may face a first-impression dismissal that is partly a perceiver phenomenon; the relational model implies the modifiable locus is the perceiver (shift weight from how the patient looks to what the patient reports), paralleling stigmatisation subtree node 8.7 “Autism Stigma → Neurodiversity Reversal”. Analogy/translation gap explicit — no ME/CFS-cohort first-impression study exists. Falsifiability: a thin-slice study of ME/CFS patients vs controls will show less favorable impressions in the audio-visual vs transcript-only condition, with perceiver-level variables accounting for more variance than patient characteristics (mirroring the autism rater-locus finding). (ch14d Invisible-Illness First-Impression Dismissal as a Relational Phenomenon: A ME/CFS Parallel by Analogy; cert 0.40 — discounted, by analogy; severity unknown; origin: /integrate-topic autism-social-perception-stigma.)	Phase 3 / 0.40
spec mecfs-motivation-misread	Speculation (ch40 The Energy-Cost Cue Misread as a Motivational Deficit: A ME/CFS-Specific First-Impression Attribution): the thin-slice first-impression mechanism may operate for ME/CFS through a specific attribution error — low motor output / reduced affect / slowed speech read as low motivation or malingering (an effort/agency inference), not as the social-awkwardness inference dominant in autism (Sasson et al. 2017). Consistent with slowed-movement-read-negatively in Parkinson’s (Hemmesch 2014) and the causal-stigma finding that psychosomatic/volitional attribution is the strongest dismissal predictor (Froehlich et al. 2022). If so, ME/CFS dismissal is a misattributed-cause error feeding psychologization/invalidation; remedy requires countering the effort default, not only weighting reported substance. Limitations: no thin-slice study has tested motivation attribution for fatigue targets — extension by analogy. Falsifiability: in a thin-slice task, perceivers free-labeling why they respond less favorably to ME/CFS audio-visual clips will disproportionately use motivation/effort words (vs transcripts), and this motivation attribution — not global likeability — will mediate reduced care-intent. (ch40 The Energy-Cost Cue Misread as a Motivational Deficit: A ME/CFS-Specific First-Impression Attribution; cert 0.30 — by analogy; severity unknown; origin: brainstorm idea 1.2.)	Phase 5 / 0.30
compat note	Compatibility (Phase 7): thin-slice-mecfs-dismissal and autism-firstimpression-mecfs-invisible reinforce the stigmatisation subtree node 8.7 (“Autism Stigma → Neurodiversity Reversal”, speculation 0.30, not yet integrated) and the ch09 social-emotional-dysfunction framing (observer misattribution of flat affect), and extend the general ME/CFS stigma content (ch40 invalidation A Causal Model of Healthcare Invalidation). spec:mecfs-motivation-misread specifically REINFORCES the pre-existing stigmatisation registry entries — “Illness Name Is an Independent Vector of Stigma” (Jason 2002: CFS label → lazy/malingering attributions) and “Perceived Stigma via Causal Attribution Pathway” (Froehlich 2022) — by supplying the thin-slice mechanism by which a low-motivation/malingering inference forms from presentational style (the visible analogue of the name-triggered stereotype). No certainty bumps applied (all new ME/CFS-transfer specs are by-analogy discounted ≤0.45, below the ≥0.60 reinforcement threshold, and Active Caps forbid bumping ME/CFS-transfer claims above 0.45 without a direct ME/CFS-cohort study). They are independent of (do not modify) the ASD-as-biological-predisposition hypotheses (ch07/ch14d neurodevelopmental-secondary) and the ND-psychological-consequences claims (ch09, ch05) — these operate on a different axis (biological/secondary vs social-perception). Compat audit at tmp/compat-audit-autism-social-perception-stigma-2026-09-04.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
oq autoimmune-sfn-serological-subtype	Open question (ch09 Is There a Distinct Autoimmune Serological SFN Subtype (Anti-TS-HDS / Anti-FGFR3) in ME/CFS?): is there a distinct autoimmune serological SFN subtype in ME/CFS defined by anti-TS-HDS (IgM) and anti-FGFR3 (IgG) antibodies? Supportive groups report these antibodies in cryptogenic/non-length-dependent SFN and sensory neuronopathy with DRG-predominance (Antoine et al. 2015) (T. D. Levine et al. 2020) (Tholance et al. 2020), and a mouse study reports FGFR3-Ab-induced DRG hypersensitivity (Salih et al. 2026) — but the claim is contested and NOT established. Independent real-world data show limited disease specificity and no immunotherapy-response prediction (Chompoopong et al. 2023); antibodies occur irrespective of objective small-fiber damage (Trevino and Novak 2021) (Murin et al. 2024); the anti-FGFR3 assay is not standardised across labs (Appeltshauser et al. 2025); fibromyalgia proxies (closest to ME/CFS) show no TS-HDS elevation or the reverse direction (Malik et al. 2019) (Nissan et al. 2023); both controlled IVIG trials are negative (Gibbons et al. 2023) (Geerts et al. 2021) (Ng et al. 2026); and an editorial calls the antibody-guided IVIG signal “fading” (Katzberg and Barnett 2023). No ME/CFS-cohort study exists. Falsifiability: a prospectively phenotyped ME/CFS cohort with antibody testing, skin-biopsy IENFD, and sudomotor testing will fail to find a seropositive subgroup that (a) has reduced IENFD/sudomotor dysfunction relative to seronegative patients and (b) responds to IVIG in an antibody-stratified trial — falsifying the ME/CFS-relevant subtype claim if seropositivity tracks neither objective small-fiber damage nor immunotherapy response. (ch09 Is There a Distinct Autoimmune Serological SFN Subtype (Anti-TS-HDS / Anti-FGFR3) in ME/CFS?; cert n/a — research question; severity unknown; origin: /integrate-topic autoimmune-sfn-subtype.)	Phase 3 / n/a
lim autoimmune-sfn-serology-not-validated	Limitation (ch09 Anti-TS-HDS / Anti-FGFR3 Serology Is Not a Validated Diagnostic or IVIG-Selection Tool in ME/CFS): anti-TS-HDS/anti-FGFR3 serology is commercially testable but NOT a validated diagnostic marker or IVIG-selection tool in ME/CFS. No ME/CFS-cohort study exists; fibromyalgia (closest overlapping population) shows no elevation or the reverse (Malik et al. 2019) (Nissan et al. 2023); real-world data find limited specificity and no response prediction (Chompoopong et al. 2023); the assay is not inter-lab standardised (Appeltshauser et al. 2025); both controlled IVIG trials are negative (Gibbons et al. 2023) (Geerts et al. 2021) while the positive open-label report is uncontrolled and responder-selective (Zeidman, Saini, and Mai 2022). Distinct from the IgG-DRG passive-transfer axis (IgG-Mediated Non-Length-Dependent SFN: DRG-Level Autoimmune Mechanism), which has direct experimental support in post-infectious cohorts. (ch09 Anti-TS-HDS / Anti-FGFR3 Serology Is Not a Validated Diagnostic or IVIG-Selection Tool in ME/CFS; cert n/a; severity unknown; origin: /integrate-topic autoimmune-sfn-subtype.)	Phase 3 / n/a
compat note	Compatibility (Phase 7): oq:autoimmune-sfn-serological-subtype and lim:autoimmune-sfn-serology-not-validated are INDEPENDENT of the stronger IgG-DRG passive-transfer axis (IgG-Mediated Non-Length-Dependent SFN: DRG-Level Autoimmune Mechanism) and the ISR-DRG mechanism (ISR Activation in Dorsal Root Ganglia as a Mechanism for Small Fibre Neuropathy in ME/CFS) — the serological TS-HDS/FGFR3 claim is a separate, contested biomarker hypothesis that the content explicitly does NOT conflate with the passive-transfer DRG evidence. They neither reinforce nor conflict with the Sjögren’s autoimmune-SFN CCM open question (Does ME/CFS Share the Sjögren’s Syndrome CCM Pattern, Pointing to an Autoimmune SFN Subtype?) or the fibromyalgia-ME/CFS-POTS triad hypothesis — those address structural/GPCR-autoantibody mechanisms, not the TS-HDS/FGFR3 serological markers. The limitation (lim:autoimmune-sfn-serology-not-validated) does not modify any existing claim’s certainty (both new rows are cert n/a — research question + limitation; no certainty adjustment applicable under Phase-7 rules or Active Caps). No reinforcement, no conflict, no circular dependencies, no feed-in. Compat audit at tmp/compat-audit-autoimmune-sfn-subtype-2026-09-07.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
oq ldn-metformin-phenotype-triage	Open question (ch47 Could LDN vs Metformin Ever Be a Phenotype-Guided First-Line Choice?): could LDN vs metformin ever become a phenotype-guided first-line choice (LDN for neuroimmune/TRPM3-dominant, metformin for immunometabolic)? No validated biomarker selects either drug today, and the two arms are NOT equally evidenced — metformin has been tested in established disease and failed to show symptom benefit (one completed RCT negative (S. Y. Lim et al. 2026); a second adaptive trial stopped its metformin arm for futility (Reis et al. 2026)), whereas LDN has never been tested in a completed placebo-controlled RCT for this endpoint. The best case for metformin is that the failed trials were unselected and did not stratify by insulin resistance — the design that can mask a true subgroup effect; its one positive RCT-level signal (COVID-OUT acute-phase prevention, 41% RRR, 6.3% vs 10.4% by day 300 (Bramante et al. 2023)) arose in a defined early/metabolically-loaded context, and post-infectious insulin resistance is documented in the same trigger population (Sanhueza et al. 2026). The mechanism-specific endpoints (REE, NETosis; the latter has no routine clinical marker) are unverified in ME/CFS — never measured — which is distinct from refuted, and no negative trial has been re-analysed by HOMA-IR subgroup. Falsifiable prediction (joint, two-stage): first an enrichment trial (HOMA-IR-high established ME/CFS, metformin vs placebo, REE + NET/insulin + symptom endpoints); if signal, the fuller TRPM3 x HOMA-IR stratified randomization vs LDN would show a significant phenotype x drug interaction (metformin better in HOMA-IR-high/TRPM3-normal; LDN better in TRPM3-abnormal/insulin-sensitive). Indeterminate if mechanism-endpoint (REE/NETosis) signal appears without symptom improvement — that establishes metabolic modulation without clinical benefit, requiring a separate clinical-endpoint trial rather than declaring falsification. Consequence: neither drug has demonstrated established-disease symptom benefit; for a post-viral-fatigue patient with insulin resistance now the choice is empirical, and metformin must NOT be imported into established ME/CFS on the strength of its acute-phase prevention signal. (ch47 Could LDN vs Metformin Ever Be a Phenotype-Guided First-Line Choice?; cert n/a — research question; severity unknown; origin: /integrate-topic ldn-metformin-bifurcation-triage.)	Phase 3 / n/a
compat note	Compatibility (Phase 7): oq:ldn-metformin-phenotype-triage is INDEPENDENT of (does not modify) the concurrent-adjunct framing Metabolic-Immune Crosstalk in LDN Response (metformin added to preserve LDN benefit in prediabetics — an either/or triage question is a different axis, so no conflict). It is COMPLEMENTARY to the ch33 LDN pharmacodiagnostic differential (low dose naltrexone ldn LDN TLR4/TRPM3 dose-window content) and consistent with the ch06 endotype/subgroup framework. No certainty bump applies (cert n/a — research question; Active Caps forbid bumping above 0.45). Phase 6 (re-run): the metformin efficacy-refuting evidence (Lim2026 negative; REVIVE metformin-arm futility) was surfaced as inline caveats at the two pre-existing metformin candidate-treatment claims — Metformin for Insulin-Driven NETosis Reduction — Research Hypothesis Only (ch31) and Metformin as Metabolic Platform for Weight Stabilization — Research Hypothesis Only (ch30) — per the safety override for inefficacy evidence and visibility symmetry; no certainty reduction applied (the trials measured fatigue/PASC-recovery, a different endpoint from NETosis/REE, a conservative cross-outcome overlap). Compat audit at tmp/compat-audit-ldn-metformin-bifurcation-2026-09-09.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
hyp population-pharmacodiagnostic-corpus	Hypothesis (ch33 Pooled Drug Responses as a Population-Level Constraint-Satisfaction Instrument): a set of uncoordinated single-patient drug trials, pooled, can estimate population and subgroup treatment effects and thereby rank rate-limiting mechanisms in ME/CFS. Statistical feasibility is established in general populations — pooled n-of-1 trials yield group means (Punja et al. 2016) (Zucker, Ruthazer, and Schmid 2010), multi-level Bayesian models combine multiple single-patient trials handling trend/carryover/autocorrelation (Schmid and Yang 2022), and aggregated n-of-1 designs are formalized for predictive-biomarker/responder-subgroup validation (Hendrickson et al. 2020). Applied to ME/CFS it is untested; no such corpus exists. Falsifiability: a corpus constructed from pooled ME/CFS drug-response reports recovers at least one drug-response association independently confirmed by a blinded randomized trial at a rate above chance, and for the minority of drugs with independent trial estimates its population estimates agree with them (ρ > 0.5 across those drugs); falsified if no pooled association replicates in any blinded trial, or if the corpus ranking’s correlation with the trial-based ranking is ρ ≤ 0.5 where both exist. (ch33 Pooled Drug Responses as a Population-Level Constraint-Satisfaction Instrument; cert 0.45 — methodological, general population; severity unknown; origin: /integrate-topic pharmacodiagnostic-corpus.)	Phase 3 / 0.45
spec dose-band-duration-load-bearing	Speculation (ch33 Dose Band and Duration Are the Load-Bearing Fields): the corpus only carries mechanism information if each report captures the dose band and duration of the trial, because a null at the top of a dose range is uninformative about a window below it (Dose-Response Curve Shape as Diagnostic Readout). “LDN did not help” pools to nothing; “LDN 0.5 mg, 8 weeks, fog improved, lost at 3.0 mg” constrains the TLR4/Nrf2 window. Current forum/clinic reports are mostly yes/no and cannot be pooled retrospectively without loss (Bourke et al. 2020). Falsifiability: a corpus recording dose band + duration predicts an independent outcome better than a yes/no corpus for the same drugs (pre-specified margin), and band-specific response rates exceed off-band rates ≥2× for at least one drug; falsified if the binary corpus predicts equally well or if band-specific rates are flat across bands for all drugs examined. (ch33 Dose Band and Duration Are the Load-Bearing Fields; cert 0.40 — design inference; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
spec convergent-aggregation-cancels-confounders	Speculation (ch33 Convergent Aggregation Cancels Independent Confounders — It Does Not Eliminate Them): per-patient confounders (placebo, fluctuation) are independent and cancel in aggregate, but shared bias (notoriety, reporting, self-selection) does not; consistency of a responder-subgroup pattern is not sufficient on its own (a socially amplified false signal is also consistent) and population-level consistency is not necessary (a subgroup signal need not move the population). But independence reduces noise, not systematic bias — notoriety, reporting bias, and self-selection are shared across a community and do not cancel (Cutroneo et al. 2023) (Richiardi et al. 2007). Open-label placebo moves subjective outcomes (Flávio-Reis et al. 2025) (Kleine-Borgmann and Bingel 2025) and unblinded single-case aggregation inflates response (95% favourable under high bias) (Nikles et al. 2022). Falsifiability: the per-drug response variance partitions into an estimable shared component and a mechanism-relevant between-patient component; the mechanism-relevant fraction exceeds zero for some drugs and apparent between-drug differences shrink after adjusting for shared channel and popularity. Falsified if the mechanism-relevant fraction is indistinguishable from zero for all drugs (signal fully explained by shared expectation and channel effects). The verbal claim carries a structural-unfalsifiability flag. (ch33 Convergent Aggregation Cancels Independent Confounders — It Does Not Eliminate Them; cert 0.30 — statistical principle, untested in ME/CFS; severity unknown; origin: brainstorm.)	Phase 5 / 0.30
spec response-heterogeneity-subtypes	Speculation (ch33 Drug-Response Heterogeneity Discloses Subtypes): heterogeneous drug response discloses subtypes — the ARDS HARP-2 case shows two subphenotypes responding differentially to simvastatin, so an overall-null trial contained a real mechanism-revealing differential response (Calfee et al. 2018), and ME/CFS has substantial partially reproducible within-diagnosis heterogeneity (Vaes et al. 2023). A corpus should therefore seek responder subgroups, not a single average. Falsifiability: corpus-derived responder subgroups differ on at least one independently measured objective marker (between-subgroup effect size larger than within-subgroup variability); falsified if the subgroups show no objective separation. (ch33 Drug-Response Heterogeneity Discloses Subtypes; cert 0.42 — cross-disease inference + measured heterogeneity; severity unknown; origin: brainstorm.)	Phase 5 / 0.42
oq corpus-minimum-data-schema	Open question (ch33 The Minimum Data Schema for a Poolable Corpus): what minimum schema makes a drug-response report poolable? Seven candidate fields each have a basis — drug (Eckey et al. 2025) (Frost and Massagli 2009), dose (Bourke et al. 2020), dose band, duration (Kleine-Borgmann and Bingel 2025) (Schmid and Yang 2022), response domain (Calfee et al. 2018) (Vaes et al. 2023), side-effect type Puijenbroek et al. (2002; Cutroneo et al. 2023), stop reason (Micale et al. 2024). One further variable is mandatory to test the null — a per-drug measure of community discussion volume or prescribing popularity; whether the set is sufficient, and what denominator or comparator is needed, is unresolved; disproportionality ranks associations without denominators Puijenbroek et al. (2002) but cannot establish causation (Cutroneo et al. 2023) (Noguchi, Tachi, and Teramachi 2021). (ch33 The Minimum Data Schema for a Poolable Corpus; cert n/a — research question; severity unknown; origin: brainstorm.)	Phase 3 / n/a
lim corpus-cannot-conclude	Limitation (ch33 What the Corpus Cannot Conclude): four structural limits not fixable by scale — (1) cannot confirm mechanism (disproportionality/observational inference generate hypotheses, not causal conclusions (Cutroneo et al. 2023) (Zong et al. 2022)); (2) cannot resolve spectrally identical hypotheses (two mechanisms predicting the same response to every accessible drug are indistinguishable — Pharmacodiagnostic Matrix - Constraint-Satisfaction Inference); (3) cannot overcome unblinded self-report (Flávio-Reis et al. 2025) (Nikles et al. 2022); (4) cannot generalize from respondents to the population (web cohorts self-selected (Richiardi et al. 2007)). A fifth ME/CFS-specific limit: severe patients are least able to contribute (Loth 2026). (ch33 What the Corpus Cannot Conclude; cert 0.60 — each limit established methodologically; severity unknown; origin: /integrate-topic pharmacodiagnostic-corpus.)	Phase 3 / 0.60
prop pharmacodiagnostic-corpus-specification	Proposal (ch33 From Scattered Prescriptions to a Ranked Differential — A Corpus Specification): a minimum viable corpus specification — unit = one drug trial with the seven fields; response coded per domain (not global better/worse); aggregation = multi-level Bayesian pooling with responder-subgroup analysis (Schmid and Yang 2022) (Zucker, Ruthazer, and Schmid 2010) (Hendrickson et al. 2020) (Calfee et al. 2018); output = ranked candidate pathways plus discriminating probes (Pharmacodiagnostic Matrix - Constraint-Satisfaction Inference); governance = published schema/aggregation/reading rules with the limits as standing caveats. Not a claim that a corpus exists. (ch33 From Scattered Prescriptions to a Ranked Differential — A Corpus Specification; cert 0.35 — design proposal from established methods; severity unknown; origin: brainstorm.) Falsifiability: an implementation following this specification yields a per-drug responder ranking with split-half agreement ρ > 0.5, and the top-ranked pathway survives adjustment for indication and treatment popularity (rank correlation > 0.7); falsified if split-half agreement is ≈ 0 or if adjustment materially reshuffles the ranking.	Phase 5 / 0.35
prop patient-drug-response-corpus	Proposal (ch46 A Poolable Drug-Response Corpus): the chapter’s N-of-1 and registry infrastructure already captures much of what a pharmacodiagnostic corpus needs but not in poolable form; minimum fields and multi-level Bayesian aggregation as in From Scattered Prescriptions to a Ranked Differential — A Corpus Specification; direct precedent is the Eckey 2025 ME/CFS + long-COVID treatment survey (n=3,925, >150 treatments) which ranks treatments by perceived benefit but not mechanisms (Eckey et al. 2025). (ch46 A Poolable Drug-Response Corpus; cert 0.35 — design proposal; severity unknown; origin: brainstorm.) Falsifiability: a corpus built to the schema reproduces ≥1 known drug-response association and yields a per-drug ranking with split-half agreement ρ > 0.5 that tracks dose/target rather than community discussion volume; falsified if the ranking is unstable across splits, if no pooled association survives adjustment for indication/popularity, or if it tracks discussion volume rather than pharmacology (Alternative Explanations That Could Explain the Corpus Entirely).	Phase 5 / 0.35
lim corpus-inherits-pgkd-limits	Limitation (ch46 The Corpus Inherits Every Limit of Patient-Generated Knowledge): a corpus built from patient/clinician reports inherits every limit of patient-generated knowledge — self-selection Evidence Quality in Patient-Generated Knowledge Research, community error amplification Community Error Amplification, unblinding (open-label placebo (Flávio-Reis et al. 2025) (Nikles et al. 2022)), and severe-patient under-representation The Severity-Epistemic Gradient. These define it as a hypothesis-generation instrument, not a conclusion. (ch46 The Corpus Inherits Every Limit of Patient-Generated Knowledge; cert n/a — synthesis of documented limits; severity unknown; origin: /integrate-topic pharmacodiagnostic-corpus.)	Phase 3 / n/a
spec pooled-nulls-more-informative	Speculation (ch33 Pooled Nulls May Be More Informative Than Pooled Positives): positive reports are inflated by open-label placebo/enthusiasm/publication bias while null reports appear less inflated by expectation and are mostly absent from the published record — though fluctuation and regression to the mean offset this, so their net-bias advantage is estimated, not assumed — so the corpus’s marginal value may concentrate in the null space. Falsifiability: in a corpus capturing both outcomes with equal recruitment effort, cross-cohort ICC is higher for null than positive reports, compared against the corpus’s own inert-intervention floor (not a bare base rate, since agreement-on-noise can mimic the asymmetry); falsified if positives agree as well or better after that adjustment. (ch33 Pooled Nulls May Be More Informative Than Pooled Positives; cert 0.40; severity unknown; origin: brainstorm. The un-operationalized form — that nulls are ‘more informative’ — is unfalsifiable without the ICC or equivalent metric.)	Phase 5 / 0.40
spec variance-decomposition	Speculation (ch33 Shared-vs-Idiosyncratic Variance Decomposition Makes “Cancellation” Quantitative): partition each drug’s response variance into a shared component (expectation/channel/era) and an idiosyncratic component (patient-specific); within the idiosyncratic component the mechanism-relevant part is the between-patient subgroup structure tracking dose/target/co-variable, separated from unstructured noise. The ratio of mechanism-relevant variance to shared variance is an estimable per-drug quantity governing how much each report counts. Falsifiability: per-drug mechanism-relevant variance fractions predict cross-cohort replication of that drug’s responder pattern (higher fraction → higher replication, ρ > 0.4); falsified if the mechanism-relevant fraction is unrelated to replication, or near zero for all drugs (only shared bias and noise recoverable). (ch33 Shared-vs-Idiosyncratic Variance Decomposition Makes “Cancellation” Quantitative; cert 0.40; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
spec inert-intervention-floor	Speculation (ch33 Mechanistically-Inert Interventions Provide the Corpus’s Internal Placebo Floor): mechanistically-inert interventions form an internal control arm estimating the community’s expectation/recall/fluctuation floor under the corpus’s own conditions; subtracting it converts a raw catalog into an expectation-adjusted one. Supplements are the high-volume stratum (Eckey et al. 2025). Falsifiability: inert interventions pool to a stable non-zero floor of magnitude smaller than targeted drugs’ response, whose subtraction changes the pathway ranking (at least one top-5 pathway changes rank); falsified if the inert floor is zero, equals the targeted-drug magnitude (corpus measures expectation, not mechanism), if subtraction leaves the ranking unchanged, or if the inert stratum’s response is unstable under pre-registration (the floor is hypothesis-space-dependent, not a fixed baseline). (ch33 Mechanistically-Inert Interventions Provide the Corpus’s Internal Placebo Floor; cert 0.40; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
prop hybrid-corpus-nested-nof1	Proposal (ch33 Hybrid Design — A Randomized n-of-1 Sub-Study Inside the Observational Corpus): observational corpus supplies wide hypothesis-generating signal; a nested randomized double-blind placebo-controlled n-of-1 sub-study on the corpus’s top 3–5 drugs supplies causal confirmation. Components established (Punja et al. 2016) (Zucker, Ruthazer, and Schmid 2010) (Schmid and Yang 2022). Falsifiability: top-ranked corpus drug beats placebo in the nested blinded sub-study at a higher rate than a randomly chosen corpus drug; falsified if nomination does not enrich for blinded-confirmed response. (ch33 Hybrid Design — A Randomized n-of-1 Sub-Study Inside the Observational Corpus; cert 0.40; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
prop shared-lc-corpus	Proposal (ch33 A Shared ME/CFS and Long-COVID Corpus Tests Mechanism Overlap Directly): a shared ME/CFS + long-COVID corpus tests mechanism overlap directly — drug-response fingerprints should correlate more within each condition than between them at equal severity. Joint survey exists (Eckey et al. 2025). Falsifiability: ρ_within > ρ_between for severity-matched pairs; falsified if fingerprints indistinguishable across conditions. (ch33 A Shared ME/CFS and Long-COVID Corpus Tests Mechanism Overlap Directly; cert 0.40; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
prop corpus-discovers-probes	Proposal (ch33 The Corpus Can Discover Which Existing Drug Is the Best Diagnostic Probe): rank existing drugs by the empirical narrowness of their responder subgroup (response concentration/entropy) to find high-spectral-resolution probes — data-driven rather than heuristic, avoiding the labeling-bias concern in the expected-response matrix (Hendrickson et al. 2020) (Calfee et al. 2018). Falsifiability: empirical response concentration predicts discriminating power in an independent validation cohort after confounder adjustment; falsified if unrelated or if the concentration disappears after adjustment. (ch33 The Corpus Can Discover Which Existing Drug Is the Best Diagnostic Probe; cert 0.35; severity unknown; origin: brainstorm. Clinician-matrix agreement cannot validate the method because the matrix is the labeling-biased object the proposal avoids.)	Phase 5 / 0.35
lim corpus-alternative-explanations	Limitation (ch33 Alternative Explanations That Could Explain the Corpus Entirely): the corpus’s testable null is that a model using only severity + drug-class popularity/availability + community discussion volume fits the response matrix as well as a model that additionally uses dose and molecular target (making the corpus a treatment-preference catalog like Eckey 2025 (Eckey et al. 2025)). Three specific alternatives — confounding by indication (Zong et al. 2022) (Shahn et al. 2022), community amplification (apparent efficacy tracking discussion volume), and spectral degeneracy of the drug set (many mechanism assignments fit equally) — can each produce the apparent signal without mechanism. Rejection requires recording indication + co-medication, subtracting an internal inert-intervention floor, showing response tracks dose/target rather than discussion volume (pre-registered model comparison), and demonstrating a small set of fitting mechanism assignments (from the volume-adjusted matrix). (ch33 Alternative Explanations That Could Explain the Corpus Entirely; cert 0.50; severity unknown; origin: brainstorm.)	Phase 5 / 0.50
lim corpus-measurement-limits	Limitation (ch33 Measurement, Denominator, Polypharmacy, and Recall Limits): four measurement problems bound precision — (1) unblinded non-standardized outcome (the class open-label placebo moves most (Flávio-Reis et al. 2025) (Kleine-Borgmann and Bingel 2025)) requires a validated instrument; (2) no denominator Puijenbroek et al. (2002); (3) polypharmacy/co-prescription confound (Schmid and Yang 2022); (4) recall and survivor bias in retrospective reports (Bourke et al. 2020) (Richiardi et al. 2007). (ch33 Measurement, Denominator, Polypharmacy, and Recall Limits; cert 0.55; severity unknown; origin: brainstorm.)	Phase 5 / 0.55
lim corpus-selection-bias	Limitation (ch33 Selection Bias Samples the Wrong End of the Severity Gradient): contributors are self-selected (online, English-speaking, mild-to-moderate, resourced), so the severe long-duration patients whose responses would be most informative are least likely to contribute — non-random missingness correlating with the biology being ranked. Fixable only by caregiver-mediated/low-tech reporting by design (Richiardi et al. 2007) The Severity-Epistemic Gradient. (ch33 Selection Bias Samples the Wrong End of the Severity Gradient; cert 0.55; severity unknown; origin: brainstorm.)	Phase 5 / 0.55
syn pharmacodiagnostic-corpus-model	Synthesis (ch33 The Population Corpus as a Hypothesis-Generation Instrument): the corpus is structured as a constraint-satisfaction problem whose statistical feasibility is established in general populations; its data substrate already exists; its most informative signal may sit in null reports; and it cannot confirm mechanism, resolve spectral degeneracy, overcome unblinding, or generalize from self-selected respondents. Consistency is not sufficient (a socially amplified false signal is also consistent) nor necessary (a subgroup signal need not move the population). The test is not calibration alone — if the adjusted residual ranking tracks discussion volume rather than dose/target, the proposal is abandoned. (ch33 The Population Corpus as a Hypothesis-Generation Instrument; cert n/a — synthesis; severity unknown; origin: /integrate-topic pharmacodiagnostic-corpus.)	Phase 10a / n/a
compat note	Compatibility (Phase 7): Pooled Drug Responses as a Population-Level Constraint-Satisfaction Instrument REINFORCES the ch33 single-patient pharmacodiagnostic framework (Differential Diagnostic Algorithm - From Drug Responses to Bottleneck Localization, Pharmacodiagnostic Matrix - Constraint-Satisfaction Inference) and the ch34 constraint-satisfaction matrix (pharmacodiagnostic matrix) — it is the population-scale extension of the same constraint-propagation logic, not a competing claim. It FEEDS INTO the ch46 patient-generated-knowledge chapter (The N-of-1 Paradigm, N-of-1 Limitations, Community Error Amplification, What If Patient Knowledge Has No Unique Epistemic Content?): the corpus is the concrete research use of patient N-of-1 data. It is INDEPENDENT of the pharmacodiagnostic-origin-inference axis (origin elimination) — one ranks mechanisms, the other eliminates origin categories. The limits (What the Corpus Cannot Conclude, The Corpus Inherits Every Limit of Patient-Generated Knowledge) are consistent with N-of-1 Limitations and The Patient Knowledge Ceiling; no conflict. No certainty bumps applied — the corpus claim is a methodological proposal (cert 0.45) whose supporting papers are general-population (discounted below 0.60), below the reinforcement-bump threshold; Active Caps permit normal bump rules but no qualifying incoming evidence exists. Compat audit at tmp/compat-audit-pharmacodiagnostic-corpus-2026-09-10.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
spec eckey-mechanism-ranking-projection	Speculation (ch33 The Eckey 2025 Benefit Ranking Can Be Projected Onto a Mechanism Ranking That Replicates): the Eckey 2025 ME/CFS + long-COVID treatment survey (n=3,925; >150 interventions; public Datasets S1–S3) ranks treatments by perceived benefit (NAS) but not mechanisms (Eckey et al. 2025); mapping each treatment to its known target and projecting the benefit ranking onto the hypothesis space yields a mechanism ranking testable by split-half replication, using drug-set enrichment (Napolitano et al. 2016) (Napolitano et al. 2019) and subgroup-validation machinery (Lipkovich, Dmitrienko, and D’Agostino 2017). Cluster-specific responses already exist (IgG multisystemic; stimulants cognitive). Falsifiability: projected subgroup-specific mechanism ranking has split-half agreement ρ > 0.5 and top pathway survives popularity adjustment; falsified if ranking unstable (ρ ≈ 0), no subgroup ranking survives, or explained entirely by treatment popularity. (ch33 The Eckey 2025 Benefit Ranking Can Be Projected Onto a Mechanism Ranking That Replicates; cert 0.45 — dataset + method exist, projection never performed; severity unknown; origin: /integrate-topic eckey2025-mechanism-reanalysis.)	Phase 3 / 0.45
oq eckey-ranking-reliability-threshold	Open question (ch33 No Reliability Standard Yet Justifies the ρ > 0.5 Replication Threshold): no published standard establishes what split-half rank correlation counts as replication for a patient-reported treatment-benefit ranking projected onto mechanisms; closest machinery is general subgroup validation, which warns unconstrained subgroup rankings overfit (Lipkovich, Dmitrienko, and D’Agostino 2017). Outcome is unblinded/non-standardized (self-reported benefit ≠ objective function (Rekeland et al. 2022); instrument choice changes measured response (Haywood, Collin, and Crawley 2014)) and expectation alone moves this outcome class (Flávio-Reis et al. 2025) (Kleine-Borgmann and Bingel 2025). A second ME/CFS ranking dataset offers an external concordance check but is not a reliability standard (Dorczok et al. 2026). (ch33 No Reliability Standard Yet Justifies the ρ > 0.5 Replication Threshold; cert n/a — research question; severity unknown; origin: /integrate-topic eckey2025-mechanism-reanalysis.)	Phase 3 / n/a
lim eckey-projection-manufactures-ranking	Limitation (ch33 Severity, Popularity, and Many-to-Many Mapping Can Manufacture a Mechanism Ranking): popularity and many-to-many target mapping can manufacture an apparent mechanism ranking without mechanism. Apparent efficacy may track community discussion volume/prescribing ((Simmering, Polgreen, and Polgreen 2014) (Stallion et al. 2026); though web data can also carry genuine signal (Yom-Tov and Gabrilovich 2013)) or a structured placebo network (R.-S. Wang et al. 2017); and one drug maps to several mechanisms (LDN → TRPM3 + TLR4/glia (Löhn and Wirth 2024); autonomic drugs act on distinct targets for one symptom (Mar and Raj 2020) (Hasan et al. 2020)). Eckey authors name self-selection/recall/placebo/polypharmacy. Falsifiability: a severity+popularity+volume model fits the response matrix at least as well as a dose+target model (pre-registered out-of-sample); falsified (ranking supported) if the target-and-dose model wins by a pre-specified margin after floor subtraction. (ch33 Severity, Popularity, and Many-to-Many Mapping Can Manufacture a Mechanism Ranking; cert 0.50 — established threats + documented ME/CFS mapping ambiguity; severity unknown; origin: /integrate-topic eckey2025-mechanism-reanalysis.)	Phase 3 / 0.50
spec eckey-vitamin-c-self-calibration	Speculation (ch33 The Survey’s Vitamin C Reference Arm Makes the Projection Self-Calibrating): Eckey contains an internal low-expectation comparator — oral non-liposomal vitamin C with FDR-adjusted p-values (Eckey et al. 2025) — so every NAS can be expressed as a margin over the survey’s own expectation floor before target mapping, making the projection self-calibrating in a way the parent corpus is not. Double-edged: vitamin C is not obviously mechanistically inert (antioxidant/collagen/immune; cf. (Castro-Marrero et al. 2021)), so it validates itself as a floor only if it behaves statistically like the inert-supplement class, and refutes that use if it tracks dose or a specific domain. Falsifiability: the vitamin-C NAS distribution is statistically indistinguishable from the pooled distribution of independently judged mechanistically-inert supplements (no significant NAS difference after FDR), and subtracting it changes at least one top-5 projected mechanism; falsified if vitamin C separates from the inert stratum or its margin is domain-specific. (ch33 The Survey’s Vitamin C Reference Arm Makes the Projection Self-Calibrating; cert 0.42; severity unknown; origin: brainstorm.)	Phase 5 / 0.42
spec eckey-cluster-discordance	Speculation (ch33 The Discriminating Signal Is Cluster Discordance, Not Average Benefit): averaging each treatment’s NAS across the cohort hides a mechanism rate-limiting in one cluster but irrelevant in another; the higher-information statistic is the between-cluster variance of a treatment’s NAS conditional on its overall NAS. Eckey documents cluster-specific response (IgG 73.3% multisystemic; ADHD stimulants 62.1% cognitive but non-significant multisystemic) (Eckey et al. 2025); ARDS HARP-2 shows a real differential response inside an overall-null trial (Calfee et al. 2018); ME/CFS shows within-diagnosis heterogeneity (Vaes et al. 2023). Because drug-set enrichment assumes drugs sharing a target cohere (Napolitano et al. 2016) while ME/CFS drug→target assignment is many-to-many (Löhn and Wirth 2024), dose-response geometry (NAS-vs-dose-band shape) is an alternative identifier that does not trust target labels. Falsifiability: a discordance-weighted mechanism ranking replicates across random cohort halves (ρ > 0.5) while the mean-weighted ranking does not, or the two differ by ≥1 top-5 mechanism; falsified if identical or if discordance fails to replicate better than the mean. (ch33 The Discriminating Signal Is Cluster Discordance, Not Average Benefit; cert 0.38; severity unknown; origin: brainstorm.)	Phase 5 / 0.38
spec eckey-null-projection	Speculation (ch33 Null-Projection Ranks Target Engagement Without Benefit): the proposal projects only positive NAS values onto targets, but a null-projection yields negative constraints — treatments whose target is documented and engaged (by dose/pharmacodynamics) while NAS sits at/near the vitamin C floor identify mechanisms repeatedly engaged without moving symptoms, i.e. non-rate-limiting pathways Pooled Nulls May Be More Informative Than Pooled Positives. The design is circular unless “target engaged” is defined from dose/target, never from benefit: many-to-many assignment (Löhn and Wirth 2024) and target-distinct autonomic drugs for one symptom (Mar and Raj 2020) (Hasan et al. 2020) both smuggle benefit back in. Falsifiability: for ≥1 documented target, treatments mapping to it cluster at/near the vitamin-C floor across clusters while target-engagement markers (dose, side-effect signature) are present; falsified if target-engagement and NAS are confounded by construction (no drug has an established target and a null NAS), making the null-projection uncomputable. (ch33 Null-Projection Ranks Target Engagement Without Benefit; cert 0.33; severity unknown; origin: brainstorm.)	Phase 5 / 0.33
spec eckey-cross-condition-gap	Speculation (ch33 The ME/CFS–Long-COVID Rank Gap Localizes Condition-Specific Mechanisms): Eckey’s two populations are not identically ranked (ME/CFS vs long-COVID NAS values share R²=0.68 of variance) (Eckey et al. 2025); reading the projection per condition, concordant mechanisms are candidate shared mechanisms and divergent ones candidate condition-specific mechanisms (Vaes et al. 2023) (Calfee et al. 2018). The R²=0.68 is a descriptive treatment-level quantity, not a null distribution: using it as a mechanism-level rank-gap baseline is an assumption, not a derived result (no sampling model bridges the two levels; unadjusted for severity distribution; high concordance is equally consistent with shared popularity). The proposal becomes a real test only with a severity-matched comparison and a permutation-derived rank-gap null. Falsifiability: after severity-matched per-condition projection, ≥1 mechanism’s cross-condition rank gap exceeds a permutation-derived null (not the raw R²=0.68) and its discordance replicates across random halves; falsified if all projected rankings are no more divergent than the derived null predicts, or diverging mechanisms fail to replicate. (ch33 The ME/CFS–Long-COVID Rank Gap Localizes Condition-Specific Mechanisms; cert 0.40; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
oq eckey-validation-design	Open question (ch33 What Validation Design Would Reject the Null?): the projection has never been applied to patient-reported benefit, so its null — that the ranking carries no mechanism beyond popularity and severity — can be rejected only by a pre-specified design: (a) validate the pipeline on synthetic ground truth (simulate a known mechanism ranking and recover it), (b) use the structured placebo network as a negative-control arm (R.-S. Wang et al. 2017), (c) derive the replication threshold by permutation rather than the ρ>0.5 convention, (d) build the popularity covariate from the survey instrument itself, (e) run an instrument-invariance pre-check against an independent ME/CFS survey (Dorczok et al. 2026) before any concordance claim. Subgroup-validation machinery (Lipkovich, Dmitrienko, and D’Agostino 2017) supplies the multiplicity control. (ch33 What Validation Design Would Reject the Null?; cert n/a — research question; severity unknown; origin: brainstorm.)	Phase 5 / n/a
compat note eckey	Compatibility (Phase 7): the Eckey re-analysis cluster REINFORCES Pooled Drug Responses as a Population-Level Constraint-Satisfaction Instrument (a single-dataset execution of the ranking thesis), Mechanistically-Inert Interventions Provide the Corpus’s Internal Placebo Floor (The Survey’s Vitamin C Reference Arm Makes the Projection Self-Calibrating is its within-dataset instance), Drug-Response Heterogeneity Discloses Subtypes and The Corpus Can Discover Which Existing Drug Is the Best Diagnostic Probe (The Discriminating Signal Is Cluster Discordance, Not Average Benefit), Pooled Nulls May Be More Informative Than Pooled Positives (Null-Projection Ranks Target Engagement Without Benefit), A Shared ME/CFS and Long-COVID Corpus Tests Mechanism Overlap Directly (The ME/CFS–Long-COVID Rank Gap Localizes Condition-Specific Mechanisms; shares the R²=0.68 baseline), and Alternative Explanations That Could Explain the Corpus Entirely / Measurement, Denominator, Polypharmacy, and Recall Limits (Severity, Popularity, and Many-to-Many Mapping Can Manufacture a Mechanism Ranking). It FEEDS INTO From Scattered Prescriptions to a Ranked Differential — A Corpus Specification, A Poolable Drug-Response Corpus, The Minimum Data Schema for a Poolable Corpus, and Hybrid Design — A Randomized n-of-1 Sub-Study Inside the Observational Corpus via What Validation Design Would Reject the Null?. No conflicts; the only internal tension (Null-Projection Ranks Target Engagement Without Benefit vs. Severity, Popularity, and Many-to-Many Mapping Can Manufacture a Mechanism Ranking) is resolved by requiring engagement to be defined from dose/target, not benefit. No certainty adjustments — incoming evidence is methodological (discounted below 0.60), below the bump threshold; Active Caps cap 0.45 not crossed. Compat audit at tmp/compat-audit-eckey2025-mechanism-reanalysis-2026-09-10.md.	Phase 7 / n/a
syn eckey-mechanism-reanalysis-model	Synthesis (ch33 The Eckey Survey as a Feasibility Test of the Corpus Thesis): the Eckey 2025 survey is the corpus thesis’s first executable test on existing data — it carries an internal expectation floor (vitamin C reference), supplies two internal tests (cluster discordance; ME/CFS-vs-LC localization), and has both positive and null reports for negative constraints. What it lacks is a basis for belief before validation: no split-half reliability standard for this ranking class, and popularity / many-to-many mapping / target-ontology dependence can each manufacture a ranking. The unanswered question is not whether the projection can run but whether a pre-registered design makes its output rejectable. Cross-refs The Eckey 2025 Benefit Ranking Can Be Projected Onto a Mechanism Ranking That Replicates The Survey’s Vitamin C Reference Arm Makes the Projection Self-Calibrating The Discriminating Signal Is Cluster Discordance, Not Average Benefit Null-Projection Ranks Target Engagement Without Benefit The ME/CFS–Long-COVID Rank Gap Localizes Condition-Specific Mechanisms No Reliability Standard Yet Justifies the ρ > 0.5 Replication Threshold What Validation Design Would Reject the Null? Severity, Popularity, and Many-to-Many Mapping Can Manufacture a Mechanism Ranking. (ch33 The Eckey Survey as a Feasibility Test of the Corpus Thesis; cert n/a — synthesis; severity unknown; origin: /integrate-topic eckey2025-mechanism-reanalysis.)	Phase 10a / n/a

ID / Label	Details	Phase / Cert
hyp shared-neuroimmune-fatigue-programme	Hypothesis (ch16 A Convergent Neuroimmune Programme Underlies Fatigue Across Immune-Inflammatory Diseases): pathological fatigue across immune-inflammatory diseases may reflect, in part, a shared neuroimmune programme (sickness-behaviour cytokine-to-brain signalling) rather than many unrelated disease-specific fatigues. Prevalence estimates are broadly similar but span a wide range ($~42$–90%), a range also compatible with generic chronic-illness/questionnaire effects; the estimates are questionnaire-based and not immune-specific: MS $~80$% (Khan, Amatya, and Galea 2014), SLE 67–90% (Kawka et al. 2021), axSpA $~56$% (H. Tang, Singh, and Fong 2023), PsA $~51$% (H. Tang, Chew, and Fong 2025), IBD 47% (72% active) (D’Silva et al. 2022), MG 42–82% (Ruiter, Verschuuren, and Tannemaat 2020), PBC $>$ 50% (Lynch et al. 2022), cancer 49% (62% on treatment) (Al Maqbali et al. 2021). Human mechanistic support: ICI therapy — 58.5% of 53 patients developed worsening fatigue, distinguished by cytotoxic effector CD8+ expansion and stronger Th1 (H. L. Li et al. 2025) (confounded by ICI endocrinopathies/anaemia, not excluded); Dubbo cohort — EBV/Coxiella/Ross River each → $~11$% post-infectious chronic fatigue at 6 months (Hickie et al. 2006b). Unifying reviews (Morris et al. 2016) (Arnett and Clark 2012). Falsifiability: a pre-registered harmonized cross-disease cohort measuring the same cytokine-to-brain markers (IL-6/IL-1β/TNF-α, kynurenine:tryptophan, central-motivational proxy) shows fatigue severity tracking the neuroimmune panel better than disease-activity markers across diseases; falsified if disease-activity markers predict fatigue equally well or better across all conditions. (ch16 A Convergent Neuroimmune Programme Underlies Fatigue Across Immune-Inflammatory Diseases; cert 0.55 — cross-disease prevalence well-replicated, shared-programme interpretation untested as one hypothesis; severity unknown; origin: /integrate-topic common-fatigue-biology.)	Phase 3 / 0.55
spec immune-metabolic-energy-redistribution	Speculation (ch16 Immune-Metabolic Energy Redistribution: “Selfish Brain” vs. “Selfish Immune System”): persistent immune activation imposes a metabolic demand (activated T cells increase glucose uptake, glycolysis, protein synthesis, proliferation (Fenckova 2024)) that may compete with brain and muscle for substrate — a “selfish brain” vs “selfish immune system” allocation conflict. Direct support is acute-model: LPS lowered blood and CSF glucose and suppressed spontaneous activity in mice, partially rescued by glucose (Kealy et al. 2020); human evidence is impaired brain glucose utilization in an inflammation-associated delirium cohort (n=406) (Titlestad et al. 2024). Translation gap: the causal glucose-substrate experiment is in mice; the human data are delirium, not ME/CFS; no study has measured an immune-metabolic allocation conflict in ME/CFS or Long COVID. Competing explanation: the human ICI correlate is immune-activation level (Th1/effector CD8), not measured substrate (H. L. Li et al. 2025) — cytokines may act directly on motivational circuits without any glucose shortfall; the two accounts are indistinguishable with current data. Falsifiability: in a prospective ME/CFS/post-infectious cohort, immune-metabolic demand markers (activated-T-cell glycolytic flux, glucose turnover) inversely track brain/muscle substrate availability (brain glucose or high-energy phosphate imaging, muscle PCr recovery) and correlate with fatigue severity; falsified if demand markers are unrelated to substrate availability, or fatigue tracks only direct cytokine signalling with no substrate change. (ch16 Immune-Metabolic Energy Redistribution: “Selfish Brain” vs. “Selfish Immune System”; cert 0.33 — mouse + human-CSF, translation gap, single-programme dependency; severity unknown; origin: source thread / brainstorm.)	Phase 3 / 0.33
oq fatigue-heterogeneity-or-single-entity	Open question (ch16 Is Fatigue One Entity or Several Mechanism-Defined Subtypes?): should pathological fatigue be treated as one entity on a 0–10 severity scale, or as several mechanistically distinct subtypes? Heterogeneity evidence: MG distinguishes objective fatigability from subjective fatigue (Ruiter, Verschuuren, and Tannemaat 2020); PBC adds an autonomic axis (40% vs 6% orthostatic; independently associated with fatigue+cognition) (Newton et al. 2007); fatigue is dissociated from disease activity in SLE (Kawka et al. 2021), sarcoidosis Kleijn et al. (2009), SSc (Basta, Afeltra, and Margiotta 2018); it persists in AAV remission (Harper et al. 2021). Suggests multiple maintenance mechanisms (cytokine-driven, autonomic, central-motivational, bioenergetic). Alternative: apparent heterogeneity could be measurement heterogeneity (different instruments/thresholds). Testable now with existing assays (cytokine panels, CD8 immunophenotyping, tilt/QSART, metabolomics). Falsifiability: biologically defined subtypes (≥ immune-inflammatory, autonomic, central-motivational) separate in an unsupervised multi-modal cross-disease analysis and each predict a distinct treatment response; falsified if fatigue maps onto a single dominant dimension with no separable mechanistic clusters. (ch16 Is Fatigue One Entity or Several Mechanism-Defined Subtypes?; cert n/a — research question; severity unknown; origin: /integrate-topic common-fatigue-biology.)	Phase 3 / n/a
lim inflammation-necessary-not-sufficient	Limitation (ch16 Inflammation Is Not Sufficient for Fatigue — and Its Necessity Is Unproven): the shared-neuroimmune-programme account does not explain why fatigue severity is often dissociated from disease activity — fatigue persists in AAV remission (Harper et al. 2021), correlates poorly with activity in SLE (Kawka et al. 2021), and can remain when sarcoidosis organ inflammation is controlled Kleijn et al. (2009); conversely glucocorticoid treatment in sarcoidosis was associated with more fatigue (observational; indication-confounded) Kleijn et al. (2009). Implication: the dissociation evidence establishes that immune activation is not sufficient for persistent fatigue; necessity is unproven, since an inflammation-independent mechanism could sustain fatigue without any active inflammatory contribution. A second maintenance mechanism (autonomic, central-motivational, or bioenergetic) must sustain persistent fatigue — exactly the situation in ME/CFS, where fatigue persists long after the trigger resolves. One cross-disease candidate is the autonomic/orthostatic axis (PBC 40% vs 6%, independently associated with fatigue+cognition (Newton et al. 2007)), consistent with shared autonomic dysregulation Three Axes for Classifying the Contested Diagnosis Cluster. (ch16 Inflammation Is Not Sufficient for Fatigue — and Its Necessity Is Unproven; cert n/a — limitation; severity unknown; origin: /integrate-topic common-fatigue-biology.)	Phase 3 / n/a
spec fatigue-tracks-immune-kinetics	Speculation (ch16 Fatigue May Track the Kinetics of Immune Activation, Not Its Tonic Level): a different resolution of the dissociation problem is temporal — fatigue may track the rate of change of immune activation rather than its steady-state level, because sustained cytokines induce tolerance (receptor desensitisation/negative feedback) while a rising state does not (Fenckova 2024). This would explain why fatigue is prominent during acute activation (ICI cohort 58.5% worsening with effector-CD8/Th1 expansion (H. L. Li et al. 2025)) yet dissociates from stable disease-activity measures in chronic disease (SLE (Kawka et al. 2021), sarcoidosis Kleijn et al. (2009), SSc (Basta, Afeltra, and Margiotta 2018), AAV remission (Harper et al. 2021)). Competing explanation: subtype-mixing (Is Fatigue One Entity or Several Mechanism-Defined Subtypes?) or a second inflammation-independent mechanism (Inflammation Is Not Sufficient for Fatigue — and Its Necessity Is Unproven). Translation gap: kinetic inference from cross-sectional/acute-phase data; no rate-of-change-vs-fatigue-trajectory study exists. Falsifiability: in a longitudinal cohort, within-individual fatigue severity correlates more strongly with the first difference of the immune-activation panel than with its absolute level; falsified if fatigue tracks the tonic level as well or better. (ch16 Fatigue May Track the Kinetics of Immune Activation, Not Its Tonic Level; cert 0.33 — single cross-sectional ICI source, no kinetic measurement in ME/CFS; severity unknown; origin: brainstorm.)	Phase 5 / 0.33
compat note common-fatigue-biology	Compatibility (Phase 7): A Convergent Neuroimmune Programme Underlies Fatigue Across Immune-Inflammatory Diseases REINFORCES Shared Neuroimmune Substrate Linking Long Covid, AD(H)D, and ME/CFS Executive Dysfunction (kynurenine-to-dopamine axis is a specific instance of the cytokine-to-brain programme) and the ch08 neuroinflammation subsection (Hundreds of Blood Biomarkers Distinguish ME/CFS, Independent of Inactivity). Is Fatigue One Entity or Several Mechanism-Defined Subtypes? REINFORCES the ch16 three-axis framework (Three Axes for Classifying the Contested Diagnosis Cluster) — mechanism-defined subtypes are the same argument applied to fatigue specifically — and the ch03 fatigue cluster entries (Fatigue Is Embedded Within Coherent Multi-System Symptom Clusters, Fatigue and Fatigability Are Distinct Constructs, Do Subjective Fatigue and Objective Fatigability Dissociate?). Immune-Metabolic Energy Redistribution: “Selfish Brain” vs. “Selfish Immune System” FEEDS INTO the ch07 energy-metabolism chapter and the ch33/34 pharmacodiagnostic corpus’s mechanism-ranking space (The Eckey 2025 Benefit Ranking Can Be Projected Onto a Mechanism Ranking That Replicates). Inflammation Is Not Sufficient for Fatigue — and Its Necessity Is Unproven is consistent with the ch06/ch15 fatigue-vs-CNS-inflammation plan and Neuroinflammation Measurement Challenges. No conflicts; no certainty bumps (incoming cross-disease evidence is other-population, discounted below the 0.60 bump threshold; no ME/CFS-cohort paper was added). Compat audit at tmp/compat-audit-common-fatigue-biology-2026-09-13.md.	Phase 7 / n/a
syn common-fatigue-biology-model	Synthesis (ch16 Fatigue Is Convergent in Prevalence but Plural in Mechanism): fatigue prevalence is convergent across 15 immune-inflammatory diseases ($~50$–90%) and defined-immune-stimulus human models (ICI 58.5%; Dubbo $~11$% at 6 months) show immune activation can produce fatigue A Convergent Neuroimmune Programme Underlies Fatigue Across Immune-Inflammatory Diseases; but the relationship to disease activity is not convergent — dissociation in SLE/sarcoidosis/SSc and persistence in AAV remission mean inflammation is not sufficient for persistent fatigue (and necessity is unproven) Inflammation Is Not Sufficient for Fatigue — and Its Necessity Is Unproven. Two non-exclusive resolutions remain open: fatigue tracks immune kinetics Fatigue May Track the Kinetics of Immune Activation, Not Its Tonic Level, or pooled signal averages distinct subtypes Is Fatigue One Entity or Several Mechanism-Defined Subtypes?. The immunometabolic allocation-conflict account has mouse/delirium proof-of-principle only and competes with a direct cytokine-to-motivation account Immune-Metabolic Energy Redistribution: “Selfish Brain” vs. “Selfish Immune System”. The synthesis rests on cross-disease/model evidence; no ME/CFS cohort has tested the shared programme, so its application to ME/CFS remains an analogy. (ch16 Fatigue Is Convergent in Prevalence but Plural in Mechanism; cert n/a — synthesis; severity unknown; origin: /integrate-topic common-fatigue-biology.)	Phase 10a / n/a

ID / Label	Details	Phase / Cert
spec stimulant-arousal-reward-not-attention	Speculation (ch33 Stimulant benefit in ME/CFS is consistent with low arousal/reward salience, not an attention deficit): large-sample human evidence (ABCD n=11,875 + controlled methylphenidate challenge) localizes therapeutic stimulant action to arousal (somato-cognitive action/motor) and reward-salience (salience/parietal-memory) networks, with no effect in dorsal/ventral attention or frontoparietal control networks (B. P. Kay et al. 2025); concordant meta-analyses show no reliable objective cognitive enhancement in rested healthy adults (Ilieva, Hook, and Farah 2015) (C. A. Roberts et al. 2020) (Marraccini et al. 2016) (Repantis et al. 2010), and amphetamines raise willingness to invest effort without improving ability (Chong, Fortunato, and Bellgrove 2023). Read through this, a positive stimulant response in ME/CFS is consistent with low arousal/reward-effort salience rather than a restored attention capacity (the site of drug action constrains what changes, not what was broken); the underlying energy deficit that drives objective impairment is untouched, mechanistically explaining the masking/overexertion hazard (stimulants raise effort level, lower effort quality (Bowman et al. 2023); survey +77.1% brain fog but −1.5% PEM (Eckey et al. 2025)). Competing mechanism: task-fMRI meta-analysis reports right inferior frontal/insular (cognitive-control) activation increases (Rubia et al. 2014) — a design/sample difference rather than a proven contradiction. Moderator caveat: Kay’s objective-cognition benefit appeared in depleted (ADHD/sleep-deprived) participants, a state ME/CFS may resemble, so the attention-capacity null may not transfer to an energy-depleted brain. Translation gap: no ME/CFS-cohort test; cross-disease (general population + ADHD). Falsifiability: an ME/CFS stimulant challenge should show subjective arousal/effort-reward benefit without a specific cognitive-control improvement (the Rubia prediction is the reverse: a specific cognitive-control improvement with an appropriate baseline deficit); disconfirmed by the Rubia pattern, by benefit scaling with objective attention improvement rather than subjective arousal, or by benefit being non-selective across all patients. Note the attention-capacity claim is bounded to rested brains — it may not hold in ME/CFS if ME/CFS resembles Kay’s depleted subgroup. Consequence: a clinician should read a good stimulant response as a probe of arousal/reward, not as proof of an attention defect, because the drug can raise effort without restoring capacity — mechanistically explaining why it can mask fatigue and enable overexertion. (ch33 Stimulant benefit in ME/CFS is consistent with low arousal/reward salience, not an attention deficit; cert 0.60 — raw 0.80 general-population/ADHD discounted; severity unknown; origin: /integrate-topic kay2025-stimulant-arousal-reward.)	Phase 3 / 0.60
oq stimulant-arousal-vs-cognitive-control	Open question (ch33 Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS?): for ME/CFS, does a positive stimulant response index low arousal/reward salience (Kay 2025 (B. P. Kay et al. 2025)) or a cognitive-control circuit idling below capacity (Rubia 2014 (Rubia et al. 2014))? The discounted certainties differ by ~0.10 (0.60 vs 0.49), within measurement uncertainty, and Kay attributes the discrepancy to design (large brain-wide association vs small task-fMRI ROI studies), but the two designs measure different things (resting-state connectivity vs task-evoked control), so it is not fully adjudicated. The two accounts predict different differential inferences from a positive response. Falsifiability: an ME/CFS stimulant challenge with simultaneous subjective arousal/reward scales and an objective cognitive-control task distinguishes them — arousal/reward benefit without cognitive-control improvement supports Kay; a specific cognitive-control improvement with an appropriate baseline deficit supports Rubia. Consequence: resolving this decides whether a positive stimulant response is a reward/arousal probe or evidence of an under-driven cognitive-control circuit — changing which upstream mechanism is targeted next. (ch33 Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS?; cert n/a — research question; severity unknown; origin: /integrate-topic kay2025-stimulant-arousal-reward.)	Phase 3 / n/a
spec stimulant-endophenotypes-arousal-reward	Speculation (ch33 A positive stimulant response may fractionate into arousal-deficient and reward-effort-deficient subtypes): the two stimulant systems (noradrenergic arousal; dopaminergic reward-effort salience (B. P. Kay et al. 2025)) suggest a positive response in ME/CFS may have two compositions (though Kay found two correlated networks, not demonstrated separable systems): an arousal-dominant phenotype (wakefulness/alertness gain) predicted to prefer arousal-selective agents (modafinil/armodafinil/pitolisant (Repantis et al. 2010)) versus a reward-effort-dominant phenotype (drive/initiation gain) predicted to prefer methylphenidate (Chong, Fortunato, and Bellgrove 2023) (Bowman et al. 2023). Adrenergic dysfunction in ME/CFS/FM (Hendrix et al. 2025) supplies a plausible arousal substrate; direct ME/CFS reward-motivation data are absent. Competing explanation: a single non-specific “feeling better” factor, with differences dose/side-effect driven. Translation gap: no ME/CFS cross-over study. Falsifiability: requires a baseline classifier independent of drug response; given one, arousal-classified patients respond to arousal-selective agents and reward-classified patients to methylphenidate on a pre-specified within-patient outcome; non-selective benefit, or no baseline classifier predicting the matched drug, falsifies separability. Consequence: if real, drug choice could follow the dominant symptom (wakefulness vs drive) rather than treating all responders identically. (ch33 A positive stimulant response may fractionate into arousal-deficient and reward-effort-deficient subtypes; cert 0.40 — raw 0.53 general-population/ADHD discounted; severity unknown; origin: brainstorm.)	Phase 5 / 0.40
syn kay-stimulant-arousal-reward-probe	Synthesis (ch33 Stimulants as arousal/reward probes, not attention tests): convergent human evidence places stimulant action on arousal and reward-salience networks, not attention-control networks (B. P. Kay et al. 2025), with no objective cognitive enhancement in rested adults (Ilieva, Hook, and Farah 2015) (C. A. Roberts et al. 2020) and effort-invigoration without ability gains (Chong, Fortunato, and Bellgrove 2023) (Bowman et al. 2023). Read through ME/CFS, a positive response is better read as an arousal/reward-effort signal (Stimulant benefit in ME/CFS is consistent with low arousal/reward salience, not an attention deficit) than as proof of a broken attention system, and a response may fractionate into arousal-deficient vs reward-effort-deficient subtypes (A positive stimulant response may fractionate into arousal-deficient and reward-effort-deficient subtypes) — hypothesis-generating only, lacking an independent classifier, against an unresolved cognitive-control competitor Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS?. Cross-refs Stimulant benefit in ME/CFS is consistent with low arousal/reward salience, not an attention deficit Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS? A positive stimulant response may fractionate into arousal-deficient and reward-effort-deficient subtypes. Consequence: stimulants may serve as probes of whether a patient’s problem is feeling awake or feeling motivated, but they are not evidence of an attention defect. (ch33 Stimulants as arousal/reward probes, not attention tests; cert n/a — synthesis; severity unknown; origin: /integrate-topic kay2025-stimulant-arousal-reward.)	Phase 10a / n/a
compat note kay2025-stimulant-arousal-reward	Compatibility (Phase 7): Stimulant benefit in ME/CFS is consistent with low arousal/reward salience, not an attention deficit REINFORCES the ch07 PFC energy-failure framework (Synthesis; Prefrontal Energy Failure as the Unifying Substrate of ADHD-Like Executive Dysfunction in ME/CFS) by refining what the stimulant signal means, and REINFORCES ADHD and ME/CFS as Same-Root Etiology — Inflammation-Driven Energy Failure — the same low-arousal/reward-effort mechanism applies in both conditions. It is CONSISTENT WITH the existing ch33 stimulant differential content (cns stimulants) and the masking/overexertion warning. It FEEDS INTO Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS? as a possible ME/CFS probe of reward-effort motivation. The endophenotype speculation A positive stimulant response may fractionate into arousal-deficient and reward-effort-deficient subtypes FEEDS INTO Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS? and the ch33 stimulant drug-selection content. No conflict: the Rubia cognitive-control account is retained as Does the stimulant-response signal localize arousal/reward or cognitive control in ME/CFS?. No certainty bumps — incoming evidence is general-population/ADHD (discounted 0.60), and the new claim is itself the adjusted value; the ch07 synthesis is not bumped because the incoming evidence is cross-disease, not ME/CFS-specific. Compat audit at tmp/compat-audit-kay2025-stimulant-arousal-reward-2026-09-13.md.	Phase 7 / n/a

ID / Label	Details	Phase / Cert
spec lactate-signaling-mecfs	Speculation (ch07 Elevated Lactate in ME/CFS May Signal Through GPR81/HCAR1): elevated lactate in ME/CFS may act as a signal through GPR81/HCAR1 (Gi-coupled; adipose antilipolysis (Ahmed et al. 2010); innate-immune suppression (Hoque et al. 2014); macrophage polarization (Colegio et al. 2014); CD4+ T-cell rewiring (Pucino et al. 2019); brain plasticity (J. Yang et al. 2014); cerebrovascular (Morland et al. 2017)), transport-gated by MCT1–4 (Halestrap and Wilson 2012) — reframing lactate from a waste marker to a candidate signal. Translation gap: all evidence animal/in vitro; no ME/CFS GPR81/HCAR1 measurement. Competing: lactate may be inert; the MRS elevation is compartment-specific (brain, not resting calf-muscle) (Godlewska et al. 2025). Falsifiability: GPR81/HCAR1 expression or lactate-stimulated signaling differs in ME/CFS PBMC/muscle vs controls and correlates with PEM severity; falsified if no difference or difference explained by ligand concentration alone. (ch07 Elevated Lactate in ME/CFS May Signal Through GPR81/HCAR1; cert 0.35 — raw 0.70 animal/in vitro discounted ×0.50; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / 0.35
spec lactate-mct-transport-node	Speculation (ch07 Monocarboxylate Transporter Capacity as a Candidate Rate-Limiting Node): reduced MCT1–4 (SLC16A) expression or proton-coupled transport capacity could make lactate clearance, not production, the rate-limiting step in ME/CFS, raising local/systemic lactate at unchanged glycolytic rate; the astrocyte–neuron lactate shuttle depends on this transport step (Halestrap and Wilson 2012) (Magistretti and Allaman 2018). Translation gap: MCT expression/kinetics unmeasured in ME/CFS. Competing: slower clearance could reflect perfusion/oxidation, not transport. Falsifiability: reduced MCT1/MCT4 expression or transport flux in ME/CFS vs controls at matched circulating lactate, correlating with delayed post-exertional clearance; falsified if transport normal or delay fully accounted for by perfusion/oxidation. (ch07 Monocarboxylate Transporter Capacity as a Candidate Rate-Limiting Node; cert 0.30 — general biology discounted, ME/CFS link untested; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / 0.30
oq lactate-signaling-mecfs	Open question (ch07 Does Dysregulated Lactate Signaling Contribute to ME/CFS Symptoms?): does lactate acting as a signal contribute to ME/CFS symptoms, and through which tissue — immune (GPR81 innate-immune suppression (Hoque et al. 2014), macrophage polarization (Colegio et al. 2014), CD4+ T-cell rewiring (Pucino et al. 2019)), adipose/metabolic (antilipolysis (Ahmed et al. 2010)), or central (NMDA plasticity (J. Yang et al. 2014), cerebrovascular (Morland et al. 2017))? Unresolvable because neither GPR81/HCAR1 expression nor lactate-stimulated signaling is measured in ME/CFS. (ch07 Does Dysregulated Lactate Signaling Contribute to ME/CFS Symptoms?; cert n/a — research question; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / n/a
lim lactate-signaling-unmeasured	Limitation (ch07 The GPR81/HCAR1 Axis Is Unmeasured in ME/CFS): the lactate-signaling interpretation rests entirely on model-system evidence; GPR81/HCAR1, MCT1–4, and lactate-stimulated signaling are unmeasured in ME/CFS. Lactate elevation is not uniform (7T MRS shows a compartment-specific brain elevation while resting calf-muscle metabolites did not differ (Godlewska et al. 2025)), so the assumed ligand may be present in only a subset and its tissue distribution is unmeasured. Largest evidence gap for the topic. (ch07 The GPR81/HCAR1 Axis Is Unmeasured in ME/CFS; cert n/a — limitation; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / n/a
spec lactate-lowering-strategies	Speculation (ch31 Cofactor and Substrate Strategies May Lower Lactate in ME/CFS): four mechanism-distinct routes may lower/bypass the lactate burden — thiamine (PDH cofactor; reduced fatigue vs placebo in IBD (Bager et al. 2021)), carnitine (fatty-acid oxidation; serum acylcarnitine low in ME/CFS (Kuratsune et al. 1998)), MCT/ketogenic substrate shift ((Colgan et al. 2026)), D-ribose ATP substrate ((Teitelbaum, Johnson, and St Cyr 2006)). No ME/CFS trial measures lactate as an endpoint or shows symptom benefit; not a recommendation. Falsifiability: an ME/CFS trial of a cofactor/substrate intervention that lowers post-exertional venous lactate vs control shows lactate reduction correlating with symptom improvement; falsified if lactate falls without symptom change or no tolerated dose lowers ME/CFS lactate. (ch31 Cofactor and Substrate Strategies May Lower Lactate in ME/CFS; cert 0.30 — different-disease RCT + low-tier pilots discounted; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / 0.30
lim bicarbonate-buffering-unsupported	Limitation (ch31 Buffering Lactate with Bicarbonate Is Not Supported): neutralizing lactate with sodium bicarbonate is not supported — it does not raise intracellular pH, may worsen hemodynamics, and lacks demonstrated benefit in lactic acidosis (Forsythe and Schmidt 2000); it treats the marker, not the mechanism, and is the competing/harm arm for the buffer-the-lactate family. (ch31 Buffering Lactate with Bicarbonate Is Not Supported; cert 0.50 against efficacy; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 3 / 0.50
oq lactate-clearance-kinetics-biomarker	Open question (ch07 Post-Exertional Lactate Kinetics as a Monitoring Biomarker): post-exertional lactate kinetics (peak + clearance rate, via submaximal 2-day CPET or at-home finger-prick) — not the compartment-specific brain elevation reported on MRS (Godlewska et al. 2025) — may separate ME/CFS from activity-matched controls and track treatment response; untested. (ch07 Post-Exertional Lactate Kinetics as a Monitoring Biomarker; cert n/a — research question; severity unknown; origin: brainstorm (idea 9.1).)	Phase 5 / n/a
oq lactate-axis-measurement-agenda	Open question (ch07 Measure the GPR81/HCAR1 and MCT Axis in a Severity-Stratified ME/CFS Cohort): a severity-stratified cohort measuring GPR81/HCAR1 + MCT1–4 expression/signaling in PBMC/muscle, with post-exertional lactate kinetics as co-primary endpoint, is the single measurement that would convert the signaling arm from model-system speculation to patient data and could subtype patients. (ch07 Measure the GPR81/HCAR1 and MCT Axis in a Severity-Stratified ME/CFS Cohort; cert n/a — research question; severity unknown; origin: brainstorm (ideas 2.1, 2.2).)	Phase 5 / n/a
spec lactate-clearance-levers	Speculation (ch32 Perfusion- and Timing-Directed Lactate Clearance): perfusion support (hydration/blood-volume + gentle heat; cold/shivering counterproductive) and a sub-threshold active-recovery window could accelerate lactate clearance without crossing the PEM threshold (Brooks 2018) (Magistretti and Allaman 2018) (Haunhorst et al. 2025); untested with lactate as an endpoint; research-stage, not a recommendation. Falsifiability: a controlled protocol of sub-threshold active recovery or perfusion support measurably accelerates post-exertional venous-lactate clearance without worsening PEM; falsified if any movement beyond rest worsens PEM with no clearance benefit. (ch32 Perfusion- and Timing-Directed Lactate Clearance; cert 0.20 — healthy-exercise/perfusion physiology, no ME/CFS trial; severity unknown; origin: brainstorm (ideas 5.1, 5.2).)	Phase 5 / 0.20
compat note lactate-gpr81-signaling	Compatibility (Phase 7): the lactate-signaling cluster REINFORCES the brain-lactate/virtual-hypoxia cluster — Virtual Hypoxia: Elevated Resting Brain Lactate with Blunted Metabolic Reactivity, Thalamic Lac/tCr as a Candidate Stratification Biomarker for Energy-Metabolism Trials, Multiple Sclerosis “Virtual Hypoxia” as a Mechanistic Template — Not an Identity, Shared “Virtual Hypoxia” Mechanism in Long COVID and ME/CFS — Different Trajectory (the signaling arm supplies a receptor mechanism for why elevated brain lactate could matter). Monocarboxylate Transporter Capacity as a Candidate Rate-Limiting Node FEEDS INTO the astrocyte–neuron lactate shuttle (Compartmental Energy Models); Cofactor and Substrate Strategies May Lower Lactate in ME/CFS FEEDS INTO the pre-existing PDH cascade (DMF AND vitamin C/NAC produce no improvement, thiamine/DCA); Post-Exertional Lactate Kinetics as a Monitoring Biomarker FEEDS INTO the ch50 sec-07 lactate-kinetics model; Perfusion- and Timing-Directed Lactate Clearance FEEDS INTO the post-viral PEM perfusion framing ((Haunhorst et al. 2025)). No conflicts — the signaling arm extends, not contradicts, the existing lactate-as-marker content; the null/harm arm ((Godlewska et al. 2025) compartment-specific brain-lactate elevation vs resting calf-muscle metabolites not differing; (Forsythe and Schmidt 2000) buffering harm) is recorded as competing clauses. No certainty adjustments (incoming evidence discounted below 0.60; PARTIAL cap 0.45; per-cycle cap) — bump log empty. Compat audit at tmp/compat-audit-lactate-gpr81-signaling-2026-09-27.md.	Phase 7 / n/a
syn lactate-gpr81-signaling-model	Synthesis (ch07 Lactate as a Signal and a Treatment Question in ME/CFS): the evidence converges on a reframing — lactate is a signaling molecule (GPR81/HCAR1) whose receptor/transporter axis is unmeasured in ME/CFS (Elevated Lactate in ME/CFS May Signal Through GPR81/HCAR1, Monocarboxylate Transporter Capacity as a Candidate Rate-Limiting Node); lactate elevation is compartment-specific rather than uniform ((Godlewska et al. 2025)) and its causal role is unresolved (Does Dysregulated Lactate Signaling Contribute to ME/CFS Symptoms?). Intervention levers are mechanistically distinct — PDH cofactor / substrate shift / carnitine are plausible (Cofactor and Substrate Strategies May Lower Lactate in ME/CFS), buffering is unsupported (Buffering Lactate with Bicarbonate Is Not Supported). Binding constraint is measurement: no ME/CFS lactate-endpoint trial, no axis/kinetics measurement (Measure the GPR81/HCAR1 and MCT Axis in a Severity-Stratified ME/CFS Cohort, Post-Exertional Lactate Kinetics as a Monitoring Biomarker). Cross-refs Elevated Lactate in ME/CFS May Signal Through GPR81/HCAR1 Monocarboxylate Transporter Capacity as a Candidate Rate-Limiting Node Cofactor and Substrate Strategies May Lower Lactate in ME/CFS Buffering Lactate with Bicarbonate Is Not Supported Does Dysregulated Lactate Signaling Contribute to ME/CFS Symptoms? Measure the GPR81/HCAR1 and MCT Axis in a Severity-Stratified ME/CFS Cohort Post-Exertional Lactate Kinetics as a Monitoring Biomarker. (ch07 Lactate as a Signal and a Treatment Question in ME/CFS; cert n/a — synthesis; severity unknown; origin: /integrate-topic lactate-gpr81-signaling.)	Phase 10a / n/a
oq cold-exposure-lactic-acid	Open question (ch32 Does Cold Exposure Genuinely Help, or Is It Selection?): cold-exposure practitioners often report feeling better, but the observation is dominated by selection/reverse causation — the people who tolerate cold are the higher-functioning subgroup, and those who worsen stop (attrition). Real mechanisms (acute noradrenaline/dopamine rise reported, hormetic stressor proposed) could exist, but no ME/CFS study separates them from selection; cold is also a common symptom and PEM trigger (cold intolerance, Raynaud’s, autonomic/mast cell reactivity). Cold-then-warm contrast is not established to clear lactate better than warmth alone (cold constricts muscle blood flow; shivering generates new lactate). Falsifiability: an unselected, activity-matched ME/CFS cohort randomized to cold/contrast vs warm control shows greater lactate clearance or symptom benefit; falsified if benefit vanishes on deselection or cold triggers PEM without a clearance gain. (ch32 Does Cold Exposure Genuinely Help, or Is It Selection?; cert n/a — research question; severity unknown; origin: user question.)	Phase 5 / n/a
compat note lactate-gpr81-signaling-addendum	Compatibility (Phase 7 addendum — missed-pair correction): the lactate-clearance content links to the pre-existing ch34 clearance entries — Impaired Lactate Clearance Delays Recovery (impaired lactate clearance delays recovery; serial post-CPET lactate) and nad lactate (NAD⁺ precursors for lactate-to-pyruvate conversion) — which the main Phase 7 audit did not pair. Cross-refs added in ch31 Additional Supplements; the cold/contrast question Does Cold Exposure Genuinely Help, or Is It Selection? is linked to Perfusion- and Timing-Directed Lactate Clearance and UCP1-Mediated Thermogenesis via Mild Cold Exposure. No certainty adjustments (PARTIAL cap 0.45; the pre-existing #hypothesis is left unchanged).	Phase 7 / n/a

ID / Label	Details	Phase / Cert
lim soreness-not-lactic-acid	Limitation (ch04 Soreness Is Not Caused by Lactic Acid): muscle soreness is NOT caused by lactic acid — lactate clears within ~1 h and does not match the 24–72 h soreness curve (Miles and Clarkson 1994); DOMS is mechanical microtrauma + inflammation (Hotfiel et al. 2018) (Cheung, Hume, and Maxwell 2003). Corrects a prior claim in this section that myalgia “reflects lactic acid accumulation.” H⁺ (proton) acidosis is a separate, plausible nociceptor stimulus. (ch04 Soreness Is Not Caused by Lactic Acid; cert n/a — limitation; severity unknown; origin: /integrate-topic exertional-muscle-soreness.)	Phase 3 / n/a
spec soreness-distinct-from-doms	Speculation (ch04 ME/CFS Exertional Soreness Is Distinct From Ordinary DOMS): ME/CFS exertional soreness is mechanistically distinct from ordinary DOMS — it occurs after minimal/non-eccentric exertion and lacks the repeated-bout adaptation (Clarkson and Hubal 2002); candidate drivers are intramuscular acidosis (ASIC3/TRPV1 acid-sensing nociceptors (W.-N. Chen et al. 2014) (Ugawa et al. 2002)), muscle ischemia/hypoperfusion, and central sensitization (Goldenberg 2025). Translation gap: animal/in vitro + healthy-human; no ME/CFS head-to-head. Competing: deconditioning strain or pure central amplification. Falsifiability: ME/CFS soreness tracks an acidosis/nociceptor or perfusion marker but not micro-damage markers (CK, myoglobin) and lacks adaptation; falsified if it tracks micro-damage and adapts normally. (ch04 ME/CFS Exertional Soreness Is Distinct From Ordinary DOMS; cert 0.40; severity unknown; origin: /integrate-topic exertional-muscle-soreness.)	Phase 3 / 0.40
spec soreness-management	Speculation (ch04 Management of Exertional Muscle Soreness: What the DOMS Evidence Suggests): no ME/CFS-tested intervention; extrapolated from healthy DOMS — gentle movement, massage, and heat/cold reduce DOMS modestly (Cheung, Hume, and Maxwell 2003) (H. L. Davis, Alabed, and Chico 2020) (Y. Wang et al. 2021); compression and supplements show small or inconsistent benefit (Hill et al. 2017) (Tanabe, Fujii, and Suzuki 2022) (Reno et al. 2022); an umbrella review rates most as low-quality (Wiecha et al. 2025). In ME/CFS these must be pacing-gated (movement/heat can cross the PEM threshold); NSAIDs are symptomatic only with no ME/CFS safety study. Not a recommendation. Falsifiability: a pacing-gated gentle-movement/massage/heat protocol reduces soreness without increasing PEM vs warm-rest; falsified otherwise. (ch04 Management of Exertional Muscle Soreness: What the DOMS Evidence Suggests; cert 0.35; severity unknown; origin: /integrate-topic exertional-muscle-soreness.)	Phase 3 / 0.35
oq soreness-marker-dissociation	Open question (ch04 How to Prove ME/CFS Exertional Soreness Is Distinct From DOMS): no ME/CFS cohort has been compared head-to-head with matched-load healthy DOMS. Decisive study: soreness time-course vs micro-damage markers (CK, myoglobin) AND an acidosis/perfusion/nociceptor readout simultaneously, testing repeated-bout adaptation (healthy DOMS adapts; ME/CFS predicted not to). A marker-dissociation panel would make “distinct from DOMS” measurable and stratify the muscle-pain-dominant phenotype. Falsifiability: ME/CFS soreness dissociates from CK/myoglobin and fails to adapt; falsified if it tracks micro-damage and adapts normally (null = ordinary DOMS/deconditioning). (ch04 How to Prove ME/CFS Exertional Soreness Is Distinct From DOMS; cert n/a — research question; severity unknown; origin: brainstorm (ideas 1.2, 2.1, 2.2, 9.1, 9.2, 6.2).)	Phase 5 / n/a
compat note exertional-muscle-soreness	Compatibility (Phase 7): ME/CFS Exertional Soreness Is Distinct From Ordinary DOMS FEEDS INTO Healthy Exercise Response Dynamics (ch50 healthy-DOMS comparator) and Inappropriate sinus tachycardia is dominant, not vascular failure. (acid/ischemia→TRPV1/ASIC3 nociceptor node), and REINFORCES the ch03 pain content (ME/CFS muscle pain differs from DOMS) and fibromyalgia central-sensitization entries. Soreness Is Not Caused by Lactic Acid REINFORCES the lactate-signaling/lactate-clearance content (lactate is a signal/fuel, not a soreness cause). Management of Exertional Muscle Soreness: What the DOMS Evidence Suggests is complementary to Perfusion- and Timing-Directed Lactate Clearance (both exertion-recovery) but explicitly excludes the “flush lactic acid” rationale. No conflicts; no certainty adjustments (healthy-population/animal evidence below 0.60; PROCEED caps). Compat audit at tmp/compat-audit-exertional-muscle-soreness-2026-09-27.md.	Phase 7 / n/a
syn soreness-doms-distinction-model	Synthesis (ch04 Exertional Soreness in ME/CFS: Not Ordinary DOMS, Not Lactic Acid): converges on two corrections and one open question — (1) soreness is not caused by lactic acid (Miles and Clarkson 1994) (Hotfiel et al. 2018); (2) ME/CFS exertional soreness is mechanistically distinct from ordinary DOMS (minimal exertion, no repeated-bout adaptation (Clarkson and Hubal 2002); acidosis H⁺/ischemia/nociceptor + central sensitization (W.-N. Chen et al. 2014) (Goldenberg 2025)); management is pacing-gated with small healthy-DOMS effects (Management of Exertional Muscle Soreness: What the DOMS Evidence Suggests); the decisive marker-dissociation test is unrun (How to Prove ME/CFS Exertional Soreness Is Distinct From DOMS). Cross-refs Soreness Is Not Caused by Lactic Acid ME/CFS Exertional Soreness Is Distinct From Ordinary DOMS Management of Exertional Muscle Soreness: What the DOMS Evidence Suggests How to Prove ME/CFS Exertional Soreness Is Distinct From DOMS. (ch04 Exertional Soreness in ME/CFS: Not Ordinary DOMS, Not Lactic Acid; cert n/a — synthesis; severity unknown; origin: /integrate-topic exertional-muscle-soreness.)	Phase 10a / n/a

ID / Label	Details	Phase / Cert
spec adrenaline-receptor-autoantibody-upstream	Speculation (ch09 (spec:adrenergic-autoantibody-upstream)): an adrenergic-receptor-directed autoantibody is a candidate upstream cause of the central NE deficit. Hartwig 2020 found ME/CFS IgG changed beta2-adrenergic receptor activation in an in vitro bioassay in a subgroup with elevated beta2 autoantibodies (Hartwig et al. 2020). Against: a standardized ELISA found no GPCR-autoantibody difference between 116 POTS patients and 81 controls, judging the tests not useful for establishing autoimmunity (Hall et al. 2022). Competing: autoantibody direction differs across studies (attenuated beta2 activation vs agonistic antiadrenergic antibodies in POTS (Fedorowski et al. 2017) (H. Li et al. 2019)) — recorded as Adrenergic-Receptor Autoantibody Direction — Attenuated or Agonistic?. Translation gap: bioassay, not in-vivo receptor occupancy. Falsifiability: ME/CFS with low CSF NE Pathway shows adrenergic-receptor autoantibodies vs controls, and immunomodulation changes NE Pathway; falsified if autoantibodies absent or uncorrelated. (ch09 (spec:adrenergic-autoantibody-upstream); cert 0.45 — Hartwig 0.70 ME/CFS discounted for unreplicated single centre; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.45
spec dbh-copper-ne-synthesis	Speculation (ch09 (spec:dbh-copper-ne-synthesis)): DBH is a copper-dependent enzyme; brain copper handling constrains NE synthesis (Lutsenko et al. 2019), and congenital DBH deficiency abolishes NE with preserved DA (Senard and Rouet 2006). Copper status is a standard, checkable upstream candidate for a selective NE deficit. Translation gap: no ME/CFS copper/DBH-activity data. Competing: the deficit could be ATP-vesicular rather than copper-limited. Falsifiability: low serum/CSF copper or low DBH activity tracks the NE Pathway deficit in ME/CFS; falsified if copper/DBH normal in low-NE patients. (ch09 (spec:dbh-copper-ne-synthesis); cert 0.40 — general biology + rare disease, ME/CFS link untested; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.40
spec net-slc6a2-variant-oi	Speculation (ch09 (spec:net-slc6a2-variant-oi)): a dominant-negative norepinephrine-transporter (SLC6A2) variant reduces surface NET and is linked to orthostatic intolerance (Hahn, Robertson, and Blakely 2003) — a genetic upstream lever on synaptic NE dwell time and a rationale for NET-targeted therapy. Translation gap: single family, in vitro; not ME/CFS. Falsifiability: SLC6A2 loss-of-function variants enrich in low-NE/orthostatic ME/CFS; falsified if variant frequency equals controls. (ch09 (spec:net-slc6a2-variant-oi); cert 0.33; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.33
spec bh4-cofactor-depletion-ne	Speculation (ch09 (spec:bh4-cofactor-depletion-ne)): BH4 depletion both constrains tyrosine-hydroxylase flux and amplifies inflammation/microvascular degeneration (Rivera et al. 2017), linking cofactor loss to two arms of the same axis. Translation gap: animal/retina model; no ME/CFS BH4 data. Falsifiability: ME/CFS shows low BH4 or high BH2/BH4 ratio correlating with NE Pathway and inflammatory markers; falsified if BH4 normal. (ch09 (spec:bh4-cofactor-depletion-ne); cert 0.28; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.28
spec lc-neuroinflammation-ne	Speculation (ch09 (spec:lc-neuroinflammation-ne)): the locus coeruleus is an inflammation-vulnerable noradrenergic hub (Ahmad, Alemani, and Feinstein 2026); LC-targeted neuroinflammation is a candidate upstream cause of low central NE output. Translation gap: multiple-sclerosis analogue, not ME/CFS; no LC imaging in ME/CFS. Falsifiability: 7T LC neuromelanin/connectivity abnormalities track the NE Pathway deficit in ME/CFS; falsified if LC normal. (ch09 (spec:lc-neuroinflammation-ne); cert 0.25; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.25
spec ne-downstream-sleep-substates	Speculation (ch14h (spec:ne-downstream-sleep-substates)): low LC-NE output is a downstream candidate for unrefreshing sleep — LC-NE activity sets NREM sleep substates and infraslow oscillations (Osorio-Forero et al. 2021), and noradrenergic microarousals couple to glymphatic clearance (Lüthi and Nedergaard 2025). Translation gap: animal circuit study + review. Falsifiability: ME/CFS with low NE Pathway shows altered NREM substate/oscillation structure and reduced clearance proxies vs controls; falsified if sleep architecture and clearance normal. (ch14h (spec:ne-downstream-sleep-substates); cert 0.38; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.38
spec ne-descending-pain-disinhibition	Speculation (ch14h (spec:ne-descending-pain-disinhibition)): low central NE disinhibits descending pain pathways — descending noradrenergic (alpha2) inhibition is a therapeutic lever in chronic pain (Hayashida and Obata 2019), giving a mechanistic account of the pain-fatigue overlap. Translation gap: review, general chronic pain. Falsifiability: ME/CFS with low NE Pathway shows weaker conditioned pain modulation correlating with NE Pathway; falsified if no correlation. (ch14h (spec:ne-descending-pain-disinhibition); cert 0.45; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.45
oq adrenergic-autoantibody-direction	Open question (ch09 Adrenergic-Receptor Autoantibody Direction — Attenuated or Agonistic?): does adrenergic-receptor autoantibody signalling run attenuated (Hartwig 2020 beta2 bioassay (Hartwig et al. 2020)) or agonistic (antiadrenergic POTS antibodies (Fedorowski et al. 2017) (H. Li et al. 2019)), and do the two coexist by receptor subtype? Unresolvable with current data; competing mechanisms have comparable-quality evidence (certainty within ±0.15). (ch09 Adrenergic-Receptor Autoantibody Direction — Attenuated or Agonistic?; cert n/a — research question; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / n/a
spec grab-ne-invivo-probe	Speculation (ch14h (spec:grab-ne-invivo-probe)): genetically encoded GRAB(NE) sensors enable direct in-vivo measurement of noradrenergic release (Jiesi Feng et al. 2024), a potential human-translatable probe of the central NE deficit. Translation gap: animal tool; not clinically available. Falsifiability: a validated human NE probe distinguishes low-NE ME/CFS from controls and tracks treatment; falsified if it fails validation. (ch14h (spec:grab-ne-invivo-probe); cert 0.38; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / 0.38
syn noradrenergic-full-axis-model	Synthesis (ch09 The Noradrenergic Axis — Upstream Causes to Downstream Consequences): the evidence assembles a single axis — upstream constraints on NE synthesis/release (copper-dependent DBH (Lutsenko et al. 2019) (Senard and Rouet 2006), BH4 cofactor (Rivera et al. 2017), ATP-dependent vesicular cycling (Rangaraju, Calloway, and Ryan 2014), NET genotype (Hahn, Robertson, and Blakely 2003), adrenergic autoantibodies (Hartwig et al. 2020), LC neuroinflammation (Ahmad, Alemani, and Feinstein 2026)) converge on the selective central NE deficit (Aregawi et al. 2026), which produces downstream sleep (Osorio-Forero et al. 2021) and pain (Hayashida and Obata 2019) consequences. Cross-refs (spec:adrenergic-autoantibody-upstream) (spec:dbh-copper-ne-synthesis) (spec:net-slc6a2-variant-oi) (spec:bh4-cofactor-depletion-ne) (spec:lc-neuroinflammation-ne) (spec:ne-downstream-sleep-substates) (spec:ne-descending-pain-disinhibition) Adrenergic-Receptor Autoantibody Direction — Attenuated or Agonistic?. Binding constraint is measurement: no ME/CFS study measures copper, DBH activity, BH4, or NET genotype against the CSF NE Pathway. (ch09 The Noradrenergic Axis — Upstream Causes to Downstream Consequences; cert n/a — synthesis; severity unknown; origin: /integrate-topic noradrenergic-full-scope.)	Phase 3 / n/a
compat note noradrenergic-full-axis	Compatibility (Phase 7): the upstream cluster FEEDS INTO the pre-existing selective-NE-deficiency entry and ATP-Dependent Vesicular Norepinephrine Deficiency as PEM Mechanism (ATP-vesicle arm); (spec:adrenergic-autoantibody-upstream) REINFORCES the pre-existing GPCR-autoantibody content and the central-peripheral-mismatch entry; (spec:dbh-copper-ne-synthesis) REINFORCES the gap-g04-copper-dysregulation queue item; (spec:ne-downstream-sleep-substates) FEEDS INTO the waking-local-sleep content; (spec:ne-descending-pain-disinhibition) FEEDS INTO the pain-central-sensitization content. Only tension: autoantibody direction (Adrenergic-Receptor Autoantibody Direction — Attenuated or Agonistic?) — recorded as competing, not conflict. No certainty adjustments (PARTIAL cap 0.45; per-cycle cap; incoming evidence below 0.60 except Hartwig ME/CFS but unreplicated).	Phase 7 / n/a

ID / Label	Details	Phase / Cert
lim noradrenergic-drug-evidence-indirect	Limitation (ch14h (lim:noradrenergic-drug-evidence-indirect)): no noradrenergic medication has an ME/CFS randomized trial with PEM as an endpoint. The human evidence is indirect (orthostatic hypotension, POTS, narcolepsy, MS, fibromyalgia, Parkinson’s, depression) and partly negative. Direct human tests: amantadine did not reduce MS fatigue (SMD -0.22, p=0.37; increased insomnia) (Cruccioli et al. 2026); modafinil matched CBT with no added combination benefit (Braley et al. 2024); reboxetine was among the least efficacious and least tolerated antidepressants (Cipriani et al. 2018). Positive but non-ME/CFS: duloxetine reduced fibromyalgia pain (NNTB 8) (Migliorini et al. 2023); droxidopa/midodrine are first-line for neurogenic OH (Palma and Kaufmann 2020b). A mechanistic fork: peripheral denervation responds to NE precursors/agonists, central autonomic failure to NE reuptake inhibitors (Park et al. 2020). Interaction cautions from FDA labels: MAOI combinations contraindicated (atomoxetine, solriamfetol, midodrine); pressor stacking (droxidopa+midodrine) risks supine hypertension. (ch14h (lim:noradrenergic-drug-evidence-indirect); cert 0.70 as a limitation — high confidence in the negative/indirect finding; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 3.)	Phase 3 / 0.70
spec peripheral-vs-central-autonomic-fork	Speculation (ch14h (spec:peripheral-vs-central-autonomic-fork)): in orthostatic intolerance, the response to a noradrenergic drug may identify the lesion site — peripheral sympathetic denervation responds to norepinephrine precursors/agonists (droxidopa, midodrine), central autonomic failure to norepinephrine reuptake inhibitors (atomoxetine) (Park et al. 2020). If this fork transfers to ME/CFS, drug response becomes a subtyping probe. Translation gap: OH populations, not ME/CFS. Falsifiability: in ME/CFS+OI, the denervation status (standing NE, QSART) predicts droxidopa vs atomoxetine response; falsified if response is uncorrelated with denervation status. (ch14h (spec:peripheral-vs-central-autonomic-fork); cert 0.40; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 3.)	Phase 3 / 0.40

ID / Label	Details	Phase / Cert
model ne-deficit-upstream-downstream	Model (ch52 Neurotransmitter Models): the ch52 catecholamine ODE is extended so the DBH step is scaled by ATP and copper availability, which reproduces the selective NE-over-DA pattern of (Aregawi et al. 2026) as a model output. Six upstream edges are tabulated with evidence tier and certainty (ATP 0.35, copper 0.40, BH4 0.28, NET 0.33, adrenergic autoantibody 0.30, LC neuroinflammation 0.25). Five downstream edges (fatigue 0.50, PEM 0.40, sleep 0.35, glymphatic 0.35, pain 0.30, PFC cognition 0.30). Critically, the model EXCLUDES the NE to autonomic and NE to mood edges because the source study found no correlation with orthostatic hypotension/tachycardia, pain, cognition, anxiety, or depression (Aregawi et al. 2026). Falsifiability: upstream driver tracks CSF NE Pathway; NE Pathway predicts effort/motor sustain but not orthostatic HR or mood. Limitation: no ME/CFS measurement of any upstream driver; core finding single-centre n=16. (ch52 Neurotransmitter Models; cert n/a — model, per-edge certainties as above; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 4.)	Phase 3 / n/a

ID / Label	Details	Phase / Cert
model ne-medication-expectation-map	Model (ch14h Drug Interactions — FDA Prescribing Information, Checked Online): symptom-level expectation map for a noradrenergic agent. Expected to improve: fatigue/effort-motor (main target, ME/CFS CSF correlation (Aregawi et al. 2026)) and PEM (PASC-PEM subgroup). Possibly: unrefreshing sleep (animal (Osorio-Forero et al. 2021)). NOT expected: pain, cognition (NE Pathway did not correlate, (Aregawi et al. 2026)); orthostatic intolerance/POTS and mood/anxiety (excluded edges); muscle soreness ((Pullinen et al. 2011)) and excess lactate ((Jessen et al. 2020)). Masking caveat included. Falsifiability: a NE-stratified trial moves fatigue/PEM in low-NE patients but not orthostatic HR or mood. (ch14h Drug Interactions — FDA Prescribing Information, Checked Online; cert n/a — model/expectation; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 6.)	Phase 3 / n/a
spec wired-but-tired-arousal-mismatch	Speculation (ch14h Wired But Tired: Arousal-Mismatch as a Named Noradrenergic Symptom): “wired but tired” — inability to sleep despite exhaustion with subjective hyperarousal — is named as a candidate noradrenergic arousal-mismatch symptom, distinct from the sleepiness state Four Biologically Distinct States Collapse Under the Single Word “Fatigue”. Mechanism: LC-NE arousal system fails to transition between states (Osorio-Forero et al. 2021) (Lüthi and Nedergaard 2025). Competing: mast-cell/cytokine hyperarousal, HPA dysregulation, anxiety, sensory hypersensitivity — not diagnostic of a noradrenergic lesion. Falsifiability: the pattern co-occurs with a noradrenergic arousal abnormality (pupil/LC-NE dynamics, altered sleep-onset latency with preserved pressure); falsified if fully explained by anxiety or objective sleepiness. (ch14h Wired But Tired: Arousal-Mismatch as a Named Noradrenergic Symptom; cert 0.35; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 6.)	Phase 3 / 0.35

ID / Label	Details	Phase / Cert
syn ne-arousal-clearance-regulator	Synthesis (ch14h The Noradrenergic Deficit as an Arousal-and-Clearance Regulator): reframes the selective central NE deficit from a “fatigue transmitter” shortage to a four-job regulator — arousal/sleep-state transitions (Osorio-Forero et al. 2021) (Lüthi and Nedergaard 2025) (Amy F. T. Arnsten and Shanafelt 2021), vasomotion-driven glymphatic clearance (Hauglund et al. 2025) (Nedergaard 2026), descending analgesia (Hayashida and Obata 2019), and astrocytic lactate fuel supply (Killeen, Russell, and Sergeant 2013) (Hertz et al. 2015) — and predicts self-reinforcement. (ch14h The Noradrenergic Deficit as an Arousal-and-Clearance Regulator; cert n/a — synthesis; severity unknown; origin: /integrate-topic noradrenergic-full-scope session 7.)	Phase 10a / n/a
spec ne-clearance-loop	Speculation (ch14h The NE-Clearance Loop: A Self-Reinforcing Trajectory): NE → vasomotion → glymphatic clearance loop. NE down → vasomotion amplitude/synchrony down → clearance down → neuroinflammation up → LC output down → NE down; thresholded loop can drive progressive decline (ratchet). Supported arm: NE→vasomotion→clearance (mouse/review (Hauglund et al. 2025) (Nedergaard 2026)); inferred arm: clearance→inflammation→LC. Falsifiability: DTI-ALPS/vasomotion correlates with an inflammatory marker and both with the CSF NE Pathway; raising NE (taVNS) moves clearance proxies. (ch14h The NE-Clearance Loop: A Self-Reinforcing Trajectory; cert 0.35; severity unknown; origin: brainstorm P2.)	Phase 3 / 0.35
oq ne-microglial-loop	Open question (ch14h Does Norepinephrine Suppress or Promote Neuroinflammation?): does NE suppress or promote neuroinflammation? Bu 2025 (mouse) found microglial β2-ADRB2 signalling suppressed microglial engagement (Bu et al. 2025); Evans 2024 (mouse) found β2 blockade/adrb2 deletion attenuated inflammation, female-specific (Evans et al. 2024). Opposing directions; the second loop depends on the suppression direction. (ch14h Does Norepinephrine Suppress or Promote Neuroinflammation?; cert n/a — research question; severity unknown; origin: brainstorm P2.)	Phase 3 / n/a

ID / Label	Details	Phase / Cert
spec tavns-lc-ne	Speculation (ch14h Transcutaneous Vagus Nerve Stimulation as a Non-Pharmacological NE Modulator): taVNS activates the LC-NE system via NTS (Hilz 2022) (Lv et al. 2024) — a non-pharmacological NE modulator and a safe probe of the axis (P3). Falsifiability: taVNS vs sham moves a non-invasive NE readout (pupillometry/handgrip/VMA-HVA) and a clearance/inflammatory proxy. (ch14h Transcutaneous Vagus Nerve Stimulation as a Non-Pharmacological NE Modulator; cert 0.30; severity unknown; origin: brainstorm P3.)	Phase 3 / 0.30
spec ne-astrocyte-lactate-fuel	Speculation (ch14h Low NE Impairs Astrocytic Lactate Fuel Supply): noradrenaline stimulates astrocytic glycogenolysis and lactate release to neurons (Hertz et al. 2015) (Killeen, Russell, and Sergeant 2013), so low NE predicts impaired astrocytic fuel supply (not lactate accumulation — opposite of the soreness myth Soreness Is Not Caused by Lactic Acid) (P4). Falsifiability: astrocytic lactate-supply markers lower and tracking the NE Pathway. (ch14h Low NE Impairs Astrocytic Lactate Fuel Supply; cert 0.30; severity unknown; origin: brainstorm P4.)	Phase 3 / 0.30

ID / Label	Details	Phase / Cert
spec ne-battery	Non-invasive NE battery (pupillometry + handgrip + VMA/HVA) for stratification without LP (P5). (Kang and Banaji 2020) (O’Callaghan et al. 2025) (Aregawi et al. 2026). Falsifiable: components co-vary with CSF NE Pathway. (ch14h A Non-Invasive Noradrenergic Battery; cert 0.30.)	Phase 3 / 0.30
spec pem-ne-crash-curve	Post-exertional NE crash curve test (serial pupil/handgrip 72 h) (P6). (Aregawi et al. 2026). Falsifiable: dip and slow recovery match PEM timing. (ch14h The Post-Exertional NE Crash: A Measurable Curve; cert 0.35.)	Phase 3 / 0.35
oq orexin-lc-triangle	Orexin-LC coupling is state/stress-dependent, not simple (P7). (Barcomb et al. 2022) (orexin minimal on LC inputs) (Lo et al. 2022) (hypocretin in LC mediates stress REM disruption). (ch14h Orexin, the Locus Coeruleus, and the ME/CFS-Narcolepsy Triangle; cert n/a.)	Phase 3 / n/a
spec pots-ne-fork	POTS noradrenergic treatment fork: denervation predicts droxidopa vs atomoxetine (P8). (Park et al. 2020) (Palma and Kaufmann 2020b). (ch14h The POTS Noradrenergic Treatment Fork; cert 0.40.)	Phase 3 / 0.40
spec adhd-ne-endophenotype	Shared noradrenergic arousal endophenotype with ADHD (P9). (Del Campo et al. 2011). (ch14h A Shared Noradrenergic Arousal Endophenotype with ADHD; cert 0.35.)	Phase 3 / 0.35
spec ne-gut-microbial-endocrinology	NE-bacteria microbial endocrinology as a host-catecholamine input to the gut (P12). (J. Gao et al. 2019) (Dicks 2022). (ch14h Noradrenaline and the Gut Microbiome: Microbial Endocrinology; cert 0.25.)	Phase 3 / 0.25
spec lc-ne-adaptive-gain	LC-NE adaptive gain links the deficit to sensory/interoceptive amplification (P13). (Aston-Jones and Cohen 2005). (ch14h Locus-Coeruleus Adaptive Gain and Symptom Amplification; cert 0.30.)	Phase 3 / 0.30
spec ne-ei-bbb	NE modulates cortical E/I and can open the BBB (gateway reflex) (P14). (Medina-Coss Y Leon et al. 2025) (Matsuyama et al. 2021). (ch14h Noradrenaline, Cortical E/I Balance, and the Blood-Brain Barrier; cert 0.25.)	Phase 3 / 0.25

ID / Label	Details	Phase / Cert
spec h4-receptor-untargeted-arm	Speculation (ch24 H4 Receptor as the Untargeted Fourth Histamine Arm): the H4 receptor is a fourth histamine arm on immune cells (mast cells, eosinophils, T cells) that H1/H2/CysLT1 blockade does not touch; H4 antagonists are research-stage (Gibbs and Levi-Schaffer 2012) (B. H. Jin et al. 2024). Falsifiability: an H4 antagonist helps where H1+H2 fail, dissociated from H1/H2 occupancy. (cert 0.35; severity unknown; translation gap: preclinical.)	Phase 3 / 0.35
spec mast-cell-mediator-escape	Speculation (ch24 Mast Cell Mediator Escape Beyond H1/H2/CysLT1 Blockade): current blockade leaves tryptase (PAR2 to microglia) and PGD2/CRTH2 unblocked, so residual MCAS can persist (Ocak et al. 2020) (Kupczyk and Kuna 2017). Falsifiability: high tryptase/PGD2 vs histamine metabolites predict residual symptoms on maximal blockade. (cert 0.35.)	Phase 3 / 0.35
spec histamine-clearance-vs-receptor	Speculation (ch24 Histamine Clearance Defect vs. Receptor Over-Stimulation): a histamine degradation defect (DAO gut, HNMT CNS) is distinct from receptor over-stimulation; extends dao and HNMT-Mediated Histamine Clearance Failure Subtype (Maintz and Novak 2007) (Kadiyska et al. 2025). Falsifiability: functional DAO/HNMT deficiency is more prevalent among antihistamine-partial responders than among blockade responders. (cert 0.40.)	Phase 3 / 0.40
spec copper-dao-histamine-bridge	Speculation (ch24 Copper–DAO–Histamine Bridge): DAO is a copper-dependent amine oxidase, so functional copper deficiency could lower histamine breakdown (Murakawa et al. 2025) (Hu Frisk et al. 2017). HONEST: the direct human copper to histamine-intolerance link was NOT found. Falsifiability: low copper/ceruloplasmin associates with low DAO activity. (cert 0.25.)	Phase 3 / 0.25
spec cholinergic-mast-cell-bidirectional	Speculation (ch24 Cholinergic Modulation of Mast Cells Is Bidirectional): acetylcholine on α7-nAChR/M3 both inhibits and, context-dependently, triggers mast cell degranulation, so pyridostigmine’s net effect in a mast-cell-active patient is unpredictable (Kutukova et al. 2025) (Woestemeier et al. 2026); α7 activation on mast cells is pro-atherogenic in an atherosclerosis mouse model (C. Wang et al. 2017). No direct pyridostigmine plus mast cell study exists. (cert 0.25.)	Phase 3 / 0.25
oq histamine-h1-h2-redistribution	Open question (ch24 Does H1 Blockade Redistribute Free Histamine onto Immunosuppressive H2 Receptors?): does H1 blockade shift free histamine onto immunosuppressive H2 receptors (raising Th2/Th1), making H1 monotherapy immunologically counterproductive while H1+H2 avoids it? (Jutel et al. 2001) (Packard and Khan 2003). (cert n/a.)	Phase 3 / n/a
oq h2-autocrine-mast-cell-brake	Open question (ch24 Do Mast Cells Carry an H2/H4 Autocrine Brake That Famotidine Could Release?): do mast cells carry an H2/H4 autocrine brake such that famotidine could increase degranulation? Contradictory old in vitro evidence (Tung et al. 1982) vs (Masini et al. 1982). (cert n/a.)	Phase 3 / n/a
spec pyridostigmine-m3-bioassay	Speculation (ch24 Pyridostigmine as a Functional M3-Receptor Bioassay): a pyridostigmine response might mark a cholinergic-reversible symptom (a proposed, unvalidated bioassay) (Schiweck et al. 2026) (Elsaghir and Witte 2026). No ME/CFS bioassay validated. (cert 0.35.)	Phase 3 / 0.35
oq four-arm-mast-cell-mediator-map	Open question (ch24 Can Mediator-Arm Response Profiles Define a Reproducible “Four-Arm” MCAS Subtype Map?): can response profiles to H1/PAF, H2, CysLT1, and cholinergic drugs be clustered into reproducible MCAS subtypes? (Kulin et al. 2026) (Rehman et al. 2026). Method not established. (cert n/a.)	Phase 3 / n/a
oq non-response-pharmacodiagnostic-fingerprint	Open question (ch24 Can the Pattern of Nulls Across the Four Drugs Constrain Mechanism by Inverse Negative Control?): can the pattern of nulls across the four drugs constrain mechanism (inverse negative control)? (Ortiz et al. 2026) (Abbotts et al. 2025). No MCAS-specific literature. (cert n/a.)	Phase 3 / n/a
spec histamine-compensatory-arousal	Speculation (ch24 Histamine as Compensatory Arousal: H1 Blockade Unmasks Fatigue in a Hyperarousal Subtype): histamine promotes wakefulness/endurance, so H1 blockade could unmask fatigue in a hyperarousal subtype (subtype-splitting) (G. G. Kay 2000) (Jones 2020). (cert 0.30.)	Phase 3 / 0.30
spec h1-trpv1-nociceptor-sensitisation	Speculation (ch24 H1-Mediated TRPV1 Nociceptor Sensitisation as an Antihistamine-Analgesia Bridge): histamine H1 sensitises TRPV1 nociceptors, so H1 blockade may indirectly reduce TRPV1 pain/soreness, though the mouse analgesia may run through Kv7 channels rather than TRPV1; links Inappropriate sinus tachycardia is dominant, not vascular failure. (Nikolaeva-Koleva et al. 2021) (Kalangara et al. 2022). In vitro/animal only. (cert 0.35.)	Phase 3 / 0.35
spec histamine-orexin-arousal-bridge	Speculation (ch24 Histamine-Orexin Arousal Bridge and the H1-Blocker Sedation Threshold): histaminergic and orexinergic wake systems interact; peripheral mast cell histamine does not freely cross the blood-brain barrier, so any contribution of MCAS histamine surges to the wired-but-tired arousal mismatch requires an unevidenced indirect route (Sakurai, Saito, and Yanagisawa 2021) (Jones 2020). (cert 0.35.)	Phase 3 / 0.35
spec paf-platelet-microclot-modulation	Speculation (ch24 PAF-Mediated Platelet Activation and Microclot Modulation): PAF activates platelets, and a contested microclot construct (amyloid fibrin(ogen) aggregates, not established in ME/CFS) is reported to contain platelets, so rupatadine’s PAF arm could in principle modulate aggregation (Hung et al. 2025) (Baker et al. 2023). Zero direct evidence for the drug effect. (cert 0.30.)	Phase 3 / 0.30
spec montelukast-cns-leukotriene-probe	Speculation (ch24 Montelukast as a CNS-Penetrant Leukotriene Probe): montelukast is the member most likely to engage a central target (in-silico BBB prediction), probing a central leukotriene/CysLT1 arm, but carries neuropsychiatric/sleep harm (Zhou et al. 2019) (Amezola-Herrera et al. 2026) (Redfern and Smith 2026). (cert 0.30; clinical-hazard caution.)	Phase 3 / 0.30
spec circadian-mast-cell-drug-timing	Speculation (ch24 Circadian Timing of Mast-Cell-Targeted Drugs): mediator activity peaks at night, so timing H1/H2/CysLT1 dosing to the peak could improve control at equal dose (Smolensky, Lemmer, and Reinberg 2007) (S. Tanaka, Tanaka, and Abe 2003). No AM-vs-PM ME/CFS trial. (cert 0.35; links gap G5.)	Phase 3 / 0.35
spec cholinergic-reserve-index	Speculation (ch24 Cholinergic Reserve Index as a Tolerability Predictor for Pyridostigmine): the pyridostigmine dose for benefit vs the dose at which muscarinic side effects appear could define a therapeutic window (Schlömer et al. 2025) (Raj et al. 2005). Relevant ME/CFS datapoint; tolerability not measured side-by-side. (cert 0.40.)	Phase 3 / 0.40
spec antihistamine-tachyphylaxis-pulsed-dosing	Speculation (ch24 Pulsed Dosing and a Drug-Holiday Restore-Test for Antihistamine Tachyphylaxis): chronic H1 blockade can upregulate H1 receptors and cause rebound, so pulsed dosing with a drug-holiday restore-test could distinguish desensitisation from tolerance (Seng, Oka, and Tan 2026) (Richardson et al. 2002). Caution: withdrawal risk. (cert 0.35.)	Phase 3 / 0.35
spec cycle-timed-mast-cell-dosing	Speculation (ch24 Cycle-Timed Mast Cell Dosing): estrogen augments mast cell degranulation, so symptoms may track menstrual phase and cycle-synchronised dosing may beat fixed dosing; extends HRT Cardiovascular and VTE Risk Is Route- and Regimen-Dependent (Zaitsu et al. 2007) (W. Chen et al. 2008). No cycle-timing trial. (cert 0.30.)	Phase 3 / 0.30
spec famotidine-gastric-ph-microbiome-histamine	Speculation (ch24 Famotidine, Gastric pH, and a Microbiome-Histamine Load Hypothesis): possible unsubstantiated harm — acid suppression can shift gut flora and bacterial histamine production (Imhann et al. 2016) (Barcik et al. 2017). Chain assembled by analogy; no famotidine-specific harm study. (cert 0.30.)	Phase 3 / 0.30
spec compensatory-mediator-synthesis-receptor-blockade	Speculation (ch24 Compensatory Mediator Release After Histamine-Receptor Blockade): possible unsubstantiated harm — receptor blockade could remove autocrine feedback and increase mediator release; old in vitro and contradictory (Tung et al. 1982) (Masini et al. 1982). Zero clinical evidence. (cert 0.25.)	Phase 3 / 0.25
lim mast-cell-energy-envelope-drain-untested	Limitation (ch24 The Mast Cell “Energy-Envelope Drain” Is an Untested Hypothesis, Not a Finding): the hypothesis that chronic mast cell degranulation is a measurable energy drain has NO supporting literature (zero hits); closest evidence is histamine-specific and does not measure mast cell metabolic cost (Abbotts et al. 2025). Untested hypothesis, not a finding. (cert n/a.)	Phase 3 / n/a

ID / Label	Details	Phase / Cert
hyp adhd-dementia-risk	Hypothesis (ch14d ADHD as a Risk Factor for Late-Life Cognitive Impairment and Dementia): ADHD diagnosis or polygenic liability is associated with substantially elevated incident MCI/dementia risk (HR 2.8–4.6 across All of Us, Taiwan, Sweden, Israel; ADHD-PRS highest-vs-lowest quintile HR 1.35) (Bauer-Negrini et al. 2026) (Tzeng et al. 2019) (Dobrosavljevic et al. 2021) (S. Z. Levine et al. 2023). Stated as association, not mechanism. Falsifiability: a Mendelian-randomisation study of ADHD liability on dementia finds no causal effect, or the association fully attenuates after joint stimulant/psychiatric/cardiometabolic control. (cert 0.60; severity n/a – general-population cohorts; translation gap: no ME/CFS cohort.)	Phase 3 / 0.60
spec energy-deficit-neurodegeneration-bridge	Speculation (ch14d Shared Cerebral Energy Deficit as the Bridge Linking Neurodevelopmental Conditions to Dementia): a shared cerebral energy deficit (glucose hypometabolism + mitochondrial/bioenergetic failure) links the neurodevelopmental energy-deficit phenotype (ADHD/autism) to late-life neurodegeneration (Cunnane et al. 2011) (Swerdlow, Burns, and Khan 2014) (Mosconi 2013); the join is inferred, not measured, and causal direction unresolved (vicious cycle) (Cunnane et al. 2011); metabolic-mediation support from (Fluegge and Fluegge 2018). Falsifiability: longitudinal FDG-PET/MRS in young ADHD adults – baseline hypometabolism magnitude must predict later decline independent of amyloid deposition for the energy-deficit-first reading. (cert 0.40; severity n/a.)	Phase 3 / 0.40
oq mecfs-energy-deficit-dementia-risk	Open question (ch14d Does ME/CFS’s Chronic Energy Deficit Confer a Comparable Late-Life Neurodegeneration Risk?): does an acquired (ME/CFS) energy deficit confer comparable late-life neurodegeneration risk as a developmental one (ADHD)? No ME/CFS-to-dementia longitudinal data exist; acquired-vs-developmental and exertion-triggered-vs-resting distinctions argue against a simple “yes”. Research question, not a patient-facing risk claim. (cert n/a.)	Phase 3 / n/a
lim adhd-dementia-confounding	Limitation (ch14d Confounding Cannot Yet Be Excluded from the ADHD–Dementia Association): stimulant medication, psychiatric comorbidity, and EHR ascertainment confound the ADHD–dementia association; PRS partly defends against ascertainment but not pleiotropy/psychiatric confounding (S. Z. Levine et al. 2023) (Dobrosavljevic et al. 2021) (S. Becker, Sharma, and Callahan 2022) (S. Becker et al. 2023). (cert n/a.)	Phase 3 / n/a
spec resting-vs-exertional-energy-deficit	Speculation (ch14d Sustained Resting — Not Exertion-Triggered — Energy Deficit Is the Dementia-Relevant State): only sustained resting cerebral energy deficit (ADHD, prodromal AD) – not episodic, exertion-triggered PEM – seeds the mitochondrial cascade to neurodegeneration (Mosconi 2013) (Cunnane et al. 2011) (Swerdlow, Burns, and Khan 2014). Origin: brainstorm. Falsifiability: resting FDG-PET/MRS in ME/CFS between crashes nearer normal than prodromal AD/ADHD. (cert 0.30; severity n/a.)	Phase 5 / 0.30
spec cumulative-energy-deficit-exposure	Speculation (ch14d Dementia Risk Scales With Cumulative (Magnitude × Duration) Energy-Deficit Exposure): dementia risk scales with lifetime (magnitude × duration) energy-deficit exposure, so developmental ADHD ≫ acquired ME/CFS; dose–response with duration/onset-age predicted. Origin: brainstorm. Falsifiability: ME/CFS dementia hazard flat across duration, or recovered patients retain risk, falsifies. (cert 0.35; severity n/a.)	Phase 5 / 0.35
spec energy-deficit-dopaminergic-trajectory	Speculation (ch14d The Energy-Deficit-to-Dementia Route Is Dopaminergic/Lewy-Body, Not Amyloid): the energy-deficit-to-dementia route is dopaminergic/Lewy-body, not amyloid (Golimstok et al. 2024); early markers would be sleep/movement, not memory. Origin: brainstorm. Falsifiability: α-synuclein seeding/RBD not elevated in older ADHD vs amyloid-PET. (cert 0.28; severity n/a.)	Phase 5 / 0.28
oq diabetes-dementia-ceiling	Open question (ch14d Type-2 Diabetes as the Empirical Ceiling for Any Acquired Energy-Deficit-to-Dementia Estimate): type-2 diabetes is the empirical ceiling for any acquired energy-deficit-to-dementia estimate (HR 1.5–2); any ME/CFS estimate should be of T2D’s order or lower, not ADHD’s (Fluegge and Fluegge 2018). Origin: brainstorm. (cert n/a.)	Phase 5 / n/a
lim energy-deficit-regional-mismatch	Limitation (ch14d The Energy Deficit Sits in Different Brain Regions Across Conditions): the energy deficit sits in different regions across conditions (ME/CFS brainstem/cerebellar vs ADHD prefrontal vs prodromal AD temporoparietal), so a brainstem/cerebellar deficit has no path to cortical proteinopathy (Mosconi 2013). Origin: brainstorm. (cert n/a.)	Phase 5 / n/a

Hypothesis p=0.40Critical Clearance Window Determining Chronic Outcomes

Evidence: A time-limited window (roughly 2–8 weeks post-infection) during which effective viral clearance determines long-term outcome. The Dubbo cohort found a similar post-infectious-fatigue rate (about 10–12%) regardless of pathogen (EBV, Coxiella, Ross River virus), suggesting host-response pattern matters more than the specific pathogen (Hickie et al. 2006b) (Hickie et al. 2006a); Long COVID immune signatures at 3 months predict 12-month outcomes (Phetsouphanh et al. 2024).
Citations: (Hickie et al. 2006b) (Hickie et al. 2006a) (Phetsouphanh et al. 2024)
Mechanism: During the clearance window, viral eradication, immune resolution, tissue repair, and immune reprogramming must all succeed; failure at any step initiates self-sustaining pathological loops. Early antiviral or immunomodulatory intervention may prevent chronicity.
Chapter ref: ch08 viral persistence and clearance failure: Critical Clearance Window Determining Chronic Outcomes
Prediction: Immune profiles at 4 weeks post-infection predict 6-month ME/CFS status with >80% accuracy; antiviral treatment within 2 weeks lowers ME/CFS incidence; inflammatory-marker elevation persisting beyond 8 weeks correlates with severity. Falsified if 4-week profiles carry no prospective information or early antivirals change nothing.
Treatment: Early intervention (antivirals, immunomodulators) during the window; biomarker-based risk stratification to target high-risk patients. No current clinical recommendation.
Limitation: Window timing likely varies by pathogen and individual; assumes clearance is the primary determinant when host factors may weigh equally; no prospective trial of early intervention. Severity not stratified. (ch08, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.35Compartmentalized Viral Reservoirs in ME/CFS

Evidence: ME/CFS patients may harbor viral reservoirs in immune-privileged compartments (gut lymphoid tissue, CNS, bone marrow) driving chronic immune activation via intermittent antigen release. Comparative virology: FIPV persists in B and T lymphocytes after antiviral treatment and clinical recovery (Kol et al. 2026); Long COVID tissue studies report viral protein persistence in gut epithelium to 12 months (Peluso et al. 2024).
Citations: (Kol et al. 2026) (Peluso et al. 2024)
Mechanism: Viral antigen is presented during any immune activation (infection, vaccination, exercise), explaining why diverse stimuli trigger symptom exacerbation. The reservoir maintains antigenic stimulation without productive replication; standard antivirals fail because they cannot access or eliminate it.
Chapter ref: ch08 viral persistence and clearance failure: Compartmentalized Viral Reservoirs in ME/CFS; ch38 feline infectious peritonitis model; related open question Viral RNA in Circulating Lymphocytes as Diagnostic Biomarker
Prediction: Single-cell RNA-seq detects viral transcripts in a small fraction of ME/CFS lymphocytes; the virus-positive fraction correlates with severity and duration; in vitro activation of patient lymphocytes increases detectable viral antigen; recoverers show clearance of lymphocyte-associated viral RNA. Falsified if lymphocytes are consistently RNA-negative across sensitive assays.
Treatment: Long-term antiviral therapy to outlast lymphocyte lifespan; targeted immune-cell depletion or reconstitution; reservoir detection and monitoring. Research directions only.
Limitation: Cross-species extrapolation from felines to humans; no study has directly demonstrated persistence of the triggering virus in human lymphocytes; “below PCR threshold” claims are currently untestable with standard methods. Severity not stratified. (ch08/ch38, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.45Viral-Immune-Metabolic Cascade

Evidence: ME/CFS initiated by failed viral clearance triggers a self-sustaining cascade: viral persistence → chronic immune activation → metabolic reprogramming → energy crisis → impaired immune function → worsened clearance. Post-infectious onset in a majority of cases, immune activation, metabolic abnormalities, and immune exhaustion are each documented separately; the chain itself is inferred. Post-infectious epidemiology (Hickie et al. 2006a); longitudinal immune dysfunction (Phetsouphanh et al. 2022).
Citations: (Hickie et al. 2006a) (Phetsouphanh et al. 2022)
Mechanism: Each step reinforces the previous one, creating a vicious cycle that explains multi-system presentation and treatment resistance.
Chapter ref: No dedicated chapter environment (registry-only retroactive entry); nearest anchors: ch08 viral persistence content and the metabolic-cascade hypotheses in Part 2
Prediction: Early antiviral intervention prevents cascade initiation in animal models; combination therapy targeting multiple cascade components outperforms single-target approaches; cascade-activity biomarkers correlate with severity. Falsified if single-target interventions match combination outcomes with no cascade biomarker correlation.
Treatment: Multi-pronged: enhance clearance (antivirals), modulate immune activation, support mitochondrial function, reduce energy demand (pacing). Framework for research sequencing, not a clinical recommendation.
Limitation: Cascade model is plausible but not proven; component weighting likely varies between patients; non-infectious triggers imply multiple routes to a similar endpoint. Severity not stratified. (2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.35Long COVID to ME/CFS Progression Model

Evidence: A subset of Long COVID patients may progress to an ME/CFS-like state marked by immune exhaustion, persistent energy limitation, and established metabolic dysfunction. Eaton-Fitch found ME/CFS more immunosuppressed versus Long COVID more immune-activated phenotypes, suggesting an activated → exhausted progression (Eaton-Fitch et al. 2024); longitudinal post-viral conditions show comparable patterns.
Citations: (Eaton-Fitch et al. 2024)
Mechanism: Progression markers: rising PD-1/CTLA-4/Tim-3 expression, worsening mitochondrial function, emerging autonomic dysfunction and neurological symptoms, and the appearance of classic PEM. Early identification of high-risk patients could enable preventive intervention.
Chapter ref: ch14d cross-disease comparisons: Long COVID to ME/CFS Progression Model
Prediction: Long COVID patients with high exhaustion-marker levels at 6 months meet ME/CFS criteria at 24 months; the activation-to-exhaustion shift correlates with functional decline; intervention within 6 months prevents progression in high-risk patients. Falsified if exhaustion markers do not prospectively identify progressors.
Treatment: Early intervention for high-risk Long COVID patients; immune-phenotype-based treatment selection; prognostic counseling. Research directions only.
Limitation: Not all Long COVID patients progress; progression timeline and predictors unknown; ME/CFS and Long COVID may be distinct conditions rather than stages. Severity not stratified. (ch14d, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.50Early Immune Profiles Predict Chronic Outcome

Evidence: Immune profiles at 4 weeks post-infection predict 12-month ME/CFS status with >80% accuracy. Candidate predictors: impaired clearance (persistent viral RNA or protein) (Peluso et al. 2024), immune dysregulation (cytokines, cell subsets) (Phetsouphanh et al. 2022), early exhaustion markers (PD-1, CTLA-4), metabolic dysfunction, autonomic signs. A composite panel should outperform single markers.
Citations: (Peluso et al. 2024) (Phetsouphanh et al. 2022) (Hickie et al. 2006b)
Mechanism: Early immune-dysregulation patterns establish the trajectory toward chronicity; a multi-parameter approach captures the post-viral transition’s complexity and enables risk stratification.
Chapter ref: ch47 proposed studies: Early Immune Profiles Predict Chronic Outcome; a separate, related ch08 label exists: Early Innate Immune Dysregulation Predicts Post-Viral Outcomes
Prediction: Prospective validation achieves >80% accuracy; specific 4-week parameters emerge as strongest predictors; the composite panel beats individual markers; prediction-guided early intervention reduces ME/CFS incidence. Falsified if no parameter set reaches useful accuracy prospectively.
Treatment: Biomarker-based risk stratification to guide early intervention during the clearance window; monitoring to track response. Research directions only.
Limitation: Requires large prospective validation cohorts; panels may need pathogen-specific adjustment; early-intervention efficacy unproven. Severity not stratified. (ch47, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.40Viral Reservoir Persistence Predicts Chronicity

Evidence: Patients who develop ME/CFS will show reservoir persistence (viral RNA or protein in gut tissue, immune cells, or other compartments) at 6 and 12 months while recoverers clear completely; reservoir presence correlates with severity and immune-activation markers. Long COVID gut-epithelium protein persistence to 12 months (Peluso et al. 2024); lymphocyte persistence after recovery in the FIPV model (Kol et al. 2026).
Citations: (Peluso et al. 2024) (Kol et al. 2026)
Mechanism: Reservoirs maintain continuous antigenic stimulation driving chronic activation; tissue persistence explains chronicity despite negative blood tests; detection provides both mechanism explanation and a treatment target.
Chapter ref: ch47 proposed studies: Viral Reservoir Persistence Predicts Chronicity; ch38 FIPV model; open question Viral RNA in Circulating Lymphocytes as Diagnostic Biomarker
Prediction: Tissue biopsies (gut, lymph node) are RNA or protein positive in progressors and negative in recoverers at 6 and 12 months; reservoir load correlates with severity and activation markers; tissue clearance precedes clinical recovery. Falsified if reservoir measures do not distinguish outcomes.
Treatment: Tissue-penetrating antivirals; immune enhancement to clear reservoir-harboring cells; monitoring. Research directions only.
Limitation: Invasive repeated biopsy sampling; causality versus correlation unclear. Severity not stratified. (ch47, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Hypothesis p=0.45Immune Activation-to-Exhaustion Transition

Evidence: Progressors show a temporal shift from immune activation (early) to exhaustion (late); recoverers resolve activation without exhaustion. Timing and rate of the shift predict outcome and explain the Eaton-Fitch finding of activated Long COVID versus exhausted ME/CFS phenotypes (Eaton-Fitch et al. 2024); longitudinal immune dysfunction across 24 months documented in Long COVID (Phetsouphanh et al. 2024).
Citations: (Eaton-Fitch et al. 2024) (Phetsouphanh et al. 2024)
Mechanism: Trajectory: initial activation → failed resolution → chronic activation → exhaustion. Transition timing and rate set the final outcome; intervention during the activation phase may prevent exhaustion.
Chapter ref: ch47 proposed studies: Immune Activation-to-Exhaustion Transition; related ch08 exhaustion content CD8+ T-cell Exhaustion as a Downstream Consequence of Exosomal EV Cargo Rather than Antigen-Driven
Prediction: Longitudinal profiling shows the transition in progressors but not recoverers; transition rate correlates with functional decline; intervention during the activation phase prevents exhaustion. Falsified if progressors and recoverers show indistinguishable trajectories.
Treatment: Phase-matched strategy: anti-inflammatory during activation, immune enhancement during exhaustion; transition-marker monitoring to time adjustments. Research directions only.
Limitation: Requires intensive longitudinal sampling; transition markers vary between individuals; optimal intervention timing unknown. Severity not stratified. (ch47, 2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Speculation p=0.30Temporal Window for Metabolic Lock Reversal

Evidence: The metabolic “safe mode” lock may become progressively harder to reverse: early in the course (first 6–12 months), trigger-removing interventions (antivirals, immune modulation) may allow disengagement; later, epigenetic and neural changes may maintain the lock regardless of trigger removal, requiring different approaches. Related lock machinery in this registry: Treatment Order Dependence: Locus-Dependent Sequencing and the ch16 metabolic safe-mode lock analysis. No direct study of reversal timing exists.
Citations: (Hickie et al. 2006a)
Mechanism: Early stage: trigger-focused intervention may suffice. Late stage: lock-focused intervention (epigenetic modulators, neuromodulation, metabolic reset) required. Would explain why antivirals help some patients and not others, and why earlier treatment tends to do better.
Chapter ref: No dedicated chapter environment (registry-only retroactive entry); nearest anchors: ch16 metabolic safe-mode lock section and Treatment Order Dependence: Locus-Dependent Sequencing
Prediction: Disease duration correlates with epigenetic stability of metabolic gene expression; early antiviral treatment shows higher response rates than late; epigenetic-age acceleration scales with duration; epigenetic-reprogramming treatments work better in long-duration patients. Falsified if response rates are duration-independent.
Treatment: Timing-stratified strategy: under 6 months trigger-focused; beyond 12 months lock-focused. Speculative framework, not a clinical recommendation.
Limitation: Lock-reversal timing speculative; epigenetic-stability markers not established; individual variation in lock formation and reversal likely; treatment efficacy unknown. Severity not stratified. (2026-05-14 cycle; retroactive registry completion 2026-10-10.)

Speculation p=0.55Glymphatic Dysfunction as a Substrate for Unrefreshing Sleep

Evidence: The first diffusion-tensor-imaging (DTI-ALPS) study of brain clearance in ME/CFS found a lower global DTI-ALPS index in patients (1.44 +- 0.086 vs 1.51 +- 0.11, p = 0.014), right-hemisphere reduction (p = 0.009), and inverse correlation with sleep disturbance (r = -0.47, p = 0.013) and impaired concentration (r = -0.43, p = 0.026) (Thapaliya et al. 2026); combined with the absence of a polysomnographic substrate for unrefreshing sleep (Mariman et al. 2013).
Citations: (Thapaliya et al. 2026) (Mariman et al. 2013)
Mechanism: Impaired glymphatic clearance during sleep co-varies with unrefreshing sleep and cognitive symptoms, supporting a disease-driven (not primary-insomnia) account of ME/CFS sleep disturbance. Cross-sectional correlation, not demonstrated causation. Reinforces the pre-existing ch03 hypothesis Unrefreshing Sleep as Failed Glymphatic Clearance.
Chapter ref: ch03 unrefreshing sleep: reinforces Unrefreshing Sleep as Failed Glymphatic Clearance
Prediction: Prospective/interventional study finds the glymphatic index tracks sleep-quality improvement or predicts cognitive decline independently; falsified if DTI-ALPS fails to replicate across sites or the sleep correlation vanishes after controlling for severity and sleep-disordered breathing.
Treatment: None directly; provides an imaging target for sleep-focused trials.
Limitation: Single site, cross-sectional, correlation not causation; severity not stratified. (ch03, 2026-10-10 cycle; first direct ME/CFS evidence reinforcing a pre-existing 0.75 hypothesis.)

Open Question p=n/aDoes Behavioural Sleep Therapy Transfer to ME/CFS?

Evidence: CBT-I in Gulf War Illness (n = 85) improved overall symptom severity, fatigue, cognitive dysfunction, depression, and anxiety, maintained at six months (Chao et al. 2021); no CBT-I or behavioural sleep trial has been conducted in ME/CFS.
Citations: (Chao et al. 2021)
Mechanism: Whether a structured behavioural sleep intervention can move non-sleep fatigue and cognitive symptoms in ME/CFS, where unrefreshing sleep is disease-driven rather than primary insomnia.
Chapter ref: ch23 sleep management: Does Behavioural Sleep Therapy Transfer to ME/CFS?
Prediction: A randomised CBT-I/behavioural sleep trial in ME/CFS shows whether sleep-targeted behaviour improves fatigue and PEM or only sleep continuity; transfer hypothesis falsified if insomnia scores improve but fatigue, PEM, and function are unchanged.
Treatment: Potential low-risk behavioural sleep programme for symptom management, unproven in ME/CFS.
Limitation: Cross-disease evidence only; severity not stratified. (ch23, 2026-10-10 cycle.)

Speculation p=0.45Mindfulness Meditation: Narrow Secondary-Outcome Efficacy

Evidence: A three-arm MBSR trial in fibromyalgia (n = 177) found no between-group difference on the primary HRQoL outcome while the mindfulness arm improved 6 of 8 secondary variables (Schmidt et al. 2011); a meta-analysis of ten mindfulness trials (n = 818) found short-term FIQ reduction (MD -6.20) and PSQI improvement (MD -1.84) that attenuated to non-significance at medium term (Meng et al. 2024).
Citations: (Schmidt et al. 2011) (Meng et al. 2024) (Porter and Jason 2022)
Mechanism: Meditation plausibly improves sleep, distress, and quality of life, without a demonstrated effect on the energy deficit defining ME/CFS; no PEM or energy-envelope endpoint has been tested in ME/CFS.
Chapter ref: ch32 lifestyle interventions (meditation-rest): Stress Management
Prediction: A mindfulness trial in ME/CFS with a PEM or energy-envelope primary endpoint shows whether meditation affects core disease; narrow-efficacy account falsified if meditation improves PEM, recovery time, or objective activity, not only sleep and distress.
Treatment: Low-risk symptom support for sleep and distress; not a core-disease treatment.
Limitation: Cross-disease (fibromyalgia) evidence, population-discounted; severity not stratified; sit-still format may be intolerable for severe/very-severe patients. (ch32, 2026-10-10 cycle.)

LIM p=n/aHeart Rate Variability Biofeedback: Mixed and Null Evidence

Evidence: HRV-BF improved severe fatigue in Long COVID (H = 4.083, p = 0.043, low drop-out) (Cossu et al. 2025); in ME/CFS an exploratory trial found fatigue improved after both HRV-BF and the graded-exercise comparator with no between-group advantage (Windthorst et al. 2017); in fibromyalgia a phase II trial (n = 64) found no between-group difference on any outcome (null) (Carta et al. 2024).
Citations: (Cossu et al. 2025) (Windthorst et al. 2017) (Carta et al. 2024)
Mechanism: Positive HRV-BF signals are small, cross-disease, or confounded by comparator effects; the disease-adjacent controlled null cautions against over-recommending HRV-BF as a core autonomic therapy.
Chapter ref: ch32 lifestyle interventions (biofeedback section)
Prediction: Not applicable — evidence summary limitation, not a falsifiable mechanism claim.
Treatment: Low-risk self-management option with contested efficacy; frame as symptom support, not validated autonomic retraining.
Limitation: Mixed/null trial evidence; severity not stratified. (ch32, 2026-10-10 cycle.)

Open Question p=n/aWhat “Recovery” Means: Three Incommensurate Referents

Evidence: Physicians define recovery as full remission to premorbid functioning (Devendorf et al. 2019); in a CBT cohort full recovery is reached by roughly one in four patients while over two in three exit diagnostic criteria — two different outcomes in the same study (Knoop et al. 2007); about 44% self-rate as “better” after CBT (Ingman et al. 2022); the “recovery conceivable” framing adds a fourth referent — a shift in illness understanding plus felt improvement and hope (Bakken, Mengshoel, and Synnes 2026).
Citations: (Devendorf et al. 2019) (Knoop et al. 2007) (Ingman et al. 2022) (Bakken, Mengshoel, and Synnes 2026)
Mechanism: The recovery-efficacy dispute is partly a definitional contest: the same study is read as showing recovery is a minority outcome (full remission), a common outcome (self-rated improvement), or a matter of orientation (conceivability). No single measure serves all three referents.
Chapter ref: ch42 psychogenic-vs-biomedical-mode: What “Recovery” Means: Three Incommensurate Referents
Prediction: A consensus (Delphi/mixed-methods) recovery taxonomy would expose whether patients, clinicians, and researchers converge or diverge on a single referent; the definitional-drift account is falsified if they converge on one operational measure.
Treatment: None directly; clarifies what any “recovery” claim in treatment research actually measures.
Limitation: Conceptual/synthesis point, not new empirical data; the referent counts are illustrative, not an exhaustive taxonomy. (ch42, 2026-10-11 cycle.)

Open Question p=n/aPost-Vaccination Syndrome: Distinct Entity or Final-Common-Pathway Phenocopy of ME/CFS

Evidence: Post-vaccination syndrome (PVS/PCVS/PACVS) is a reported ME/CFS-like syndrome (fatigue, exercise intolerance, brain fog, dysautonomia, small-fiber neuropathy) after vaccination in a small minority. Evidence of a reported ME/CFS-like phenotype (not of distinctness): German PACVS registry n=191 with 131/191 self-reportedly meeting ME/CFS criteria (survey-based) (Mundorf et al. 2024) and a single-lab antibody/IL-6 panel (sens 90%, AUC 0.82–0.85) (Semmler et al. 2023); Yale LISTEN n=241 descriptive phenotype, median onset 3 days (preprint) (Krumholz et al. 2023); Erlangen n=70 PVS vs 723 PASC near-identical (Hanc et al. 2026); Columbia (n=181 total across three groups; PACVS subgroup smaller) PACVS functional impairment comparable to ME/CFS (Purpura et al. 2026). Evidence against a distinct entity: most reported mechanisms (spike persistence/EBV, SFN, POTS) also exist in post-infectious ME/CFS/LC; anti-nerve IgG was compared against vaccinated controls, not post-infectious ME/CFS (Arlt et al. 2024); no PVS-specific mechanism/biomarker/criteria is replicated. Candidate discriminating data (non-decisive): a single-clinic comparison (Purpura et al. 2026) found in PACVS higher anticardiolipin IgM (42.9% vs 11.6%, p=0.02) and anti-U1-RNP (21.4% vs 2.3%, p=0.04) versus the Long-COVID arm (post-acute-COVID without ME/CFS), and higher atypical chief complaints (peripheral neuropathy 17.9%, tinnitus 7.1%, rash 10.7%, $p < 0.01$ — most-debilitating-symptom rates, not symptom prevalence) than the other groups (including the post-acute-COVID ME/CFS group). The antibody comparison (comparator not ME/CFS) cannot discriminate distinct entity from phenocopy; the atypical-complaints difference versus the ME/CFS group leans weakly toward distinctness. All single-centre, cross-sectional, uncorrected — preliminary; none discriminates robustly. Trigger comparisons are heterogeneous (referral-cohort prevalences plus one population sequence-symmetry odds analysis), not a population gradient (SFN 94% vs 79% (Donadio et al. 2025); POTS roughly 5× (Kwan et al. 2022), (Leys et al. 2025) opposite direction in a small series). Origin: literature.
Citations: (Mundorf et al. 2024) (Semmler et al. 2023) (Krumholz et al. 2023) (Hanc et al. 2026) (Purpura et al. 2026) (Bhattacharjee et al. 2025) (Arlt et al. 2024) (Donadio et al. 2025) (Leys et al. 2025) (Kwan et al. 2022) (C. B. Jensen et al. 2025) (D. Yang and Wang 2026)
Mechanism: Entity status unresolved; distinct-entity and final-common-pathway readings are equally compatible with current data; no current data discriminate them robustly. No mechanistic separation established between post-vaccinal and post-infectious ME/CFS-like illness.
Chapter ref: ch14d: post-vaccination syndrome
Prediction: A prospective controlled PVS cohort with ME/CFS and Long COVID comparison arms and objective outcomes separates the two readings: distinct if mechanism/autonomic/biomarker profiles diverge; phenocopy if matched post-vaccination and post-infection cases are indistinguishable within a pre-specified equivalence margin. (ch14d Is Post-Vaccination Syndrome a Distinct Entity or a Phenocopy of Post-Infectious ME/CFS?.)
Treatment: None — entity-status question, no treatment implication.
Limitation: Cohorts ambulatory and not severity-stratified (severity applicability unknown); no PVS-specific diagnostic criteria or validated biomarker panel exist. (ch14d, 2026-10-11 cycle.)

Speculation p=0.35Post-Vaccination Syndrome as a Final-Common-Pathway Manifestation of Post-Infectious ME/CFS Biology

Evidence: The mechanisms most reported in post-vaccination cohorts overlap those in ME/CFS/Long COVID: GPCR receptor antibodies + IL-6 (Semmler et al. 2023) (Mundorf et al. 2024) (which may be a non-specific B-cell activation readout, since GPCR autoantibodies are already documented in post-infectious ME/CFS (Freitag et al. 2021) (Loebel et al. 2016)); reduced somatic small-nerve-fiber density (Donadio et al. 2025); POTS (Leys et al. 2025); spike persistence with EBV reactivation (preprint) (Bhattacharjee et al. 2025). (Anti-peripheral-nerve IgG 18% vs 3% (Arlt et al. 2024) is against vaccinated controls, not a post-infectious comparison.) Comparison across cohorts (heterogeneous: referral-cohort prevalences plus one population sequence-symmetry odds analysis; not a population gradient): infection > vaccination for SFN (94% vs 79% (Donadio et al. 2025)) and POTS (roughly 5× (Kwan et al. 2022), though (Leys et al. 2025) reports the opposite direction in a small series); infection HR 2.04 vs adjuvanted vaccine HR 0.97 (Magnus et al. 2015) (non-mRNA). Heterogeneous symptom outcomes after vaccination in established Long COVID (relief 16.7%, worsening 21.4%) (Tsuchida et al. 2022) are compatible with, but do not require, a trigger-nonspecific process. Origin: literature.
Citations: (Semmler et al. 2023) (Mundorf et al. 2024) (Freitag et al. 2021) (Loebel et al. 2016) (Arlt et al. 2024) (Donadio et al. 2025) (Leys et al. 2025) (Bhattacharjee et al. 2025) (Kwan et al. 2022) (Magnus et al. 2015) (Tsuchida et al. 2022)
Mechanism: Proposed shared final-common-pathway: antigen persistence + functional autoantibodies + small-fiber neuropathy + autonomic dysfunction — with vaccination as possibly one (weaker) trigger alongside infection — a reading that may not entail a distinct cause.
Chapter ref: ch14d: post-vaccination syndrome
Prediction: In a cohort with matched post-vaccination and post-infection ME/CFS-like cases, the two groups do not differ on the mechanistic panel (functional autoantibodies, small-nerve-fiber density, autonomic testing) or on treatment-response clustering after adjusting for disease duration, with a pre-specified equivalence margin. Falsified if post-vaccination cases carry a reproducible mechanistic signature (at least three discriminating markers) absent from matched post-infection cases. This test addresses only the shared-pathway-versus-distinct-entity question and cannot separate ‘vaccination triggers weakly’ from ‘vaccination does not trigger at all’ — that requires the anti-N-serology cohort (see the OQ entry). The antigen-load sub-claim is falsified if PVS spike load is not lower than post-infectious spike load. (ch14d Vaccination as a Possible Weaker Trigger on the Shared Post-Infectious ME/CFS Pathway.)
Treatment: N/A — mechanism speculation, not a therapeutic claim.
Limitation: Competing explanations not excluded: a distinct vaccine-triggered mechanism whose readouts resemble ME/CFS; PVS being post-infectious ME/CFS misattributed to a coincident infection; reporting/recall bias in self-referred cohorts. Severity applicability unknown. (ch14d, 2026-10-11 cycle.)

LIM p=n/aPost-Vaccination Syndrome: Population Nulls, Cohort Overlap, and Causal-Inference Limits

Evidence: The causal claim that vaccination causes a ME/CFS-like syndrome is not supported. ME/CFS-endpoint registry nulls are non-mRNA: HPV→CFS/ME HR 0.86 (Feiring et al. 2017); HPV→composite autonomic-dysfunction RR 0.99 (incl. CFS RR 0.38) (Hviid et al. 2020); adjuvanted pandemic influenza vaccine HR 0.97 (Magnus et al. 2015). The mRNA-era data concern non-ME/CFS outcomes: a Danish cohort found lower post-acute symptom odds in vaccinated/unconcerned individuals (muscular-pain OR 0.56), higher only in vaccine-concerned (OR 1.23, 95% CI 0.91–1.67, not significant) (C. B. Jensen et al. 2025); post-vaccination POTS was roughly five-fold below post-infection (Kwan et al. 2022) (a small post-vaccination referral series reports the opposite direction (Leys et al. 2025)). Real harm (mRNA myocarditis) rare and far below infection risk (Patone et al. 2022). Cohort overlap: Semmler2023 ↔︎ Mundorf2024 (same Düsseldorf PACVS n=191); Krumholz2023 ↔︎ Bhattacharjee2025 (same Yale LISTEN PVS population) — not independent replications. No prospective controlled PVS cohort; receptor-antibody panel discovery-cohort-only (single lab); no RCT of any PVS treatment (D. Yang and Wang 2026).
Citations: (Feiring et al. 2017) (Hviid et al. 2020) (Magnus et al. 2015) (C. B. Jensen et al. 2025) (Kwan et al. 2022) (Leys et al. 2025) (Patone et al. 2022) (Semmler et al. 2023) (Mundorf et al. 2024) (Krumholz et al. 2023) (Bhattacharjee et al. 2025) (D. Yang and Wang 2026)
Mechanism: Epistemic: population-registry null governs the general vaccine→ME/CFS claim; cohort-positive reports are single-lab/single-registry and conflate temporal association with causation.
Chapter ref: ch14d: post-vaccination syndrome limitation; ch08 vaccination safety
Prediction: Not applicable — causal-inference limitation, not a falsifiable mechanism claim.
Treatment: N/A — guards against telling any patient that a vaccine caused their ME/CFS, nor affirmatively that it did not; the mRNA question is untested, so vaccination decisions remain individualized.
Limitation: No PVS-specific diagnostic criteria; severity applicability unknown. (ch14d/ch08, 2026-10-11 cycle.)

ID / Label	Details	Phase / Cert
syn pvs-model	Synthesis (ch14d Post-Vaccination Syndrome: A Reported Phenomenon on a Shared Pathway, Causally Untested): the PVS environments converge on a bounded reading — a minority of vaccinated people report a ME/CFS-like symptom cluster (real as a report) with a reproducible self-reported phenotype (Is Post-Vaccination Syndrome a Distinct Entity or a Phenocopy of Post-Infectious ME/CFS?), but whether this is a distinct entity or a phenocopy of post-infectious ME/CFS is unresolved, and mechanism overlap is compatible with both readings and so cannot discriminate them (Vaccination as a Possible Weaker Trigger on the Shared Post-Infectious ME/CFS Pathway). One reading among several not excluded is that vaccination may be a weaker trigger on a shared post-infectious pathway (in the sub-syndromic comparisons reported, infection showed the larger reported association (SFN and POTS, both mRNA-era, though heterogeneous and the POTS direction inconsistent); the non-mRNA registries show no detectable vaccine→ME/CFS signal; the mRNA→ME/CFS-endpoint question is untested); misattribution of post-infectious ME/CFS to a coincident silent infection is not excluded (Post-Vaccination Syndrome: Platform-Mismatched Nulls, Cohort Overlap, and Causal-Inference Limits). Critically, existing population nulls are for other vaccine platforms (HPV, adjuvanted influenza); no registry is known to have tested an mRNA vaccine against an ME/CFS endpoint, so the mRNA causal question is untested, not answered. Severity unknown. Consequence: study the reported trigger-nonspecific ME/CFS-like syndrome with a controlled cohort while explicitly not attributing it to the vaccine on current evidence.	Phase 10a / n/a

12 Contents

References

A, Mu, Thomas E. Wales, Haixia Zhou, Sorin-Valeriu Draga-Coletă, Christoph Gorgulla, Katherine A. Blackmore, Melanie J. Mittenbühler, et al. 2023. “Irisin Acts Through Its Integrin Receptor in a Two-Step Process Involving Extracellular Hsp90α.” Molecular Cell 83 (11): 1903–1920.e12. https://doi.org/10.1016/j.molcel.2023.05.008.

Abbotts, K. S. S., J. H. Hudgins, I. S. Viveros, C. T. Minson, B. W. Wilkins, and J. R. Halliwill. 2025. “Histamine-Receptor Blockade Does Not Influence the Heavy-Severe Domain Boundary and Time to Task Failure in the Severe Domain During Cycling Exercise in Adults.” Physiological Reports 13 (19): e70587. https://doi.org/10.14814/phy2.70587.

Aditi, Thomas Blackwell, Xiang Fang, Surendra Sharma, Matthew Mendoza, Alan Landay, Georgiy Golovko, and Parimal Samir. 2026. “Long-Term Risk of Dementia Following Encephalitis: A Large-Scale Retrospective Cohort Study of Electronic Health Records.” Journal of Neurology 273 (9): 511. https://doi.org/10.1007/s00415-026-14017-3.

Ahmad, I., R. Alemani, and D. L. Feinstein. 2026. “The Locus Coeruleus at the Crossroads of Inflammation and Neurodegeneration in Multiple Sclerosis.” Journal of Neuroinflammation 23 (1). https://doi.org/10.1186/s12974-026-03981-2.

Ahmed, Kashan, Sorin Tunaru, Cong Tang, Michaela Müller, Andreas Gille, Antonia Sassmann, Julien Hanson, and Stefan Offermanns. 2010. “An Autocrine Lactate Loop Mediates Insulin-Dependent Inhibition of Lipolysis Through GPR81.” Cell Metabolism 11 (4): 311–19. https://doi.org/10.1016/j.cmet.2010.02.012.

Al Maqbali, Mohammed, Mallak Al Sinani, Zahra Al Naamani, Khalid Al Badi, and Murad I Tanash. 2021. “Prevalence of Fatigue in Patients with Cancer: A Systematic Review and Meta-Analysis.” Journal of Pain and Symptom Management 61 (1): 167–189.e14. https://doi.org/10.1016/j.jpainsymman.2020.07.037.

Al Shahab, Shatha, Rawan Al Balushi, Amna Qambar, Ruqayah Abdulla, Maryam Qader, Shooq Abdulla, and Haitham Jahrami. 2025. “Efficiency of Different Supplements in Alleviating Symptoms of ADHD with or Without the Use of Stimulants: A Systematic Review.” Nutrients 17 (9): 1482. https://doi.org/10.3390/nu17091482.

Alfonsa, Hannah, Robert J. Burman, Peter J. N. Brodersen, Sarah E. Newey, Kashif Mahfooz, Tomoko Yamagata, Marios C. Panayi, David M. Bannerman, Vladyslav V. Vyazovskiy, and Colin J. Akerman. 2023. “Intracellular Chloride Regulation Mediates Local Sleep Pressure in the Cortex.” Nature Neuroscience 26 (1): 64–78. https://doi.org/10.1038/s41593-022-01214-2.

Alshdefat, Ahmad, Ahmad Ayed, Ismael Aqtam, Omowunmi Ibitoye, Sherry Vincent, and Abdulaziz A Furqani. 2026. “Saffron and Pregnancy: Cultural Practices, Beliefs, and Safety Evidence: An Integrative Review.” Journal of Evidence-Based Integrative Medicine 31: 2515690X261455162. https://doi.org/10.1177/2515690X261455162.

Alten, Rieke, Amy Grahn, Robert J Holt, Patricia Rice, and Frank Buttgereit. 2015. “Delayed-Release Prednisone Improves Fatigue and Health-Related Quality of Life: Findings from the CAPRA-2 Double-Blind Randomised Study in Rheumatoid Arthritis.” RMD Open 1 (1): e000134. https://doi.org/10.1136/rmdopen-2015-000134.

Amadieu, Camille, Quentin Leyrolle, Marta Farneti, et al. 2025. “Effect of Saffron Extract Supplementation on Mood in Healthy Adults with Subclinical Symptoms of Depression: A Randomized, Double-Blind, Placebo-Controlled Trial.” American Journal of Clinical Nutrition 122 (6): 1625–35. https://doi.org/10.1016/j.ajcnut.2025.09.050.

Amezola-Herrera, E., L. V. Galindo-Pacheco, G. P. Sosa-Bustamante, C. Paque-Bautista, A. Moctezuma-Paz, and A. P. González. 2026. “[Sleep Disorders Associated with Montelukast in Children with Respiratory Allergy].” Revista Medica Del Instituto Mexicano Del Seguro Social 64 (5): e7135. https://doi.org/10.5281/zenodo.21462729.

Andréen, Lotta, Sigrid Nyberg, Sahruh Turkmen, Guido van Wingen, Guillén Fernández, and Torbjörn Bäckström. 2009. “Sex Steroid Induced Negative Mood May Be Explained by the Paradoxical Effect Mediated by GABA-A Modulators.” Psychoneuroendocrinology 34 (8): 1121–32. https://doi.org/10.1016/j.psyneuen.2009.02.003.

Andrillon, Thomas, and Delphine Oudiette. 2023. “What Is Sleep Exactly? Global and Local Modulations of Sleep Oscillations All Around the Clock.” Neuroscience & Biobehavioral Reviews 155: 105465. https://doi.org/10.1016/j.neubiorev.2023.105465.

Antoine, Jean-Christophe, N Boutahar, F Lassablière, E Reynaud, K Ferraud, V Rogemond, S Paul, J Honnorat, and J-P Camdessanché. 2015. “Antifibroblast Growth Factor Receptor 3 Antibodies Identify a Subgroup of Patients with Sensory Neuropathy.” Journal of Neurology, Neurosurgery, and Psychiatry 86 (12): 1347–55. https://doi.org/10.1136/jnnp-2014-309730.

Aoun Sebaiti, Mouna, Paul Kauv, Anouk Charles-Nelson, Axel Van Der Gucht, Paul Blanc-Durand, Emmanuel Itti, Romain K Gherardi, Anne-Catherine Bachoud-Levi, and François-Jérôme Authier. 2018. “Cognitive Dysfunction Associated with Aluminum Hydroxide-Induced Macrophages Myofasciitis: A Reappraisal of Neuropsychological Profile.” Journal of Inorganic Biochemistry 181: 132–38. https://doi.org/10.1016/j.jinorgbio.2017.09.019.

Appelman, Brent, Braeden T. Charlton, Richie P. Goulding, Tom J. Kerkhoff, Ellen A. Breedveld, Wendy Noort, Carla Offringa, et al. 2024. “Muscle Abnormalities Worsen After Post-Exertional Malaise in Long COVID.” Nature Communications 15: 17. https://doi.org/10.1038/s41467-023-44432-3.

Appeltshauser, L, C P Moritz, L Reinhardt, L Kreß, N Üçeyler, F Lassablière, A Barcic, et al. 2025. “Inter-Laboratory Comparison of a Serum Fibroblast Growth Factor Receptor 3 (FGFR3) Antibody Test in Sensory Neuropathies.” Frontiers in Immunology 16: 1604456. https://doi.org/10.3389/fimmu.2025.1604456.

Aregawi, Mesfin, Brian Walitt, Peter Sullivan, Gustavo Norato, J. David Benjamin, and David Goldstein. 2026. “Central Noradrenergic Deficiency in Post-Infectious Chronic Fatigue: Neurobehavioral Correlates.” Brain Communications. https://doi.org/10.1093/braincomms/fcag173.

Arjunan, Darshana, Taruna Bhadada, Siddharth S Mohankumar, and Sanjay K Bhadada. 2023. “Non-Biological Antiresorptive: Bisphosphonates.” Indian Journal of Orthopaedics 57 (Suppl 1): 120–26. https://doi.org/10.1007/s43465-023-01054-7.

Arlt, Friederike A., Ameli Breuer, Elli Trampenau, Fabian Boesl, Marieluise Kirchner, Philipp Mertins, Elisa Sanchez-Sendin, et al. 2024. “High Serum Prevalence of Autoreactive IgG Antibodies Against Peripheral Nerve Structures in Patients with Neurological Post-COVID-19 Vaccination Syndrome.” Frontiers in Immunology 15: 1404800. https://doi.org/10.3389/fimmu.2024.1404800.

Arnett, Simon V, and Ian A Clark. 2012. “Inflammatory Fatigue and Sickness Behaviour - Lessons for the Diagnosis and Management of Chronic Fatigue Syndrome.” Journal of Affective Disorders 141 (2-3): 130–42. https://doi.org/10.1016/j.jad.2012.04.004.

Arnsten, Amy F T. 2011. “Catecholamine Influences on Dorsolateral Prefrontal Cortical Networks.” Biological Psychiatry 69 (12): e89–99. https://doi.org/10.1016/j.biopsych.2011.01.027.

Arnsten, Amy F. T., and Tait Shanafelt. 2021. “Physician Distress and Burnout: The Neurobiological Perspective.” Mayo Clinic Proceedings 96 (3): 763–69. https://doi.org/10.1016/j.mayocp.2020.12.027.

Asbring, Pia, and Anna-Liisa Närvänen. 2002. “Women’s Experiences of Stigma in Relation to Chronic Fatigue Syndrome and Fibromyalgia.” Qualitative Health Research 12 (2): 148–60. https://doi.org/10.1177/104973230201200202.

Asherson, Philip, and Jessica Agnew-Blais. 2019. “Annual Research Review: Does Late-Onset Attention-Deficit/Hyperactivity Disorder Exist?” Journal of Child Psychology and Psychiatry 60 (4): 333–52. https://doi.org/10.1111/jcpp.13020.

Aston-Jones, G., and J. D. Cohen. 2005. “An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance.” Annual Review of Neuroscience 28: 403–50. https://doi.org/10.1146/annurev.neuro.28.061604.135709.

Augustin, M. et al. 2021. “Post-COVID Syndrome in Non-Hospitalised Patients with COVID-19: A Longitudinal Prospective Cohort Study.” The Lancet Regional Health - Europe 6: 100122. https://doi.org/10.1016/j.lanepe.2021.100122.

Azcue, N, A Prada, R Del Pino, M Acera, T Fernandez-Valle, N Ayo-Mentxakatorre, T Perez-Concha, et al. 2026. “Involvement of Autoantibodies Against G Protein-Coupled Receptors in Post-COVID Condition and Chronic Fatigue Syndrome.” Scientific Reports 16 (1): 20609. https://doi.org/10.1038/s41598-026-49131-9.

Bader, Viola, Katharina Estermann, Eva Niess, Tobias Zrzavy, Florian Fischmeister, Teresa Haider, Birgit Ludwig, et al. 2026. “Experimental Hypoxia to Probe Neuro-Metabolic and Vascular Dysregulation in ME/CFS: A Multimodal Proof-of-Concept MRI Study.” medRxiv. https://doi.org/10.64898/2026.08.10.26359935.

Bager, Palle, Christian Lodberg Hvas, Charlotte Lock Rud, and Jens Frederik Dahlerup. 2021. “Randomised Clinical Trial: High-Dose Oral Thiamine Versus Placebo for Chronic Fatigue in Patients with Quiescent Inflammatory Bowel Disease.” Alimentary Pharmacology & Therapeutics 53 (1): 79–86. https://doi.org/10.1111/apt.16166.

Baker, S. R., G. Halliday, M. Ząbczyk, G. Alkarithi, F. L. Macrae, A. Undas, B. J. Hunt, and R. A. S. Ariëns. 2023. “Plasma from Patients with Pulmonary Embolism Show Aggregates That Reduce After Anticoagulation.” Communications Medicine 3 (1): 12. https://doi.org/10.1038/s43856-023-00242-8.

Bakheit, A M O, P O Behan, T G Dinan, C E Gray, and V O’Keane. 1992. “Possible Upregulation of Hypothalamic 5-Hydroxytryptamine Receptors in Patients with Postviral Fatigue Syndrome.” BMJ 304 (6833): 1010–12. https://doi.org/10.1136/bmj.304.6833.1010.

Bakken, Anne Karen, Anne Marit Mengshoel, and Oddgeir Synnes. 2026. “Making Recovery Conceivable: Perspectives from Healthcare Professionals Using Psychologically and Behaviourally Informed Approaches to Chronic Fatigue Syndrome.” International Journal of Qualitative Studies on Health and Well-Being 21 (1): 2745089. https://doi.org/10.1080/17482631.2026.2745089.

Baraniuk, J N, N Eaton-Fitch, and S Marshall-Gradisnik. 2024. “Meta-Analysis of Natural Killer Cell Cytotoxicity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Frontiers in Immunology 15: 1440643. https://doi.org/10.3389/fimmu.2024.1440643.

Barcik, Weronika, Marcin Wawrzyniak, Cezmi A. Akdis, and Liam O’Mahony. 2017. “Immune Regulation by Histamine and Histamine-Secreting Bacteria.” Current Opinion in Immunology 48: 108–13. https://doi.org/10.1016/j.coi.2017.08.011.

Barcomb, K., S. S. Olah, M. J. Kennedy, et al. 2022. “Properties and Modulation of Excitatory Inputs to the Locus Coeruleus.” The Journal of Physiology 600 (22): 4897–4916. https://doi.org/10.1113/JP283605.

Barnden, Leighton R., Zack Y. Shan, Donald R. Staines, Sonya Marshall-Gradisnik, Kevin Finegan, Timothy Ireland, and Sandeep Bhuta. 2018. “Hyperintense Sensorimotor T1 Spin Echo MRI Is Associated with Brainstem Abnormality in Chronic Fatigue Syndrome.” NeuroImage: Clinical 20: 102–9. https://doi.org/10.1016/j.nicl.2018.07.011.

Basta, Fabio, Antonella Afeltra, and Domenico Paolo Emanuele Margiotta. 2018. “Fatigue in Systemic Sclerosis: A Systematic Review.” Clinical and Experimental Rheumatology 36 Suppl 113 (4): 150–60.

Bateman, Lucinda, Alison C. Bested, Hector F. Bonilla, Bela V. Chheda, Lily Chu, Jennifer M. Curtin, Tania T. Dempsey, et al. 2021. “Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Essentials of Diagnosis and Management.” Mayo Clinic Proceedings 96 (11): 2861–78. https://doi.org/10.1016/j.mayocp.2021.07.004.

Bauer-Negrini, Guilherme, Douglas Teixeira Leffa, Pamela C. L. Ferreira, Bruna Bellaver, Guilherme Povala, Marina S. Medeiros, Firoza Z. Lussier, et al. 2026. “Attention-Deficit/Hyperactivity Disorder and Late-Life Cognitive Impairment.” JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2026.3165.

Baumeister, Roy F, Ellen Bratslavsky, Mark Muraven, and Dianne M Tice. 1998. “Ego Depletion: Is the Active Self a Limited Resource?” Journal of Personality and Social Psychology 74 (5): 1252–65. https://doi.org/10.1037/0022-3514.74.5.1252.

Bayliss, Kirstie, Mark Goodall, Anna Chisholm, Beth Fordham, Carolyn Chew-Graham, Lisa Riste, Louise Fisher, Karina Lovell, Sarah Peters, and Alison Wearden. 2014. “Overcoming the Barriers to the Diagnosis and Management of Chronic Fatigue Syndrome/ME in Primary Care: A Meta-Synthesis of Qualitative Studies.” BMC Family Practice 15: 44. https://doi.org/10.1186/1471-2296-15-44.

Bearn, J, T Allain, P Coskeran, N Munro, J Butler, A McGregor, and S Wessely. 1995. “Neuroendocrine Responses to d-Fenfluramine and Insulin-Induced Hypoglycemia in Chronic Fatigue Syndrome.” Biological Psychiatry 37 (4): 245–52. https://doi.org/10.1016/0006-3223(94)00121-i.

Becker, Maxi, Dimitris Repantis, Martin Dresler, and Simone Kühn. 2022. “Cognitive Enhancement: Effects of Methylphenidate, Modafinil, and Caffeine on Latent Memory and Resting State Functional Connectivity in Healthy Adults.” Human Brain Mapping 43 (14): 4225–38. https://doi.org/10.1002/hbm.25949.

Becker, Sara, Md. M. Chowdhury, P. Tavilsup, Dallas Seitz, and Brandy L. Callahan. 2023. “Risk of Neurodegenerative Disease or Dementia in Adults with Attention-Deficit/Hyperactivity Disorder: A Systematic Review.” Frontiers in Psychiatry 14: 1158546. https://doi.org/10.3389/fpsyt.2023.1158546.

Becker, Sara, M. J. Sharma, and Brandy L. Callahan. 2022. “ADHD and Neurodegenerative Disease Risk: A Critical Examination of the Evidence.” Frontiers in Aging Neuroscience 13: 826213. https://doi.org/10.3389/fnagi.2021.826213.

Bedard, Patrick, Kristine M. Knutson, Patrick M. McGurrin, Felipe Vial, Traian Popa, Silvina G. Horovitz, Mark Hallett, Avindra Nath, and Brian Walitt. 2026. “Central Origin of Fatigability in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Revealed by Multimodal Neuroimaging.” NeuroImage: Clinical 51: 104041. https://doi.org/10.1016/j.nicl.2026.104041.

Ben-Jonathan, N, and R Hnasko. 2001. “Dopamine as a Prolactin (PRL) Inhibitor.” Endocrine Reviews 22 (6): 724–63. https://doi.org/10.1210/edrv.22.6.0451.

Bhattacharjee, Bornali, Peiwen Lu, Valter Silva Monteiro, Alexandra Tabachnikova, Kaisha Wang, William B. Hooper, Valter Bastos, Kaitlin Greene, et al. 2025. “Immunological and Antigenic Signatures Associated with Chronic Illnesses After COVID-19 Vaccination.” medRxiv. https://doi.org/10.1101/2025.02.18.25322379.

Biswal, Bharat, Priyanka Kunwar, and Benjamin H. Natelson. 2011. “Cerebral Blood Flow Is Reduced in Chronic Fatigue Syndrome as Assessed by Arterial Spin Labeling.” Journal of the Neurological Sciences 301 (1-2): 9–11. https://doi.org/10.1016/j.jns.2010.11.018.

Bizjak, Daniel Alexander, Birgit Ohmayer, Jasmine Leonike Buhl, Elisabeth Marion Schneider, Paul Walther, Enrico Calzia, Achim Jerg, Lynn Matits, and Jürgen Michael Steinacker. 2024. “Functional and Morphological Differences of Muscle Mitochondria in Chronic Fatigue Syndrome and Post-COVID Syndrome.” International Journal of Molecular Sciences 25 (3): 1675. https://doi.org/10.3390/ijms25031675.

Blanco, Marina B, Lydia K Greene, Kayla H Welser, Erin E Ehmke, Anne D Yoder, and Peter H Klopfer. 2024. “Primate Hibernation: The Past, Present, and Promise of Captive Dwarf Lemurs.” Annals of the New York Academy of Sciences 1540 (1): 178–90. https://doi.org/10.1111/nyas.15206.

Blockmans, Daniel, Philippe Persoons, Boudewijn Van Houdenhove, and Herman Bobbaers. 2006. “Does Methylphenidate Reduce the Symptoms of Chronic Fatigue Syndrome?” American Journal of Medicine 119 (2): 167.e23–30. https://doi.org/10.1016/j.amjmed.2005.07.047.

Boldrini, Maura, Camille A. Fulmore, Alexandria N. Tartt, Laika R. Simeon, Iva Pavlova, Verica Poposka, Gorazd B. Rosoklija, et al. 2018. “Human Hippocampal Neurogenesis Persists Throughout Aging.” Cell Stem Cell 22 (4): 589–99. https://doi.org/10.1016/j.stem.2018.03.015.

Boldrini, Maura, Camille A. Fulmore, Alexandria N. Tartt, Laika R. Simeon, Iva Pavlova, Verica Poposka, Gorazd B. Rosoklija, et al. 2019. “Resilience Is Associated with Larger Dentate Gyrus, While Suicide Decedents with Major Depressive Disorder Have Fewer Granule Neurons.” Biological Psychiatry 85 (10): 850–62. https://doi.org/10.1016/j.biopsych.2018.12.022.

Bolland, Mark J, Alison Avenell, John A Baron, Andrew Grey, Graeme S MacLennan, Gregory D Gamble, and Ian R Reid. 2010. “Effect of Calcium Supplements on Risk of Myocardial Infarction and Cardiovascular Events: Meta-Analysis.” BMJ 341: c3691. https://doi.org/10.1136/bmj.c3691.

Boneva, Roumiana S, Jin-Mann Lin, and Elizabeth R Unger. 2011. “Gynecological History in Chronic Fatigue Syndrome: A Population-Based Case-Control Study.” Journal of Women’s Health 20 (1): 21–28. https://doi.org/10.1089/jwh.2009.1900.

Boneva, Roumiana S, Jin-Mann Lin, Florian Wieser, Urs M Nater, Beate Ditzen, Rebecca N Taylor, and Elizabeth R Unger. 2015. “Early Menopause and Other Gynecologic Risk Indicators for Chronic Fatigue Syndrome in Women.” Menopause 22 (8): 826–34. https://doi.org/10.1097/GME.0000000000000411.

Bontempo, Alyssa C., Jennifer M. Bontempo, and Paul R. Duberstein. 2025. “Ignored, Dismissed, and Minimized: Understanding the Harmful Consequences of Invalidation in Health Care—a Systematic Meta-Synthesis of Qualitative Research.” Psychological Bulletin 151 (4): 399–427. https://doi.org/10.1037/bul0000473.

Bosch, E L, Iris E C Sommer, and Daan J Touw. 2025. “The Influence of Female Sex and Estrogens on Drug Pharmacokinetics: What Is the Evidence?” Expert Opinion on Drug Metabolism & Toxicology 21 (6): 637–47. https://doi.org/10.1080/17425255.2025.2481891.

Boström, Pontus, Jun Wu, Mark P. Jedrychowski, Anisha Korde, Li Ye, James C. Lo, Kyle A. Rasbach, et al. 2012. “A PGC1-α-Dependent Myokine That Drives Brown-Fat-Like Development of White Fat and Thermogenesis.” Nature 481 (7382): 463–68. https://doi.org/10.1038/nature10777.

Bou-Holaigah, I., P. C. Rowe, J. Kan, and H. Calkins. 1995. “The Relationship Between Neurally Mediated Hypotension and the Chronic Fatigue Syndrome.” JAMA 274 (12): 961–67. https://doi.org/10.1001/jama.1995.03530120053041.

Bourke, Ann, William G. Dixon, Andrew Roddam, Kueiyu Joshua Lin, Gillian C. Hall, Jeffrey R. Curtis, Sabine N. van der Veer, et al. 2020. “Incorporating Patient Generated Health Data into Pharmacoepidemiological Research.” Pharmacoepidemiology and Drug Safety 29 (12): 1540–49. https://doi.org/10.1002/pds.5169.

Bowman, Elise, David Coghill, Carsten Murawski, and Peter Bossaerts. 2023. “Not so Smart? ‘Smart’ Drugs Increase the Level but Decrease the Quality of Cognitive Effort.” Science Advances 9 (28): eadd4165. https://doi.org/10.1126/sciadv.add4165.

Braley, Tiffany J., Dawn M. Ehde, Kevin N. Alschuler, Robert Little, et al. 2024. “Comparative Effectiveness of Cognitive Behavioural Therapy, Modafinil, and Their Combination for Treating Fatigue in Multiple Sclerosis (COMBO-MS): A Randomised, Statistician-Blinded, Parallel-Arm Trial.” The Lancet Neurology 23 (11): 1108–18. https://doi.org/10.1016/S1474-4422(24)00354-5.

Bramante, Carolyn T, John B Buse, David M Liebovitz, Jacinda M Nicklas, Michael A Puskarich, Ken Cohen, et al. 2023. “Outpatient Treatment of COVID-19 and Incidence of Post-COVID-19 Condition over 10 Months (COVID-OUT): Randomised, Quadruple-Blind, Placebo-Controlled Trial.” The Lancet Infectious Diseases 23 (10): 1119–29. https://doi.org/10.1016/S1473-3099(23)00299-2.

Breda, V, Luis Augusto Rohde, Ana M B Menezes, Luciana Anselmi, Arthur Caye, Diego L Rovaris, Eugenia S Vitola, Claiton H D Bau, and Eugenio H Grevet. 2020. “Revisiting ADHD Age-of-Onset in Adults: To What Extent Should We Rely on the Recall of Childhood Symptoms?” Psychological Medicine 50 (5): 857–66. https://doi.org/10.1017/S003329171900076X.

Briese, Thomas, Rafal Tokarz, Lucinda Bateman, Xiaoyu Che, Cheng Guo, Komal Jain, Vishal Kapoor, et al. 2023. “A Multicenter Virome Analysis of Blood, Feces, and Saliva in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Medical Virology 95 (8): e28993. https://doi.org/10.1002/jmv.28993.

Brigo, Natascha, Wolfram Mayr, Maja Taenzer, Judith Löffler-Ragg, Andrea Schroll, Sabine Engl, Burkhard Schütz, et al. 2025. “Concentrations of Uremic Bacterial Metabolites in Patients with Post-COVID-19 Syndrome.” Frontiers in Cellular and Infection Microbiology 15: 1582972. https://doi.org/10.3389/fcimb.2025.1582972.

Brooks, George A. 2018. “The Science and Translation of Lactate Shuttle Theory.” Cell Metabolism 27 (4): 757–85. https://doi.org/10.1016/j.cmet.2018.03.008.

Brown, Abigail A, Meredyth A Evans, and Leonard A Jason. 2013. “Examining the Energy Envelope and Associated Symptom Patterns in Chronic Fatigue Syndrome: Does Coping Matter?” Chronic Illness 9 (4): 302–11. https://doi.org/10.1177/1742395313478220.

Brown, Abigail, and Leonard A. Jason. 2020. “Meta-Analysis Investigating Post-Exertional Malaise Between Patients and Controls.” Journal of Health Psychology 25 (13–14): 2053–71. https://doi.org/10.1177/1359105318784161.

Bu, S., B. Mou, Z. Xiang, L. Zhang, et al. 2025. “Hyperglycemia Impairs Microglia Responding to Retinal Vasculopathy via Enhanced Norepinephrine-ADRB2 Signaling.” Journal of Neuroinflammation 23 (1): 14. https://doi.org/10.1186/s12974-025-03647-5.

Bubak, Andrew N., Charlotte Warren-Gash, Cristina Tommasi, Judith Breuer, Ravi Mahalingam, Padma Srikanth, and Maria A. Nagel. 2026. “Varicella Zoster Virus Infection.” Nature Reviews Disease Primers 12 (1): article 73. https://doi.org/10.1038/s41572-026-00735-5.

Buchwald, Dedra, Rhoda L Ashley, Terry Pearlman, Phalla Kith, and Anthony L Komaroff. 1996. “Viral Serologies in Patients with Chronic Fatigue and Chronic Fatigue Syndrome.” Journal of Medical Virology 50 (1): 25–30. https://doi.org/10.1002/(SICI)1096-9071(199609)50:1<25::AID-JMV6>3.0.CO;2-V.

Burnet, Richard B., and Brian E. Chatterton. 2004. “Gastric Emptying Is Slow in Chronic Fatigue Syndrome.” BMC Gastroenterology 4: 32. https://doi.org/10.1186/1471-230X-4-32.

Calabrese, Edward J. 2002. “Hormesis: Changing View of the Dose-Response, a Personal Account of the History and Current Status.” Mutation Research 511 (3): 181–89. https://doi.org/10.1016/S1383-5742(02)00013-3.

———. 2010. “Hormesis Is Central to Toxicology, Pharmacology and Risk Assessment.” Human & Experimental Toxicology 29 (4): 249–61. https://doi.org/10.1177/0960327109363973.

Calabrese, Edward J, and Linda A Baldwin. 2003. “Toxicology Rethinks Its Central Belief.” Nature 421 (6924): 691–92. https://doi.org/10.1038/421691a.

Calfee, Carolyn S., Kevin L. Delucchi, Pratik Sinha, Michael A. Matthay, Jonathan Hackett, Manu Shankar-Hari, Cliona McDowell, John G. Laffey, Cecilia M. O’Kane, and Daniel F. McAuley. 2018. “Acute Respiratory Distress Syndrome Subphenotypes and Differential Response to Simvastatin: Secondary Analysis of a Randomised Controlled Trial.” The Lancet Respiratory Medicine 6 (9): 691–98. https://doi.org/10.1016/S2213-2600(18)30177-2.

Cambras, Trinitat, Joan Carles Domingo, Ramon Sanmartin-Sentanes, Jose Alegre-Martin, and Jesus Castro-Marrero. 2026. “Association Between Light Exposure Patterns and Multidimensional Health Outcomes in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Findings from an Observational Cross-Sectional Cohort Study.” Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08556-6.

Cameron, D. J. 2006. “Generalizability in Two Clinical Trials of Lyme Disease.” Epidemiologic Perspectives and Innovations 3: 12. https://doi.org/10.1186/1742-5573-3-12.

Carbone, Manuel Glauco, Giovanni Pagni, Claudia Tagliarini, Icro Maremmani, and Angelo Giovanni Icro Maremmani. 2025. “Caffeine in Aging Brains: Cognitive Enhancement, Neurodegeneration, and Emerging Concerns about Addiction.” International Journal of Environmental Research and Public Health 22 (8): 1171. https://doi.org/10.3390/ijerph22081171.

Carta, Mauro Giovanni, Giorgia Testa, Serena Stocchino, Gabriele Finco, Federica Sancassiani, Maria Teresa Littera, Maria Cristina Deidda, Antonio Ventriglio, Dinesh Bhugra, and Giulia Cossu. 2024. “The Efficacy of Heart Rate Variability Biofeedback Training on Sleep Disorders and Impact of Fibromyalgia: Results of a Phase II Randomized Controlled Trial.” Journal of Psychosomatic Research 181: 111664. https://doi.org/10.1016/j.jpsychores.2024.111664.

Castro-Marrero, Jesús, Mario D Cordero, María J Segundo, Naia Sáez-Francàs, Natalia Calvo, Lorena Román-Malo, Luis Aliste, Tomás Fernández de Sevilla, and José Alegre. 2021. “Effect of Coenzyme Q10 Plus Nicotinamide Adenine Dinucleotide Supplementation on Maximum Heart Rate After Exercise Testing in Chronic Fatigue Syndrome - a Randomized, Controlled, Double-Blind Trial.” Clinical Nutrition 40 (5): 2898–2906. https://doi.org/10.1016/j.clnu.2021.03.012.

Căuș, Marius Nicolae, Mihaela Lupoae, and Carmen Lidia Chițescu. 2026. “Efficacy and Safety of Herbal Supplements with Anxiolytic, Antidepressant, and Sedative Action: A Review of Clinical Data.” Pharmaceuticals (Basel) 19 (3): 399. https://doi.org/10.3390/ph19030399.

Chao, Linda L., Jennifer C. Kanady, Nicole Crocker, Laura D. Straus, Jennifer Hlavin, Thomas J. Metzler, Shira Maguen, and Thomas C. Neylan. 2021. “Cognitive Behavioral Therapy for Insomnia in Veterans with Gulf War Illness: Results from a Randomized Controlled Trial.” Life Sciences 279: 119147. https://doi.org/10.1016/j.lfs.2021.119147.

Charlton, Braeden T., Richie P. Goulding, Richard T. Jaspers, Brent Appelman, Michèle van Vugt, and Rob C. I. Wüst. 2025. “Skeletal Muscle Adaptations and Post-Exertional Malaise in Long COVID.” Trends in Endocrinology & Metabolism 36 (7): 614–22. https://doi.org/10.1016/j.tem.2024.11.008.

Charlton, Braeden T., Anouk Slaghekke, Brent Appelman, Moritz Eggelbusch, Jelle Y. Huijts, Wendy Noort, Paul W. Hendrickse, et al. 2026. “Skeletal Muscle Properties in Long COVID and ME/CFS Differ from Those Induced by Bed Rest.” Nature Communications. https://doi.org/10.1038/s41467-026-75725-y.

Chen, Wei-Nan, Cheng-Han Lee, Shing-Hong Lin, Chia-Wen Wong, Wei-Hsin Sun, John N. Wood, and Chih-Cheng Chen. 2014. “Roles of ASIC3, TRPV1, and NaV1.8 in the Transition from Acute to Chronic Pain in a Mouse Model of Fibromyalgia.” Molecular Pain 10: 40. https://doi.org/10.1186/1744-8069-10-40.

Chen, WenChieh, Martin Mempel, Wolfgang Schober, and Heidrun Behrendt. 2008. “Gender Difference, Sex Hormones, and Immediate Type Hypersensitivity Reactions.” Allergy 63 (11): 1418–27. https://doi.org/10.1111/j.1398-9995.2008.01880.x.

Cherneva, Dilyana I., Gabriela Kehayova, Simona Dimitrova, and Stela Dragomanova. 2025. “The Central Nervous System Modulatory Activities of N-Acetylcysteine: A Synthesis of Two Decades of Research.” Current Issues in Molecular Biology 47 (9): 710. https://doi.org/10.3390/cimb47090710.

Cheung, Karoline, Patria Hume, and Linda Maxwell. 2003. “Delayed Onset Muscle Soreness: Treatment Strategies and Performance Factors.” Sports Medicine 33 (2): 145–64. https://doi.org/10.2165/00007256-200333020-00005.

Chojnacki, Marcin, Aleksandra Blonska, Aleksandra Kaczka, Jan Chojnacki, Ewa Walecka-Kapica, Natalia Romanowska, Karolina Przybylowska-Sygut, and Tomasz Poplawski. 2025. “Assessment of the Gut Microbiome in Patients with Coexisting Irritable Bowel Syndrome and Chronic Fatigue Syndrome.” Nutrients 17 (13): 2232. https://doi.org/10.3390/nu17132232.

Chompoopong, P, M Rezk, I Mirman, S E Berini, P J B Dyck, M Mauermann, K Shouman, C Klein, J R Mills, and D Dubey. 2023. “TS-HDS Autoantibody: Clinical Characterization and Utility from Real-World Tertiary Care Center Experience.” Journal of Neurology 270 (9): 4523–28. https://doi.org/10.1007/s00415-023-11798-9.

Chong, Trevor T.-J., Emily Fortunato, and Mark A. Bellgrove. 2023. “Amphetamines Improve the Motivation to Invest Effort in Attention-Deficit/Hyperactivity Disorder.” Journal of Neuroscience 43 (41): 6898–908. https://doi.org/10.1523/JNEUROSCI.0982-23.2023.

Cipriani, Andrea, Toshi A. Furukawa, Georgia Salanti, Anna Chaimani, Lauren Z. Atkinson, Yusuke Ogawa, et al. 2018. “Comparative Efficacy and Acceptability of 21 Antidepressant Drugs for the Acute Treatment of Adults with Major Depressive Disorder: A Systematic Review and Network Meta-Analysis.” The Lancet 391 (10128): 1357–66. https://doi.org/10.1016/S0140-6736(17)32802-7.

Clarkson, Priscilla M., and Monica J. Hubal. 2002. “Exercise-Induced Muscle Damage in Humans.” American Journal of Physical Medicine & Rehabilitation 81 (11): S52–69. https://doi.org/10.1097/00002060-200211001-00007.

Cleare, A J, J Bearn, T Allain, A McGregor, S Wessely, R M Murray, and V O’Keane. 1995. “Contrasting Neuroendocrine Responses in Depression and Chronic Fatigue Syndrome.” Journal of Affective Disorders 34 (4): 283–89. https://doi.org/10.1016/0165-0327(95)00026-j.

Cleare, Anthony J., Veronica O’Keane, and John Miell. 2001. “Plasma Leptin in Chronic Fatigue Syndrome and a Placebo-Controlled Study of the Effects of Hydrocortisone.” Clinical Endocrinology 55 (1): 113–19. https://doi.org/10.1046/j.1365-2265.2001.01341.x.

Cohen, Jack, Aparna Mathew, Katerina D. Dourvetakis, Estefania Sanchez-Guerrero, Rajendra P. Pangeni, Narasimman Gurusamy, Kristina K. Aenlle, et al. 2024. “Recent Research Trends in Neuroinflammatory and Neurodegenerative Disorders.” Cells 13 (6): 511. https://doi.org/10.3390/cells13060511.

Colegio, Oscar R., Ngoc-Quynh Chu, Alison L. Szabo, Thach Chu, Anne Marie Rhebergen, Vikram Jairam, Ruslan Medzhitov, et al. 2014. “Functional Polarization of Tumour-Associated Macrophages by Tumour-Derived Lactic Acid.” Nature 513 (7519): 559–63. https://doi.org/10.1038/nature13490.

Colgan, Dana Dharmakaya, Diane D. Stadler, Tabitha Grow, Mary Ruddick, Thomas Weimbs, Todd E. Davenport, and Heather Zwickey. 2026. “Telehealth-Based Ketogenic Metabolic Therapy with Lifestyle Interventions for Post-Viral Illness: A Research Brief.” Journal of Patient Experience 13: 23743735261459218. https://doi.org/10.1177/23743735261459218.

Collin, Simon M, Kate Tilling, Carol Joinson, Katharine A Rimes, Rebecca M Pearson, Rachael A Hughes, Jonathan A C Sterne, and Esther M Crawley. 2015. “Maternal and Childhood Psychological Factors Predict Chronic Disabling Fatigue at Age 13 Years.” Journal of Adolescent Health 56 (2): 181–87. https://doi.org/10.1016/j.jadohealth.2014.09.002.

Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. 2015. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. Washington, DC: National Academies Press. https://doi.org/10.17226/19012.

Compton, Sabrina, Rodolf Alkabalan, Judd Cadet, Azin Mastali, and Prakash V A K Ramdass. 2025. “Endometriosis and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Systematic Review and Meta-Analysis.” Diagnostics 15 (18): 2332. https://doi.org/10.3390/diagnostics15182332.

Conroy, Kristina E., Mohammed F. Islam, and Leonard A. Jason. 2023. “Evaluating Case Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): Toward an Empirical Case Definition.” Disability and Rehabilitation 45 (5): 840–47. https://doi.org/10.1080/09638288.2022.2043462.

Cools, Roshan, and Mark D’Esposito. 2011. “Inverted-U-Shaped Dopamine Actions on Human Working Memory and Cognitive Control.” Biological Psychiatry 69 (12): e113–25. https://doi.org/10.1016/j.biopsych.2011.03.028.

Corrado, G., G. Riezzo, P. Rea, C. Pacchiarotti, M. Cavaliere, and E. Cardi. 1998. “Normal Gastric Emptying Time and Myoelectrical Activity in an Adolescent with Chronic Fatigue Syndrome.” Italian Journal of Gastroenterology and Hepatology 30 (4): 444–45.

Cossu, Giulia, Goce Kalcev, Diego Primavera, Stefano Lorrai, Alessandra Perra, Alessia Galetti, et al. 2025. “The Use of Heart Rate Variability-Biofeedback (HRV-BF) as an Adjunctive Intervention in Chronic Fatigue Syndrome (CSF/ME) in Long COVID: Results of a Phase II Controlled Feasibility Trial.” Journal of Clinical Medicine 14 (15): 5363. https://doi.org/10.3390/jcm14155363.

Cox, Brandon S, Khaled Alharshawi, Irene Mena-Palomo, William P Lafuse, and Maria Eugenia Ariza. 2022. “EBV/HHV-6A dUTPases Contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Pathophysiology by Enhancing TFH Cell Differentiation and Extrafollicular Activities.” JCI Insight 7 (11): e158193. https://doi.org/10.1172/jci.insight.158193.

Crosby, Lauren D., Kara L. Kalantar, and Joseph L. DeRisi. 2021. “Off Label Use of Aripiprazole Shows Promise as a Treatment for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Retrospective Study of 101 Patients Treated with a Low Dose of Aripiprazole.” Journal of Translational Medicine 19 (1): 50. https://doi.org/10.1186/s12967-021-02721-9.

Cruccioli, M. M., P. H. T. Carneiro, J. F. J. Pitanga, Y. I. Rossi, et al. 2026. “Amantadine for Multiple Sclerosis-Related Fatigue: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.” Neurodegenerative Disease Management 16 (5): 643–49. https://doi.org/10.1080/17582024.2025.2607678.

Cunnane, Stephen, Scott Nugent, Mélanie Roy, Alexandre Courchesne-Loyer, Etienne Croteau, Sébastien Tremblay, Christian-Alexandre Castellano, et al. 2011. “Brain Fuel Metabolism, Aging, and Alzheimer’s Disease.” Nutrition 27 (1): 3–20. https://doi.org/10.1016/j.nut.2010.07.021.

Cutolo, Maurizio. 2019. “Circadian Rhythms and Rheumatoid Arthritis.” Joint Bone Spine 86 (3): 327–33. https://doi.org/10.1016/j.jbspin.2018.09.003.

Cutroneo, Paola M., Debora Sartori, Marco Tuccori, Salvatore Crisafulli, Vera Battini, Carla Carnovale, Concetta Rafaniello, et al. 2023. “Conducting and Interpreting Disproportionality Analyses Derived from Spontaneous Reporting Systems.” Frontiers in Drug Safety and Regulation 3: 1323057. https://doi.org/10.3389/fdsfr.2023.1323057.

Cysique, Lucette A., David Jakabek, Stephanie G. Bracken, Yana Allen-Davidian, Benjamin Heng, Simone Chow, Mona Dehhaghi, et al. 2023. “The Kynurenine Pathway Relates to Post-Acute COVID-19 Objective Cognitive Impairment and PASC.” Annals of Clinical and Translational Neurology 10 (8): 1338–52. https://doi.org/10.1002/acn3.51825.

Czesnik, Dirk, James Howells, Francesco Negro, Hajo Wagenknecht, David Burke, and Hugh Bostock. 2015. “Increased HCN Channel Driven Inward Rectification in Benign Cramp Fasciculation Syndrome.” Brain 138 (Pt 11): 3168–79. https://doi.org/10.1093/brain/awv250.

D’Silva, Adrijana, Danielle E Fox, Yasmin Nasser, Jeff K Vallance, Robert R Quinn, Paul E Ronksley, and Maitreyi Raman. 2022. “Prevalence and Risk Factors for Fatigue in Adults with Inflammatory Bowel Disease: A Systematic Review with Meta-Analysis.” Clinical Gastroenterology and Hepatology 20 (5): 995–1009.e7. https://doi.org/10.1016/j.cgh.2021.06.034.

Dastan, Farzaneh, Jamshid Salamzadeh, Jalal Heshmatnia, and Reza Mahmoudian. 2025. “Evaluating the Effects of Crocus sativus l. Herbal Product on Chronic Fatigue Syndrome in Patients with Chronic Obstructive Pulmonary Disease.” Iranian Journal of Pharmaceutical Research 24 (1): e165333. https://doi.org/10.5812/ijpr-165333.

Davenport, Todd E., Lily Chu, Staci R. Stevens, Jared Stevens, Christopher R. Snell, and J. Mark Van Ness. 2023. “Two Symptoms Can Accurately Identify Post-Exertional Malaise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Work 74 (4): 1199–1213. https://doi.org/10.3233/WOR-220554.

Davis, Hannah E., Lisa McCorkell, Julia Moore Vogel, and Eric J. Topol. 2023. “Long COVID: Major Findings, Mechanisms and Recommendations.” Nature Reviews Microbiology 21 (3): 133–46. https://doi.org/10.1038/s41579-022-00846-2.

Davis, Holly Louisa, Samer Alabed, and Timothy James Ainsley Chico. 2020. “Effect of Sports Massage on Performance and Recovery: A Systematic Review and Meta-Analysis.” BMJ Open Sport & Exercise Medicine 6 (1): e000614. https://doi.org/10.1136/bmjsem-2019-000614.

Davis, Leah, Megan Higgs, Alice Snaith, Tiffany A. Lodge, Jessica Strong, José Antonio Espejo-Oltra, Slawomir Kujawski, et al. 2025. “Dysregulation of Lipid Metabolism, Energy Production, and Oxidative Stress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, Gulf War Syndrome and Fibromyalgia.” Frontiers in Neuroscience 19: 1498981. https://doi.org/10.3389/fnins.2025.1498981.

Dayal, Monika, Maulin S. Shah, Niloofar Radfar, Smit Patel, and Roshny Thomas. 2025. “Multimodal Management of Supine Hypertension with Orthostatic Hypotension in an Elderly Male Patient with Parkinson’s Disease.” Cureus 17 (4): e82394. https://doi.org/10.7759/cureus.82394.

De la Fuente Muñoz, Marina, Marta Román-Carmena, Sara Amor, Ángel L García-Villalón, Ana E Espinel, Daniel González-Hedström, and Miriam Granado García. 2023. “Effects of Supplementation with the Standardized Extract of Saffron (Affron) on the Kynurenine Pathway and the Synthesis of Melatonin in Rats.” Antioxidants (Basel) 12 (8): 1619. https://doi.org/10.3390/antiox12081619.

De Monte, Celeste, Simone Carradori, Paola Chimenti, Daniela Secci, Luisa Mannina, Federica Alcaro, Anél Petzer, Chunderika N’Da, et al. 2014. “New Insights into the Biological Properties of Crocus sativus l.: Chemical Modifications, Human Monoamine Oxidases Inhibition and Molecular Modeling.” European Journal of Medicinal Chemistry 82: 164–71. https://doi.org/10.1016/j.ejmech.2014.05.038.

Deale, Alicia, and Simon Wessely. 2001. “Patients’ Perceptions of Medical Care in Chronic Fatigue Syndrome.” Social Science & Medicine 52 (12): 1859–64. https://doi.org/10.1016/S0277-9536(00)00302-6.

Debourdeau, Antoine, Jean-Michel Gonzalez, Florence Mathias, Christophe Prost, Marc Barthet, and Véronique Vitton. 2024. “Gastric Volumetry for the Assessment of Fundic Compliance and Visceral Hypersensitivity in Patients with Gastroparesis: A Retrospective Comparative Study.” Scandinavian Journal of Gastroenterology 59 (3): 254–59. https://doi.org/10.1080/00365521.2023.2279928.

DeBrabander, Kilee M., Kerrianne E. Morrison, Desiree R. Jones, Daniel J. Faso, Michael Chmielewski, and Noah J. Sasson. 2019. “Do First Impressions of Autistic Adults Differ Between Autistic and Nonautistic Observers?” Autism in Adulthood 1 (4): 250–57. https://doi.org/10.1089/aut.2019.0018.

DecodeME Consortium, Chris P Ponting, et al. 2025. “Initial Findings from the DecodeME Genome-Wide Association Study of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” medRxiv. https://doi.org/10.1101/2025.08.06.25333109v1.

Del Campo, N., S. R. Chamberlain, B. J. Sahakian, et al. 2011. “The Roles of Dopamine and Noradrenaline in the Pathophysiology and Treatment of Attention-Deficit/Hyperactivity Disorder.” Biological Psychiatry 69 (12): e145–57. https://doi.org/10.1016/j.biopsych.2011.02.036.

DeLong, A. K., B. Blossom, E. L. Maloney, and S. E. Phillips. 2012. “Antibiotic Retreatment of Lyme Disease in Patients with Persistent Symptoms: A Biostatistical Review of Randomized, Placebo-Controlled, Clinical Trials.” Contemporary Clinical Trials 33 (6): 1132–42. https://doi.org/10.1016/j.cct.2012.08.009.

Devendorf, Andrew R., Carly T. Jackson, Madison Sunnquist, and Leonard A. Jason. 2019. “Defining and Measuring Recovery from Myalgic Encephalomyelitis and Chronic Fatigue Syndrome: The Physician Perspective.” Disability and Rehabilitation 41 (2): 158–65. https://doi.org/10.1080/09638288.2017.1383518.

Dicks, L. M. T. 2022. “Gut Bacteria and Neurotransmitters.” Microorganisms 10 (9). https://doi.org/10.3390/microorganisms10091838.

Dinan, T G, L V Scott, J Thakore, J Naesdal, and P W Keeling. 2001. “Impact of Cortisol on Buspirone Stimulated Prolactin Release: A Double-Blind Placebo-Controlled Study.” Psychoneuroendocrinology 26 (7): 751–56. https://doi.org/10.1016/S0306-4530(01)00029-4.

Dobrosavljevic, Maja, Le Zhang, Miguel Garcia-Argibay, Ebba Du Rietz, Henrik Andershed, Zheng Chang, Stephen Faraone, and Henrik Larsson. 2021. “Attention-Deficit/Hyperactivity Disorder as a Risk Factor for Dementia and Mild Cognitive Impairment: A Population-Based Register Study.” European Psychiatry 65 (1): 1–19. https://doi.org/10.1192/j.eurpsy.2021.2261.

Dodge-Kafka, Kimberly L., Matthew G. Turcotte, Stephanie M. Possidento, et al. 2026. “Β-Adrenergic Receptors: Not Always Outside-in.” Physiology 41 (4). https://doi.org/10.1152/physiol.00026.2025.

Domingo, Joan Carles, Federica Battistini, Begona Cordobilla, Maria Cleofe Zaragoza, Ramon Sanmartin-Sentanes, Jose Alegre-Martin, Trinitat Cambras, and Jesus Castro-Marrero. 2024. “Association of Circulating Biomarkers with Illness Severity Measures Differentiates Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Post-COVID-19 Condition: A Prospective Pilot Cohort Study.” Journal of Translational Medicine 22 (1): 343. https://doi.org/10.1186/s12967-024-05148-0.

Domingues, T. D., J. Malato, A. D. Grabowska, J. S. Lee, J. Ameijeiras-Alonso, P. Biecek, L. Graça, et al. 2023. “Association Analysis Between Symptomology and Herpesvirus IgG Antibody Concentrations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Multiple Sclerosis.” Heliyon 9 (7): e18250. https://doi.org/10.1016/j.heliyon.2023.e18250.

Donadio, Vincenzo, Alex Incensi, Alessandro Furia, Sara Parisini, Francesco Colaci, Maria Pia Giannoccaro, Luana Morelli, et al. 2025. “Small Fiber Neuropathy Associated with COVID-19 Infection and Vaccination: A Prospective Case-Control Study.” European Journal of Neurology 32 (1): e16538. https://doi.org/10.1111/ene.16538.

Dorczok, Marie Celine, Nilufar Mossaheb, Gloria Mittmann, Marina F. Thomas, Lucie Bartova, Beate Schrank, and Verena Steiner-Hofbauer. 2026. “Use and Perceived Helpfulness of Different Intervention Strategies in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Depression.” Journal of Clinical Medicine 15 (2): 849. https://doi.org/10.3390/jcm15020849.

Due Bruun, Karin, Robin Christensen, Kirstine Amris, Henrik Bjarke Vaegter, Morten Rune Blichfeldt-Eckhardt, L. Bye-Møller, Anders Holsgaard-Larsen, and Palle Toft. 2024. “Naltrexone 6 Mg Once Daily Versus Placebo in Women with Fibromyalgia: A Randomised, Double-Blind, Placebo-Controlled Trial.” The Lancet Rheumatology 6 (1): e31–39. https://doi.org/10.1016/S2665-9913(23)00278-3.

Duffy, Brynne M, James F Staples, and Shannon N Tessier. 2022. “Arousal from Torpor Increases Oxidative Damage in the Hibernating Thirteen-Lined Ground Squirrel (Ictidomys Tridecemlineatus).” Physiological and Biochemical Zoology 95 (3): 229–38. https://doi.org/10.1086/719931.

Durstenfeld, Matthew S., Michael J. Peluso, Feng Lin, et al. 2024. “Association of Nirmatrelvir for Acute SARS-CoV-2 Infection with Subsequent Long COVID Symptoms in an Observational Cohort Study.” Journal of Medical Virology 96: e29333. https://doi.org/10.1002/jmv.29333.

Earl, Katie E, Giorgos K Sakellariou, Mary Sinclair, Mark Fenech, Fiona Croden, Daniel J Owens, Jonathan Tang, et al. 2017. “Vitamin d Status in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis: A Cohort Study from the North-West of England.” BMJ Open 7 (11): e015296. https://doi.org/10.1136/bmjopen-2016-015296.

Eaton-Fitch, Natalie, and Sonya Marshall-Gradisnik. 2026. “Australian Registry Reports Poor Health and Wellbeing in People Living with ME/CFS.” Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08618-9.

Eaton-Fitch, Natalie, Penny Rudd, Tin Er, Livia Hool, Laura Herrero, and Sonya Marshall-Gradisnik. 2024. “Immune Exhaustion in ME/CFS and Long COVID.” JCI Insight 9 (22): e183810. https://doi.org/10.1172/jci.insight.183810.

Eckey, Macy, Peng Li, Brett Morrison, Jonas Bergquist, Ronald W. Davis, and Wenzhong Xiao. 2025. “Patient-Reported Treatment Outcomes in ME/CFS and Long COVID.” Proceedings of the National Academy of Sciences 122 (28): e2426874122. https://doi.org/10.1073/pnas.2426874122.

Efthimiou, Themis N., Samantha Lewis, Stephen J. Foster, Charlotte E. H. Wilks, Michael Dodd, Lucía Jiménez-Sánchez, Danielle Ropar, et al. 2025. “Diagnostic Status Influences Rapport and Communicative Behaviours in Dyadic Interactions Between Autistic and Non-Autistic People.” PLoS ONE 20 (8): e0330222. https://doi.org/10.1371/journal.pone.0330222.

Eggelbusch, Moritz, Braeden T. Charlton, Alessandra Bosutti, Bergita Ganse, Ifigeneia Giakoumaki, Anita E. Grootemaat, Paul W. Hendrickse, et al. 2024. “The Impact of Bed Rest on Human Skeletal Muscle Metabolism.” Cell Reports Medicine 5 (1): 101372. https://doi.org/10.1016/j.xcrm.2023.101372.

Eisenbarth, Stephanie C, Oscar R Colegio, William O’Connor, Fayyaz S Sutterwala, and Richard A Flavell. 2008. “Crucial Role for the Nalp3 Inflammasome in the Immunostimulatory Properties of Aluminium Adjuvants.” Nature 453 (7198): 1122–26. https://doi.org/10.1038/nature06939.

Elsaghir, A., and T. Witte. 2026. “New Autoantibodies in Sjögren’s Disease.” Current Opinion in Immunology 99: 102722. https://doi.org/10.1016/j.coi.2025.102722.

El-Tawil, Salwa, Tariq Al Musa, Hassan Valli, Michael P. Lunn, Tarek El-Tawil, and Markus Weber. 2010. “Quinine for Muscle Cramps.” Cochrane Database of Systematic Reviews, no. 12: CD005044. https://doi.org/10.1002/14651858.CD005044.pub2.

Eriksson, Peter S., Ekaterina Perfilieva, Thomas Björk-Eriksson, Ann-Marie Alborn, Claes Nordborg, Daniel A. Peterson, and Fred H. Gage. 1998. “Neurogenesis in the Adult Human Hippocampus.” Nature Medicine 4 (11): 1313–17. https://doi.org/10.1038/3305.

Esteban, David J., Brynn Conrad, Autumn Cullinan, Sharon Luong, Jason Albaum, and Victoria Wilk. 2026. “Tryptophan Metabolism and Aryl-Hydrocarbon Receptor Agonists in the Gut Microbiome of People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” MicrobiologyOpen 15 (3): e70333. https://doi.org/10.1002/mbo3.70333.

Evans, A. K., H. H. Park, C. E. Woods, R. K. Lam, et al. 2024. “Impact of Noradrenergic Inhibition on Neuroinflammation and Pathophysiology in Mouse Models of Alzheimer’s Disease.” Journal of Neuroinflammation 21 (1): 322. https://doi.org/10.1186/s12974-024-03306-1.

Everly, Michael J., Pamela C. Heaton, and Jr. Cluxton Robert J. 2004. “Beta-Blocker Underuse in Secondary Prevention of Myocardial Infarction.” Annals of Pharmacotherapy 38 (2): 286–93. https://doi.org/10.1345/aph.1C472.

Faber, Chelsea L., Jennifer D. Deem, Bao Anh Phan, Tammy P. Doan, Kayoko Ogimoto, Zaman Mirzadeh, Michael W. Schwartz, and Gregory J. Morton. 2021. “Leptin Receptor Neurons in the Dorsomedial Hypothalamus Regulate Diurnal Patterns of Feeding, Locomotion, and Metabolism.” eLife 10: e63671. https://doi.org/10.7554/eLife.63671.

Fallon, B. A., J. G. Keilp, K. M. Corbera, E. Petkova, C. B. Britton, E. Dwyer, I. Slavov, et al. 2008. “A Randomized, Placebo-Controlled Trial of Repeated IV Antibiotic Therapy for Lyme Encephalopathy.” Neurology 70 (13): 992–1003. https://doi.org/10.1212/01.WNL.0000284604.61160.2d.

Fatt, Scott J., Jessica E. Beilharz, Michelle Joubert, Chloe Wilson, Andrew R. Lloyd, Ute Vollmer-Conna, and Erin Cvejic. 2020. “Parasympathetic Activity Is Reduced During Slow-Wave Sleep, but Not Resting Wakefulness, in Patients with Chronic Fatigue Syndrome.” Journal of Clinical Sleep Medicine 16 (1): 19–28. https://doi.org/10.5664/jcsm.8114.

Fedorowski, Artur et al. 2017. “Antiadrenergic Autoimmunity in Postural Tachycardia Syndrome.” Europace 19 (7): 1211–19. https://doi.org/10.1093/europace/euw154.

Feiring, Berit, Ida Laake, Inger Johanne Bakken, Margrethe Greve-Isdahl, Vegard Bruun Wyller, Siri E. Haberg, Per Magnus, and Lill Trogstad. 2017. “HPV Vaccination and Risk of Chronic Fatigue Syndrome/Myalgic Encephalomyelitis: A Nationwide Register-Based Study from Norway.” Vaccine 35 (33): 4203–12. https://doi.org/10.1016/j.vaccine.2017.06.031.

Fenckova, Michaela. 2024. “When the ’Selfish’ Brain and the ’Selfish’ Immune System Clash.” Nature Reviews Neuroscience 26: 140. https://doi.org/10.1038/s41583-024-00900-3.

Feng, Jiesi, Huiling Dong, Julieta E. Lischinsky, Jing Zhou, Fei Deng, Chunyang Zhuang, Xiaoyu Miao, et al. 2024. “Monitoring Norepinephrine Release in Vivo Using Next-Generation GRAB(NE) Sensors.” Neuron 112 (12): 1930–1942.e6. https://doi.org/10.1016/j.neuron.2024.03.001.

Feng, J., T. Li, R. Yee, Y. Yuan, C. Bai, M. Cai, W. Shi, et al. 2019. “Stationary Phase Persister/Biofilm Microcolony of Borrelia Burgdorferi Causes More Severe Disease in a Mouse Model of Lyme Arthritis.” Discovery Medicine 27 (148): 125–38.

Feng, Yuqing, and Yun Lu. 2021. “Immunomodulatory Effects of Dopamine in Inflammatory Diseases.” Frontiers in Immunology 12: 663102. https://doi.org/10.3389/fimmu.2021.663102.

Fernández-Castañeda, Anthony, Peiwen Lu, Anna C. Geraghty, Eric Song, Myoung-Hwa Lee, Jamie Wood, Michael R. O’Dea, et al. 2022. “Mild Respiratory COVID Can Cause Multi-Lineage Neural Cell and Myelin Dysregulation.” Cell 185 (14): 2452–68. https://doi.org/10.1016/j.cell.2022.06.008.

Fernández-Garibay, Xavi, Manuel Gómez-Florit, Rui M. A. Domingues, Manuela E. Gomes, Juan M. Fernández-Costa, and Javier Ramón-Azcón. 2022. “Xeno-Free Bioengineered Human Skeletal Muscle Tissue Using Human Platelet Lysate-Based Hydrogels.” Biofabrication 14 (4). https://doi.org/10.1088/1758-5090/ac8dc8.

Fesharaki-Zadeh, Arman, Timothy Belliveau, Robert H. Pietrzak, and Amy Arnsten. 2025. “A Novel Multimodal Pharmacologic Approach Using Guanfacine, N-Acetylcysteine, and Donepezil in Severe TBI: A Case Series.” Frontiers in Rehabilitation Sciences 6: 1648002. https://doi.org/10.3389/fresc.2025.1648002.

Fesharaki-Zadeh, Arman, Naomi Lowe, and Amy F. T. Arnsten. 2023. “Clinical Experience with the $\alpha$2A-Adrenoceptor Agonist, Guanfacine, and N-Acetylcysteine for the Treatment of Cognitive Deficits in Long-COVID19.” Neuroimmunology Reports 3: 100154. https://doi.org/10.1016/j.nerep.2022.100154.

Finlay, David B., Stephen B. Duffull, and Michelle Glass. 2020. “100 Years of Modelling Ligand–Receptor Binding and Response: A Focus on GPCRs.” British Journal of Pharmacology 177 (7): 1501–20. https://doi.org/10.1111/bph.14907.

Fitzgerald, P, and T G Dinan. 2008. “Prolactin and Dopamine: What Is the Connection? A Review Article.” Journal of Psychopharmacology 22 (2 Suppl): 12–19. https://doi.org/10.1177/0269216307087148.

Flávio-Reis, Victor Hugo Prado, Yasmine Maria Pessoa-Gonçalves, Cláudia A. V. Diaz, Ana Sofia A. Lamoglia, Cinthia S. Desidério, and Carlo José Freire Oliveira. 2025. “Open Label Placebo for Chronic Low Back Pain: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.” Pain Management 15 (3): 149–60. https://doi.org/10.1080/17581869.2025.2467025.

Fleury, Samantha T., Xuanyi Lin, Peter D. Rios, Christopher Olker, Eun Joo Song, Alejandra Cobos Perez, Cody Fell, et al. 2026. “Encapsulated Leptin-Producing Cells Facilitate Entrainment of Circadian Rhythms in Rodents and Nonhuman Primates.” Advanced Science, e77191. https://doi.org/10.1002/advs.77191.

Fluegge, Keith, and Kyle Fluegge. 2018. “Antecedent ADHD, Dementia, and Metabolic Dysregulation: A U.S. Based Cohort Analysis.” Neurochemistry International 112: 255–58. https://doi.org/10.1016/j.neuint.2017.08.005.

Fluge, Ø., I. G. Rekeland, K. Lien, H. Thürmer, P. C. Borchgrevink, C. Schäfer, K. Sørland, et al. 2019. “B-Lymphocyte Depletion in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Randomized, Double-Blind, Placebo-Controlled Trial.” Annals of Internal Medicine 170 (9): 585–93. https://doi.org/10.7326/M18-1451.

Fluge, Ø., K. Risa, S. Lunde, K. Alme, I. G. Rekeland, D. Sapkota, E. K. Kristoffersen, et al. 2015. “B-Lymphocyte Depletion in Myalgic Encephalopathy/Chronic Fatigue Syndrome. An Open-Label Phase II Study with Rituximab Maintenance Treatment.” PLoS ONE 10 (7): e0129898. https://doi.org/10.1371/journal.pone.0129898.

Fluge, Øystein, Olav Mella, Ove Bruland, Kristin Risa, Sissel E. Dyrstad, Kine Alme, Ingrid G. Rekeland, et al. 2016. “Metabolic Profiling Indicates Impaired Pyruvate Dehydrogenase Function in Myalgic Encephalopathy/Chronic Fatigue Syndrome.” JCI Insight 1 (21): e89376. https://doi.org/10.1172/jci.insight.89376.

Forsythe, Sean M., and Gregory A. Schmidt. 2000. “Sodium Bicarbonate for the Treatment of Lactic Acidosis.” Chest 117 (1): 260–67. https://doi.org/10.1378/chest.117.1.260.

Freitag, Helma, Milena Szklarski, Sibylle Lorenz, Franziska Sotzny, Stephan Bauer, Anke Philippe, Claudia Kedor, et al. 2021. “Autoantibodies to Vasoregulative G-Protein-Coupled Receptors Correlate with Symptom Severity, Autonomic Dysfunction and Disability in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Clinical Medicine 10 (16): 3675. https://doi.org/10.3390/jcm10163675.

Froehlich, Laura, Daniel B R Hattesohl, Joseph Cotler, Leonard A Jason, Carmen Scheibenbogen, and Uta Behrends. 2022. “Causal Attributions and Perceived Stigma for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Health Psychology 27 (10): 2291–2304. https://doi.org/10.1177/13591053211027631.

Frost, Jeana, and Michael Massagli. 2009. “PatientsLikeMe the Case for a Data-Centered Patient Community and How ALS Patients Use the Community to Inform Treatment Decisions and Manage Pulmonary Health.” Chronic Respiratory Disease 6 (4): 225–29. https://doi.org/10.1177/1479972309348655.

Fu, Qi, Tiffany B VanGundy, Shigeki Shibata, Richard J Auchus, Gordon H Williams, and Benjamin D Levine. 2010. “Menstrual Cycle Affects Renal-Adrenal and Hemodynamic Responses During Prolonged Standing in the Postural Orthostatic Tachycardia Syndrome.” Hypertension 56 (1): 82–90. https://doi.org/10.1161/HYPERTENSIONAHA.110.151787.

Fullerton, Heather J., Nancy K. Hills, Max Wintermark, Nomazulu Dlamini, Catherine Amlie-Lefond, Michael M. Dowling, Lori C. Jordan, et al. 2025. “Evidence of Varicella Zoster Virus Reactivation in Children with Arterial Ischemic Stroke: Results of the VIPS II Study.” Journal of the American Heart Association 14 (6): e039184. https://doi.org/10.1161/JAHA.124.039184.

Gailliot, Matthew T, Roy F Baumeister, C Nathan DeWall, Jon K Maner, E Ashby Plant, Dianne M Tice, Lauren E Brewer, and Brandon J Schmeichel. 2007. “Self-Control Relies on Glucose as a Limited Energy Source: Willpower Is More Than a Metaphor.” Journal of Personality and Social Psychology 92 (2): 325–36. https://doi.org/10.1037/0022-3514.92.2.325.

Galbo, Henrik, and Lisbeth Kall. 2016. “Circadian Variations in Clinical Symptoms and Concentrations of Inflammatory Cytokines, Melatonin, and Cortisol in Polymyalgia Rheumatica Before and During Prednisolone Treatment: A Controlled, Observational, Clinical Experimental Study.” Arthritis Research & Therapy 18: 174. https://doi.org/10.1186/s13075-016-1072-4.

Gao, Jingwei, Cong Lin, Lehua Deng, Hongshuang Wang, and Xiaohui Wang. 2025. “Nanomolar TLR4 Antagonist CIAC101 Derived from (+)-Naltrexone Blocks Microglial Activation and Methamphetamine Addiction.” Journal of Medicinal Chemistry 68 (23): 25469–84. https://doi.org/10.1021/acs.jmedchem.5c02596.

Gao, J., B. Xi, K. Chen, et al. 2019. “The Stress Hormone Norepinephrine Increases the Growth and Virulence of Aeromonas Hydrophila.” MicrobiologyOpen 8 (4): e00664. https://doi.org/10.1002/mbo3.664.

García-Abellán, J. et al. 2021. “Antibody Response to SARS-CoV-2 Is Associated with Long-Term Clinical Outcome in Patients with COVID-19: A Longitudinal Study.” Journal of Clinical Immunology 41 (7): 1490–1501. https://doi.org/10.1007/s10875-021-01077-7.

Garrison, Scott R., Christina S. Korownyk, Michael R. Kolber, G. Michael Allan, Vijaya M. Musini, Ravneet K. Sekhon, and Nicolas Dugré. 2020. “Magnesium for Skeletal Muscle Cramps.” The Cochrane Database of Systematic Reviews 9: CD009402. https://doi.org/10.1002/14651858.CD009402.pub3.

Geerts, M, B T A de Greef, M Sopacua, S M J van Kuijk, J G J Hoeijmakers, C G Faber, and I S J Merkies. 2021. “Intravenous Immunoglobulin Therapy in Patients with Painful Idiopathic Small Fiber Neuropathy.” Neurology 96 (20): e2534–45. https://doi.org/10.1212/WNL.0000000000011919.

Germain, Arnaud, Katherine A. Glass, Mary A. Eckert, Ludovic Giloteaux, and Maureen R. Hanson. 2025. “Temporal Dynamics of the Plasma Proteomic Landscape Reveals Maladaptation in ME/CFS Following Exertion.” Molecular & Cellular Proteomics 24 (12): 101467. https://doi.org/10.1016/j.mcpro.2025.101467.

Gerolymos, Cyril, Enzo Saddier, Laurent Boyer, and Guillaume Fond. 2026. “Cognitive and Affective Effects of l-Theanine: A Systematic Review and Meta-Analysis of 31 Randomized Trials.” Molecular Psychiatry. https://doi.org/10.1038/s41380-026-03727-9.

Gherardi, Romain K, Géraldine Crépeaux, and François-Jérôme Authier. 2019. “Myalgia and Chronic Fatigue Syndrome Following Immunization: Macrophages Myofasciitis and Animal Studies Support Linkage to Aluminum Adjuvant Persistency and Diffusion in the Immune System.” Autoimmunity Reviews 18 (7): 691–705. https://doi.org/10.1016/j.autrev.2019.05.006.

Gherardi, Romain K, Hélène Eidi, Géraldine Crépeaux, François-Jérôme Authier, and Josette Cadusseau. 2015. “Biopersistence and Brain Translocation of Aluminum Adjuvants of Vaccines.” Frontiers in Neurology 6: 4. https://doi.org/10.3389/fneur.2015.00004.

Gibbons, C H, S Rajan, K Senechal, E Hendry, B McCallister, and T D Levine. 2023. “A Double-Blind Placebo-Controlled Pilot Study of Immunoglobulin for Small Fiber Neuropathy Associated with TS-HDS and FGFR-3 Autoantibodies.” Muscle & Nerve 67 (5): 363–70. https://doi.org/10.1002/mus.27745.

Gibbs, Bernard F., and Francesca Levi-Schaffer. 2012. “H4 Receptors in Mast Cells and Basophils: A New Therapeutic Target for Allergy?” Frontiers in Bioscience (Landmark Edition) 17 (2): 430–37. https://doi.org/10.2741/3936.

Gimbach, Silke, Daniel Vogel, Rainer Fried, Stephen V. Faraone, Tobias Banaschewski, Jan Buitelaar, Manfred Döpfner, and Ruth Ammer. 2024. “ADHD Medicine Consumption in Europe After COVID-19: Catch-up or Trend Change?” BMC Psychiatry 24 (1): 112. https://doi.org/10.1186/s12888-024-05505-9.

Giménez-Orenga, Karen, Eva Martín-Martínez, Lubov Nathanson, and Elisa Oltra. 2025. “HERV Activation Segregates ME/CFS from Fibromyalgia While Defining a Novel Nosologic Entity.” eLife 14: RP104441. https://doi.org/10.7554/eLife.104441.

Godlewska, Beata R., Amy L. Sylvester, Uzay E. Emir, Ann L. Sharpley, William T. Clarke, Stephen R. Williams, Ana Jorge Gonçalves, and Philip J. Cowen. 2025. “Brain and Muscle Chemistry in Myalgic Encephalitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID: A 7T Magnetic Resonance Spectroscopy Study.” Molecular Psychiatry 30: 5215–26. https://doi.org/10.1038/s41380-025-03108-8.

Goebel, A., K. Busch, R. Riedl, and C. Sommer. 2021. “Passive Transfer of Fibromyalgia IgG Induces Pain and Fatigue in Mice.” Pain 162 (4): 893–904. https://doi.org/10.1097/j.pain.0000000000002134.

Goldenberg, Don L. 2025. “The Pivotal Role of Central Sensitization in Long COVID, Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Expert Review of Neurotherapeutics 25 (8): 973–89. https://doi.org/10.1080/14737175.2025.2516097.

Golimstok, Angel, María José García Basalo, M. Majul, W. Berrios, M. Rugiero, M. C. Fernández, and R. Eichel. 2024. “Adult Attention Deficit-Hyperactivity Disorder Is Associated with Lewy Body Disease and Cognitive Impairment: A Prospective Cohort Study with 15-Year Follow-up.” American Journal of Geriatric Psychiatry 32 (9): 1063–77. https://doi.org/10.1016/j.jagp.2024.04.005.

Granerod, Julia, Nicholas W. S. Davies, Parashar P. Ramanuj, Ava Easton, David W. Brown, and Sara L. Thomas. 2017. “Increased Rates of Sequelae Post-Encephalitis in Individuals Attending Primary Care Practices in the United Kingdom: A Population-Based Retrospective Cohort Study.” Journal of Neurology 264 (2): 407–15. https://doi.org/10.1007/s00415-016-8316-8.

Grant, Sarah, Sam Norton, and Rosa A. Hoekstra. 2026. “A Cross-Neurotype Analysis of Camouflaging and Illness Perceptions in People with Central Sensitivity Syndromes.” Autism in Adulthood 8 (4): 646–63. https://doi.org/10.1089/aut.2024.0186.

Grover, Madhusudan, Gianrico Farrugia, Matthew S. Lurken, Cheryl E. Bernard, Maria-Simonetta Faussone-Pellegrini, et al. 2011. “Cellular Changes in Diabetic and Idiopathic Gastroparesis.” Gastroenterology 140 (5): 1575–1585.e8. https://doi.org/10.1053/j.gastro.2011.01.046.

Guarnotta, Valentina, Roberta Amodei, and Carla Giordano. 2021. “Metabolic Comorbidities of Adrenal Insufficiency: Focus on Steroid Replacement Therapy and Chronopharmacology.” Current Opinion in Pharmacology 60: 123–32. https://doi.org/10.1016/j.coph.2021.07.003.

Guise, Jennifer, Chris McVittie, and Andy McKinlay. 2010. “A Discourse Analytic Study of ME/CFS (Chronic Fatigue Syndrome) Sufferers’ Experiences of Interactions with Doctors.” Journal of Health Psychology 15 (3): 426–35. https://doi.org/10.1177/1359105309350515.

Gürbüzer, Nazan, Ahmet Ozkaya, and Filiz Mercantepe. 2026. “The SIRT-1/Nrf-2/HO-1 Antioxidant Defense Axis in Adult Attention-Deficit/Hyperactivity Disorder.” Metabolic Brain Disease 41 (1). https://doi.org/10.1007/s11011-026-01845-5.

Habermann-Horstmeier, Lotte, and Lukas M. Horstmeier. 2025. “Symptom Clusters in ME/CFS Reflect Distinct Neuroimmune and Autonomic Pathophysiological Mechanisms: A Translational Model.” Journal of Translational Medicine 24 (1): 606. https://doi.org/10.1186/s12967-026-08159-1.

Hadanny, Amir, Shani Zilberman-Itskovich, Marat Catalogna, Keren Elman-Shina, Elad Lang, Shachar Finci, Natalie Polak, Rachel Shorer, Yonatan Parag, and Shai Efrati. 2024. “Long Term Outcomes of Hyperbaric Oxygen Therapy in Post Covid Condition: Longitudinal Follow-up of a Randomized Controlled Trial.” Scientific Reports 14 (1): 3604. https://doi.org/10.1038/s41598-024-53091-3.

Hadidchi, Ramin, Bonny Patel, Jay Madan, Andrew Liu, Sean Henry, and Tim Q. Duong. 2025. “Elevated Risk of New-Onset Chronic Fatigue Syndrome/Myalgic Encephalomyelitis up to Four Years After SARS-CoV-2 Infection.” Journal of Translational Medicine 23 (1): 815. https://doi.org/10.1186/s12967-025-06625-w.

Haeusler, Isabella L., Xin Hui S. Chan, Philippe J. Guérin, and Nicholas J. White. 2018. “The Arrhythmogenic Cardiotoxicity of the Quinoline and Structurally Related Antimalarial Drugs: A Systematic Review.” BMC Medicine 16 (1): 200. https://doi.org/10.1186/s12916-018-1188-2.

Haghighi, S, J Forsmark, O Zachrisson, A Carlsson, M Nilsson, M L Carlsson, R C Schuit, and C G Gottfries. 2021. “Open-Label Study with the Monoamine Stabilizer (-)-OSU6162 in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Brain and Behavior 11 (4): e02040. https://doi.org/10.1002/brb3.2040.

Hahn, Maureen K., David Robertson, and Randy D. Blakely. 2003. “A Mutation in the Human Norepinephrine Transporter Gene (SLC6A2) Associated with Orthostatic Intolerance Disrupts Surface Expression of Mutant and Wild-Type Transporters.” The Journal of Neuroscience 23 (11): 4470–78. https://doi.org/10.1523/JNEUROSCI.23-11-04470.2003.

Hajhashemy, Zahra, Mohammad Bagherniya, and Omid Sadeghi. 2026. “The Effect of Saffron Supplementation on Indices of Oxidative Stress, Inflammation, Mental Health, and Quality of Life in Patients with Parkinson’s Disease.” Food & Function 17 (2): 889–901. https://doi.org/10.1039/d5fo01924a.

Halestrap, Andrew P., and Marieangela C. Wilson. 2012. “The Monocarboxylate Transporter Family–Role and Regulation.” IUBMB Life 64 (2): 109–19. https://doi.org/10.1002/iub.572.

Hall, J., K. M. Bourne, S. Vernino, V. Hamrefors, I. Kharraziha, J. Nilsson, R. S. Sheldon, A. Fedorowski, and S. R. Raj. 2022. “Detection of G Protein-Coupled Receptor Autoantibodies in Postural Orthostatic Tachycardia Syndrome Using Standard Methodology.” Circulation 146 (8): 613–22. https://doi.org/10.1161/CIRCULATIONAHA.122.059971.

Hanc, Evelyne, Katharina Koller, Regina Herold, Yesim Erim, and Eva Morawa. 2026. “Cognitive Impairment, Depression, and Fatigue in Post-COVID and Post-Vaccination Syndrome: A Large-Scale Cross-Sectional Study.” European Archives of Psychiatry and Clinical Neuroscience 276 (1): 221–34. https://doi.org/10.1007/s00406-025-02144-3.

Hanson, Aimee L., Matthew P. Mulè, Hélène Ruffieux, Federica Mescia, Laura Bergamaschi, Victoria S. Pelly, Lorinda Turner, et al. 2024. “Iron Dysregulation and Inflammatory Stress Erythropoiesis Associates with Long-Term Outcome of COVID-19.” Nature Immunology 25 (3): 471–82. https://doi.org/10.1038/s41590-024-01754-8.

Harper, Lorraine, Catherine A Hewitt, Ian Litchfield, Matthew D Morgan, Dimitrios Chanouzas, Hannah K Caulfield, Laura Coughlan, et al. 2021. “Management of Fatigue with Physical Activity and Behavioural Change Support in Vasculitis: A Feasibility Study.” Rheumatology (Oxford) 60 (9): 4130–40. https://doi.org/10.1093/rheumatology/keaa890.

Hart, David A. 2021. “Learning from Human Responses to Deconditioning Environments: Improved Understanding of the "Use It or Lose It" Principle.” Frontiers in Sports and Active Living 3: 685845. https://doi.org/10.3389/fspor.2021.685845.

Hartwig, J., F. Sotzny, S. Bauer, H. Heidecke, G. Riemekasten, D. Dragun, C. Meisel, C. Dames, P. Grabowski, and C. Scheibenbogen. 2020. “IgG Stimulated $\beta$2 Adrenergic Receptor Activation Is Attenuated in Patients with ME/CFS.” Brain, Behavior, & Immunity - Health 3: 100047. https://doi.org/10.1016/j.bbih.2020.100047.

Harvey, Samuel B, Simon Wessely, Diana Kuh, and Matthew Hotopf. 2009. “The Relationship Between Fatigue and Psychiatric Disorders: Evidence for the Concept of Neurasthenia.” Journal of Psychosomatic Research 66 (5): 445–54. https://doi.org/10.1016/j.jpsychores.2008.12.007.

Hasan, Bashar, Jehad Almasri, Bilal Marwa, Kelsey M. Klaas, and Philip R. Fischer. 2020. “Treatment of Postural Orthostatic Tachycardia Syndrome with Medication: A Systematic Review.” Journal of Child Neurology 35 (14): 1004–16. https://doi.org/10.1177/0883073820948679.

Hauglund, Natalie L., Mie Andersen, Klaudia Tokarska, Tessa Radovanovic, Celia Kjaerby, Frederikke L. Sørensen, Zuzanna Bojarowska, et al. 2025. “Norepinephrine-Mediated Slow Vasomotion Drives Glymphatic Clearance During Sleep.” Cell 188 (3): 606–622.e17. https://doi.org/10.1016/j.cell.2024.11.027.

Haunhorst, Simon, Diana Dudziak, Carmen Scheibenbogen, Martina Seifert, Franziska Sotzny, Carsten Finke, Christian Puta, et al. 2025. “Towards an Understanding of Physical Activity-Induced Post-Exertional Malaise: Insights into Microvascular Alterations and Immunometabolic Interactions in Post-COVID Condition and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Infection 53 (1): 1–13. https://doi.org/10.1007/s15010-024-02386-8.

Hawke, Fiona, Sean G. Sadler, Hans D. Katzberg, Fereshteh Pourkazemi, Vivienne Chuter, and Joshua Burns. 2021. “Non-Drug Therapies for the Secondary Prevention of Lower Limb Muscle Cramps.” Cochrane Database of Systematic Reviews, no. 5: CD008496. https://doi.org/10.1002/14651858.CD008496.pub3.

Hayashida, Ken-ichiro, and Hideaki Obata. 2019. “Strategies to Treat Chronic Pain and Strengthen Impaired Descending Noradrenergic Inhibitory System.” International Journal of Molecular Sciences 20 (4): 822. https://doi.org/10.3390/ijms20040822.

Haywood, K. L., S. M. Collin, and E. Crawley. 2014. “Assessing Severity of Illness and Outcomes of Treatment in Children with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME): A Systematic Review of Patient-Reported Outcome Measures (PROMs).” Child: Care, Health and Development 40 (6): 806–24. https://doi.org/10.1111/cch.12135.

Hazrati, Ebrahim, Seyed Parsa Eftekhar, Reza Mosaed, et al. 2024. “Understanding the Kynurenine Pathway: A Narrative Review on Its Impact Across Chronic Pain Conditions.” Molecular Pain 20: 17448069241297714. https://doi.org/10.1177/17448069241297714.

He, Jiabao, Kieren G. Hollingsworth, Julia L. Newton, and Andrew M. Blamire. 2013. “Cerebral Vascular Control Is Associated with Skeletal Muscle pH in Chronic Fatigue Syndrome Patients Both at Rest and During Dynamic Stimulation.” NeuroImage: Clinical 2: 168–73. https://doi.org/10.1016/j.nicl.2012.12.006.

Hemmesch, Amanda R. 2014. “The Detrimental Effects of Atypical Nonverbal Behavior on Older Adults’ First Impressions of Individuals with Parkinson’s Disease.” Psychology and Aging 29 (3): 521–27. https://doi.org/10.1037/a0036637.

Hendrickson, Rebecca C., Ronald G. Thomas, Nicholas J. Schork, and Murray A. Raskind. 2020. “Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings.” Frontiers in Digital Health 2: 13. https://doi.org/10.3389/fdgth.2020.00013.

Hendrix, Jolien, Lara Fanning, Arne Wyns, Ishtiaq Ahmed, Madhura Shekhar Patil, Emma Richter, Jente Van Campenhout, et al. 2025. “Adrenergic Dysfunction in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Fibromyalgia: A Systematic Review and Meta-Analysis.” European Journal of Clinical Investigation 55 (1): e14318. https://doi.org/10.1111/eci.14318.

Heneka, Michael T., Wiesje M. van der Flier, Frank Jessen, Jeroen Hoozemans, Dietmar R. Thal, Delphine Boche, Frederic Brosseron, Charlotte Teunissen, Henrik Zetterberg, et al. 2025. “Neuroinflammation in Alzheimer Disease.” Nature Reviews Immunology 25 (5): 321–52. https://doi.org/10.1038/s41577-024-01104-7.

Heneka, Michael T., Markus P. Kummer, Andrea Stutz, Andrea Delekate, Stephanie Schwartz, Ana Vieira-Saecker, Angelika Griep, et al. 2013. “NLRP3 Is Activated in Alzheimer’s Disease and Contributes to Pathology in APP/PS1 Mice.” Nature 493 (7434): 674–78. https://doi.org/10.1038/nature11729.

Heng, Ruiwen Benjamin, Bavani Gunasegaran, Shivani Krishnamurthy, Sonia Bustamante, Ananda Staats, Sharron Chow, Seong Beom Ahn, et al. 2025. “Mapping the Complexity of ME/CFS: Evidence for Abnormal Energy Metabolism, Altered Immune Profile, and Vascular Dysfunction.” Cell Reports Medicine 6 (12): 102514. https://doi.org/10.1016/j.xcrm.2025.102514.

Hermida, Ramón C, Michael H Smolensky, Howard Balan, Richard J Castriotta, Juan J Crespo, Yaron Dagan, Sherine El-Toukhy, et al. 2021. “Guidelines for the Design and Conduct of Human Clinical Trials on Ingestion-Time Differences — Chronopharmacology and Chronotherapy — of Hypertension Medications.” Chronobiology International 38 (1): 1–26. https://doi.org/10.1080/07420528.2020.1850468.

Hertz, Leif, Junnan Xu, Dan Song, Ting Du, et al. 2015. “Astrocytic Glycogenolysis: Mechanisms and Functions.” Metabolic Brain Disease 30 (1): 317–33. https://doi.org/10.1007/s11011-014-9536-1.

Hickie, Ian, Tracey Davenport, Denis Wakefield, Ute Vollmer-Conna, Barbara Cameron, Suzanne D Vernon, William C Reeves, and Andrew Lloyd. 2006a. “Post-Infective and Chronic Fatigue Syndromes Precipitated by Viral and Non-Viral Pathogens: Prospective Cohort Study.” BMJ 333 (7568): 575. https://doi.org/10.1136/bmj.38933.585764.AE.

———. 2006b. “Post-Infective and Chronic Fatigue Syndromes Precipitated by Viral and Non-Viral Pathogens: Prospective Cohort Study.” BMJ 333 (7568): 575. https://doi.org/10.1136/bmj.38933.585764.AE.

Hill, Jessica, Glyn Howatson, Ken van Someren, David Gaze, Hayley Legg, Jack Lineham, and Charles Pedlar. 2017. “The Effects of Compression-Garment Pressure on Recovery After Strenuous Exercise.” International Journal of Sports Physiology and Performance 12 (8): 1078–84. https://doi.org/10.1123/ijspp.2016-0380.

Hilz, Max J. 2022. “Transcutaneous Vagus Nerve Stimulation - a Brief Introduction and Overview.” Autonomic Neuroscience 243: 103038. https://doi.org/10.1016/j.autneu.2022.103038.

Hodge, Ellie Kiger, and Katherine Kuhl Meltzoff. 2026. “The Relationship Between Autistic Camouflaging and Mental Health: A Scoping Review.” Frontiers in Psychiatry 17 (June): 1701615. https://doi.org/10.3389/fpsyt.2026.1701615.

Hoel, August, Fredrik Hoel, Sissel Elisabeth Dyrstad, Henrique Chapola, Ingrid Gurvin Rekeland, Kristin Risa, Kine Alme, et al. 2026. “Charting the Circulating Proteome in ME/CFS Using Cross-System Profiling to Uncover Mechanistic Insights.” Cell Reports Medicine 7 (March): 102647. https://doi.org/10.1016/j.xcrm.2026.102647.

Hoque, Rafaz, Ahmad Farooq, Ayaz Ghani, Fred Gorelick, and Wajahat Zafar Mehal. 2014. “Lactate Reduces Liver and Pancreatic Injury in Toll-like Receptor- and Inflammasome-Mediated Inflammation via GPR81-Mediated Suppression of Innate Immunity.” Gastroenterology 146 (7): 1763–74. https://doi.org/10.1053/j.gastro.2014.03.014.

Horowitz, R. I., and P. R. Freeman. 2020. “Efficacy of Double-Dose Dapsone Combination Therapy in the Treatment of Chronic Lyme Disease/Post-Treatment Lyme Disease Syndrome (PTLDS) and Associated Co-Infections: A Report of Three Cases and Retrospective Chart Review.” Antibiotics 9 (11): 725. https://doi.org/10.3390/antibiotics9110725.

Horvath, Audrey E, Miriam Grozis, Peter R S Baker, et al. 2026. “Saffron Alters Microbial Amino Acid Metabolism and Neurotransmitter Production in a Defined Gut Consortium.” Food Science & Nutrition 14 (4): e71694. https://doi.org/10.1002/fsn3.71694.

Hotfiel, Thilo, Jürgen Freiwald, Matthias Wilhelm Hoppe, Christoph Lutter, Raimund Forst, Casper Grim, Wilhelm Bloch, Moritz Hüttel, and Rafael Heiss. 2018. “Advances in Delayed-Onset Muscle Soreness (DOMS): Part I: Pathogenesis and Diagnostics.” Sportverletzung Sportschaden 32 (4): 243–50. https://doi.org/10.1055/a-0753-1884.

Hrvatin, S, S Sun, O F Wilcox, H Yao, A J Lavin-Peter, M Cicconet, E G Assad, et al. 2020. “Neurons That Regulate Mouse Torpor.” Nature 583 (7814): 115–21. https://doi.org/10.1038/s41586-020-2387-5.

Hu Frisk, Jun Mei, Lena Kjellen, Stephen G. Kaler, Gunnar Pejler, and Helena Ohrvik. 2017. “Copper Regulates Maturation and Expression of an MITF:tryptase Axis in Mast Cells.” Journal of Immunology 199 (12): 4132–41. https://doi.org/10.4049/jimmunol.1700786.

Hung, S. C., C. C. Chen, H. C. Chan, M. L. Chan, Y. S. Ngui, J. B. Mei, Y. W. Huang, V. H. Chaithra, et al. 2025. “Structural and Molecular Dynamics Insights into the Competitive Inhibition of the Platelet-Activating Factor Receptor by Acyl-PAF.” Journal of Biological Chemistry 301 (12): 110831. https://doi.org/10.1016/j.jbc.2025.110831.

Hunstiger, Moriah, Maren M Johannsen, and Shawn R Oliver. 2023. “Non-Shivering Thermogenesis Is Differentially Regulated During the Hibernation Season in Arctic Ground Squirrels.” Frontiers in Physiology 14: 1207529. https://doi.org/10.3389/fphys.2023.1207529.

Hunter, Ewan, Heba Alshaker, Oliver Bundock, Cicely Weston, Shekinah Bautista, Abel Gebregzabhar, Anya Virdi, et al. 2025. “Development and Validation of Blood-Based Diagnostic Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Using EpiSwitch® 3-Dimensional Genomic Regulatory Immuno-Genetic Profiling.” Journal of Translational Medicine 23 (1): 1010. https://doi.org/10.1186/s12967-025-07203-w.

Hunter, Ewan, Heba Alshaker, Dominik Vugrinec, Shekinah Bautista, Abel Gebregzabhar, Anya Virdi, Joseph Croxford, et al. 2026. “Beyond Genes: EpiSwitch® and Orion Platform-Powered 3D Genome Architecture Biomarkers Reveal Shared Biology Across ME/CFS, Long COVID, PTSD, Rheumatoid Arthritis, and Multiple Sclerosis.” Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08874-9.

Hussein, Sabreena, Lindsay Eiriksson, Michelle MacQuarrie, Shanon Merriam, Marissa Dalton, Luis Nacul, and Eliana Lacerda. 2024. “Healthcare System Barriers Impacting the Care of Canadians with Myalgic Encephalomyelitis: A Scoping Review.” Journal of Evaluation in Clinical Practice 30 (7): 1337–60. https://doi.org/10.1111/jep.14047.

Hviid, Anders, Nicklas M. Thorsen, Palle Valentiner-Branth, Morten Frisch, and Kare Molbak. 2020. “Association Between Quadrivalent Human Papillomavirus Vaccination and Selected Syndromes with Autonomic Dysfunction in Danish Females: Population Based, Self-Controlled, Case Series Analysis.” BMJ 370: m2930. https://doi.org/10.1136/bmj.m2930.

Ilieva, Irena P., Cayce J. Hook, and Martha J. Farah. 2015. “Prescription Stimulants’ Effects on Healthy Inhibitory Control, Working Memory, and Episodic Memory: A Meta-Analysis.” Journal of Cognitive Neuroscience 27 (6): 1069–89. https://doi.org/10.1162/jocn_a_00776.

Imhann, Floris, Marc Jan Bonder, Arnau Vich Vila, and Jingyuan Fu. 2016. “Proton Pump Inhibitors Affect the Gut Microbiome.” Gut 65 (5): 740–48. https://doi.org/10.1136/gutjnl-2015-310376.

Ingman, Tom, Abigail Smakowski, Kimberley Goldsmith, and Trudie Chalder. 2022. “A Systematic Literature Review of Randomized Controlled Trials Evaluating Prognosis Following Treatment for Adults with Chronic Fatigue Syndrome.” Psychological Medicine 52 (14): 2917–29. https://doi.org/10.1017/S0033291722002471.

Institute of Medicine. 2015. “Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness.” The National Academies Press. https://doi.org/10.17226/19012.

Ising, Christina, Carmen Venegas, Shuangshuang Zhang, Hannah Scheiblich, Susanne V. Schmidt, Ana Vieira-Saecker, Stephanie Schwartz, et al. 2019. “NLRP3 Inflammasome Activation Drives Tau Pathology.” Nature 575 (7784): 669–73. https://doi.org/10.1038/s41586-019-1769-z.

Iu, David S., Jessica Maya, Luyen T. Vu, Elizabeth A. Fogarty, Adrian J. McNairn, Faraz Ahmed, Carl J. Franconi, et al. 2024. “Transcriptional Reprogramming Primes CD8+ t Cells Toward Exhaustion in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Proceedings of the National Academy of Sciences 121 (50): e2415119121. https://doi.org/10.1073/pnas.2415119121.

Jahanshahi, Arash, Sina Mohammadi, Mohammad Amin Salehi, Mahsa Dolatshahi, et al. 2026. “Brain Microstructural Alterations in COVID-19: A Systematic Review of Diffusion Weighted Imaging Studies.” Brain Imaging and Behavior 20 (2): 49. https://doi.org/10.1007/s11682-026-01084-3.

Jason, L. A., W. J. McGarrigle, and R. C. W. Vermeulen. 2024. “The Head-up Tilt Table Test as a Measure of Autonomic Functioning Among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Personalized Medicine 14 (3): 238. https://doi.org/10.3390/jpm14030238.

Jason, Leonard A, Arielle Boulton, Nicole S Porter, Trine Jessen, Mary Gloria Njoku, and Fred Friedberg. 2010. “Classification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome by Types of Fatigue.” Behavioral Medicine 36 (1): 24–31. https://doi.org/10.1080/08964280903521370.

Jason, Leonard A, Jasmine A Dorri, Benjamin H Natelson, Lucinda Bateman, and Suzanne D Vernon. 2025. “Incidence and Prevalence of Post-COVID-19 Myalgic Encephalomyelitis: A RECOVER-Adult Study.” Journal of General Internal Medicine. https://doi.org/10.1007/s11606-024-09290-9.

Jason, Leonard A., Meredyth Evans, Suzanna So, Jodie Scott, and Abigail Brown. 2015. “Problems in Defining Post-Exertional Malaise.” Journal of Prevention and Intervention in the Community 43 (1): 20–31. https://doi.org/10.1080/10852352.2014.973239.

Jason, Leonard A, Renee R Taylor, Sigita Plioplys, Zuzana Stepanek, and Jennifer Shlaes. 2002. “Evaluating Attributions for an Illness Based Upon the Name: Chronic Fatigue Syndrome, Myalgic Encephalopathy and Florence Nightingale Disease.” American Journal of Community Psychology 30 (1): 133–48. https://doi.org/10.1023/A:1014328319297.

Javonillo, Dominic I., S. Furman, L. Le, K. Fernandez, J. Mulford, V. Singla, R. Jha, et al. 2026. “Characterization of Effects of a Neurotropic Murine Coronavirus Infection on Alzheimer’s Disease Neuropathology of 5xFAD Mice.” bioRxiv. https://doi.org/10.64898/2026.02.23.707587.

Jensen, Christina Bisgaard, Kristoffer Torp Hansen, Bodil Hammer Bech, Stefan Nygaard Hansen, Henrik Nielsen, Charlotte Ulrikka Rask, Per Fink, Thomas Meinertz Dantoft, et al. 2025. “Post-Acute Non-Specific Symptoms Following COVID-19 Vaccination: A Danish Population-Based Study.” International Journal of Epidemiology 54 (5): dyaf173. https://doi.org/10.1093/ije/dyaf173.

Jensen, Federico, Mariana Woudwyk, Ana Teles, Katja Woidacki, Florin Taran, Sonia Costa, Sara F Malfertheiner, and Ana Claudia Zenclussen. 2010. “Estradiol and Progesterone Regulate the Migration of Mast Cells from the Periphery to the Uterus and Induce Their Maturation and Degranulation.” PLoS ONE 5 (12): e14409. https://doi.org/10.1371/journal.pone.0014409.

Jessen, Søren, Sissel A. Solheim, Glenn A. Jacobson, Kasper Eibye, et al. 2020. “Beta(2)-Adrenergic Agonist Clenbuterol Increases Energy Expenditure and Fat Oxidation, and Induces mTOR Phosphorylation in Skeletal Muscle of Young Healthy Men.” Drug Testing and Analysis 12 (5): 610–18. https://doi.org/10.1002/dta.2755.

Jin, B. H., T. Hong, B. W. Yoo, C. O. Kim, D. Kim, Y. N. Kim, and M. S. Park. 2024. “Pharmacokinetics, Pharmacodynamics, and Safety of Izuforant, an H4R Inhibitor, in Healthy Subjects: A Phase i Single and Multiple Ascending Dose Study.” Clinical and Translational Science 17 (10): e70032. https://doi.org/10.1111/cts.70032.

Jin, H, Y An, J Huang, T Luo, and X Wu. 2026. “Pathophysiological Mechanisms of Post-Exertional Malaise: An Integrative Analysis Based on the Metabolism-Immune-Neuro Interaction Model.” Frontiers in Immunology 17: 1774310. https://doi.org/10.3389/fimmu.2026.1774310.

Jobava, Raul, Yuanhui Mao, Bo-Jhih Guan, Dawei Hu, Dawid Krokowski, Chih-Wei Chen, Xing-En Shu, et al. 2021. “Adaptive Translational Pausing Is a Hallmark of the Cellular Response to Severe Environmental Stress.” Molecular Cell 81 (20): 4191–4208.e8. https://doi.org/10.1016/j.molcel.2021.09.029.

Johnston, K. J. A., R. Signer, and L. M. Huckins. 2025. “Chronic Overlapping Pain Conditions and Nociplastic Pain.” HGG Advances 6 (1): 100381. https://doi.org/10.1016/j.xhgg.2024.100381.

Jones, B. E. 2020. “Arousal and Sleep Circuits.” Neuropsychopharmacology 45 (1): 6–20. https://doi.org/10.1038/s41386-019-0444-2.

Joseph, Phillip, Claudia Arevalo, Rudolf K. F. Oliveira, Mariana Faria-Urbina, Aaron B. Waxman, and David M. Systrom. 2021. “Insights from Invasive Cardiopulmonary Exercise Testing of Patients with ME/CFS.” Chest 160 (2): 642–51. https://doi.org/10.1016/j.chest.2021.01.082.

Jovanovic, Hristina, Per Karlsson, Asa Cerin, Christer Halldin, and Anna-Lena Nordström. 2009. “5-HT(1A) Receptor and 5-HTT Binding During the Menstrual Cycle in Healthy Women Examined with [(11)C] WAY100635 and [(11)C] MADAM PET.” Psychiatry Research: Neuroimaging 172 (1): 31–37. https://doi.org/10.1016/j.pscychresns.2008.07.002.

Jutel, Marek, Takeshi Watanabe, Sven Klunker, Mubeccel Akdis, Oliver A. Thomet, Jozef Malolepszy, Teresa Zak-Nejmark, et al. 2001. “Histamine Regulates t-Cell and Antibody Responses by Differential Expression of H1 and H2 Receptors.” Nature 413 (6854): 420–25. https://doi.org/10.1038/35096564.

Kadiyska, Tsvetana, Ivan Tourtourikov, Darina Madzharova, Samanta Ciurinskiene, Marta Savcheva, Ekaterina Ivanova-Todorova, Ralitsa Mileva-Popova, Radka Tafradjiiska-Hadjiolova, and Vanyo Mitev. 2025. “Impact of AOC1 and HNMT Variants on the Therapeutic Outcomes of a Histamine Reducing Diet in Autism Spectrum Disorder.” Journal of Molecular Neuroscience 75 (3): 105. https://doi.org/10.1007/s12031-025-02399-4.

Kahathuduwa, Chanaka N., Sarah Wakefield, Blake D. West, Jessica Blume, Tharaka Lagath Dassanayake, Vajira S. Weerasinghe, and Ann Mastergeorge. 2020. “Effects of l-Theanine-Caffeine Combination on Sustained Attention and Inhibitory Control Among Children with ADHD: A Proof-of-Concept Neuroimaging RCT.” Scientific Reports 10 (1): 13072. https://doi.org/10.1038/s41598-020-70037-7.

Kalangara, J. P., K. Vanijcharoenkarn, S. Chisolm, and M. E. Kuruvilla. 2022. “Neuropathic Pain and Itch: Mechanisms in Allergic Conjunctivitis.” Current Opinion in Allergy and Clinical Immunology 22 (5): 298–303. https://doi.org/10.1097/ACI.0000000000000843.

Kang, O., and M. R. Banaji. 2020. “Pupillometric Decoding of High-Level Musical Imagery.” Consciousness and Cognition 77: 102862. https://doi.org/10.1016/j.concog.2019.102862.

Katzberg, H D, and C Barnett. 2023. “IVIg for TS-HDS and FGFR-3 Antibody-Positive Small-Fiber Neuropathy: A Fading Signal for Efficacy?” Muscle & Nerve 67 (5): 339–40. https://doi.org/10.1002/mus.27800.

Kavyani, Bahar, Seong Beom Ahn, Daniel Missailidis, Sarah J. Annesley, Paul R. Fisher, Richard Schloeffel, Gilles J. Guillemin, David B. Lovejoy, and Benjamin Heng. 2024. “Dysregulation of the Kynurenine Pathway, Cytokine Expression Pattern, and Proteomics Profile Link to Symptomology in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).” Molecular Neurobiology 61 (7): 3771–87. https://doi.org/10.1007/s12035-023-03784-z.

Kavyani, Behrouz, Seong Beom Ahn, Daniel Missailidis, Sarah J. Annesley, Paul R. Fisher, Gilles J. Guillemin, et al. 2024. “Dysregulation of the Kynurenine Pathway, Cytokine Expression Pattern, and Proteomics Profile Link to Symptomology in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).” Molecular Neurobiology 61 (7): 4728–48. https://doi.org/10.1007/s12035-023-03756-w.

Kavyani, Zahra, Ekua W Brenu, Donald R Staines, and Sonya M Marshall-Gradisnik. 2022. “Kynurenine Pathway Metabolites in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Systematic Review.” Journal of Translational Medicine 20 (1): 512. https://doi.org/10.1186/s12967-022-03712-3.

Kawka, Lou, Aurelien Schlencker, Philippe Mertz, Thierry Martin, and Laurent Arnaud. 2021. “Fatigue in Systemic Lupus Erythematosus: An Update on Its Impact, Determinants and Therapeutic Management.” Journal of Clinical Medicine 10 (17): 3996. https://doi.org/10.3390/jcm10173996.

Kay, Benjamin P., Muriah D. Wheelock, Joshua S. Siegel, Ryan V. Raut, Roselyne J. Chauvin, Athanasia Metoki, Aishwarya Rajesh, et al. 2025. “Stimulant Medications Affect Arousal and Reward, Not Attention Networks.” Cell 188 (26): 7529–7546.e20. https://doi.org/10.1016/j.cell.2025.11.039.

Kay, G. G. 2000. “The Effects of Antihistamines on Cognition and Performance.” Journal of Allergy and Clinical Immunology 105 (6 Pt 2): S622–27. https://doi.org/10.1067/mai.2000.106153.

Kealy, John, Carol Murray, Eoin W Griffin, Ana Belen Lopez-Rodriguez, Daire Healy, Lucas Silva Tortorelli, John P Lowry, Leiv Otto Watne, and Colm Cunningham. 2020. “Acute Inflammation Alters Brain Energy Metabolism in Mice and Humans: Role in Suppressed Spontaneous Activity, Impaired Cognition, and Delirium.” Journal of Neuroscience 40 (29): 5681–96. https://doi.org/10.1523/JNEUROSCI.2876-19.2020.

Kentrou, Vasiliki, Lucy A. Livingston, Rachel Grove, Rosa A. Hoekstra, and Sander Begeer. 2024. “Perceived Misdiagnosis of Psychiatric Conditions in Autistic Adults.” eClinicalMedicine 71 (May): 102586. https://doi.org/10.1016/j.eclinm.2024.102586.

Kerrebijn, Isabel, Gyda Bjornsdottir, Keon Arbabi, Lea Urpa, Hele Haapaniemi, Gudmar Thorleifsson, Lilja Stefansdottir, et al. 2026. “The Genetic Architecture of Fibromyalgia Across 2.5 Million Individuals.” Nature Medicine 32 (8): 3060–70. https://doi.org/10.1038/s41591-026-04492-6.

Khan, Fary, Bhasker Amatya, and Mary Galea. 2014. “Management of Fatigue in Persons with Multiple Sclerosis.” Frontiers in Neurology 5: 177. https://doi.org/10.3389/fneur.2014.00177.

Khasnavis, Siddharth, Timothy Belliveau, Amy Arnsten, and Arman Fesharaki-Zadeh. 2024. “Combined Use of Guanfacine and N-Acetylcysteine for the Treatment of Cognitive Deficits After Traumatic Brain Injury.” Neurotrauma Reports 5 (1): 226–31. https://doi.org/10.1089/neur.2023.0124.

Kielland, Anne, Jing Liu, and Leonard A. Jason. 2023. “Do Diagnostic Criteria for ME Matter to Patient Experience with Services and Interventions? Key Results from an Online RDS Survey Targeting Fatigue Patients in Norway.” Journal of Health Psychology 28 (13): 1189–1203. https://doi.org/10.1177/13591053231169191.

Killeen, Peter R., Vivienne A. Russell, and Joseph A. Sergeant. 2013. “A Behavioral Neuroenergetics Theory of ADHD.” Neuroscience and Biobehavioral Reviews 37 (4): 625–57. https://doi.org/10.1016/j.neubiorev.2013.02.011.

Kim, Hyeonwoo, Christiane D. Wrann, Mark Jedrychowski, Sara Vidoni, Yukiko Kitase, Kenichi Nagano, Chenhe Zhou, et al. 2018. “Irisin Mediates Effects on Bone and Fat via αV Integrin Receptors.” Cell 175 (7): 1756–1768.e17. https://doi.org/10.1016/j.cell.2018.10.025.

Kleijn, Willemien P E de, Jolanda De Vries, Elyse E Lower, Marjon D P Elfferich, Robert P Baughman, and Marjolein Drent. 2009. “Fatigue in Sarcoidosis: A Systematic Review.” Current Opinion in Pulmonary Medicine 15 (5): 499–506. https://doi.org/10.1097/MCP.0b013e32832d0403.

Klein, Jon, Jamie Wood, Jillian R. Jaycox, Rahul M. Dhodapkar, Peiwen Lu, Jeff R. Gehlhausen, Alexandra Tabachnikova, et al. 2023. “Distinguishing Features of Long COVID Identified Through Immune Profiling.” Nature 623 (7985): 139–48. https://doi.org/10.1038/s41586-023-06651-y.

Kleine-Borgmann, Jan, and Ulrike Bingel. 2025. “Open-Label Nondeceptive Placebos.” Handbook of Clinical Neurology 213: 51–58. https://doi.org/10.1016/B978-0-443-29884-4.00020-0.

Klempner, M. S., L. T. Hu, J. Evans, C. H. Schmid, G. M. Johnson, R. P. Trevino, D. Norton, et al. 2001. “Two Controlled Trials of Antibiotic Treatment in Patients with Persistent Symptoms and a History of Lyme Disease.” The New England Journal of Medicine 345 (2): 85–92. https://doi.org/10.1056/NEJM200107123450202.

Knadler, Mary Pat, Evelyn Lobo, Jill Chappell, and Richard Bergstrom. 2011. “Duloxetine: Clinical Pharmacokinetics and Drug Interactions.” Clinical Pharmacokinetics 50 (5): 281–94. https://doi.org/10.2165/11539240-000000000-00000.

Knoop, Hans, Gijs Bleijenberg, Marieke F. M. Gielissen, Jos W. M. van der Meer, and Peter D. White. 2007. “Is a Full Recovery Possible After Cognitive Behavioural Therapy for Chronic Fatigue Syndrome?” Psychotherapy and Psychosomatics 76 (3): 171–76. https://doi.org/10.1159/000099844.

Knudsen, Ann Kristin, Anne-Nina Omenås, Samuel B Harvey, Camilla M Løvvik, Linn V Lervik, and Arnstein Mykletun. 2011. “Chronic Fatigue Syndrome in the Media: A Content Analysis of Newspaper Articles.” JRSM Short Reports 2 (5): 42. https://doi.org/10.1258/shorts.2011.011016.

Ko, C, P Lucassen, B van der Linden, A Ballering, and T Olde Hartman. 2022. “Stigma Perceived by Patients with Functional Somatic Syndromes and Its Effect on Health Outcomes — a Systematic Review.” Journal of Psychosomatic Research 154: 110715. https://doi.org/10.1016/j.jpsychores.2021.110715.

Koelle, David M, Serge Barcy, Meei-Li Huang, Rhoda L Ashley, Lawrence Corey, Judy Zeh, Suzanne Ashton, and Dedra Buchwald. 2002. “Markers of Viral Infection in Monozygotic Twins Discordant for Chronic Fatigue Syndrome.” Clinical Infectious Diseases 35 (5): 518–25. https://doi.org/10.1086/341774.

Kol, Amir et al. 2026. “Beyond Macrophages: FIPV Tropism Includes T and B Lymphocytes.” Veterinary Microbiology 313: 110864. https://doi.org/10.1016/j.vetmic.2025.110864.

Komaroff, Anthony L, and Robert Dantzer. 2025. “Causes of Symptoms and Symptom Persistence in Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Cell Reports Medicine 6 (8): 102259. https://doi.org/10.1016/j.xcrm.2025.102259.

Koonce, Rachel M., and Bradley C. Martin. 2024. “The Association Between Suspected Long-COVID and Stimulant Prescribing in the United States.” Psychiatry Research 331: 115686. https://doi.org/10.1016/j.psychres.2023.115686.

Krajewski, PK, W Szukała, and JC Szepietowski. 2024. “The NLRP3 Inflammasome Gene Is Overexpressed in Hidradenitis Suppurativa Lesions: A Preliminary Study on the Role of Pyroptosis in Disease Pathogenesis.” Current Issues in Molecular Biology 46 (3): 2544–52. https://doi.org/10.3390/cimb46030161.

Krishnan, Kalpana, Ying Lin, Kim R. M. Prewitt, and David A. Potter. 2022. “Multidisciplinary Approach to Brain Fog and Related Persisting Symptoms Post COVID-19.” Journal of Health Service Psychology 48 (1): 31–38. https://doi.org/10.1007/s42843-022-00056-7.

Krumholz, Harlan M., Yilun Wu, Mitsuaki Sawano, Rishi Shah, Tianna Zhou, Adith S. Arun, Pavan Khosla, Shayaan Kaleem, et al. 2023. “Post-Vaccination Syndrome: A Descriptive Analysis of Reported Symptoms and Patient Experiences After COVID-19 Immunization.” medRxiv. https://doi.org/10.1101/2023.11.09.23298266.

Krupp, L. B., L. G. Hyman, R. Grimson, P. K. Coyle, P. Melville, S. Ahnn, R. Dattwyler, and B. Chandler. 2003. “Study and Treatment of Post Lyme Disease (STOP-LD): A Randomized Double Masked Clinical Trial.” Neurology 60 (12): 1923–30. https://doi.org/10.1212/01.WNL.0000071227.23769.9e.

Kuczyk, Christina, Martina Nöhre, Christoph Herrmann-Lingen, Maike Stolz, Christian Krauth, Elmar Brähler, Leonard A. Jason, and Martina de Zwaan. 2025. “Reliability and Validity of the German Version of the DePaul Symptom Questionnaire Post-Exertional Malaise (DSQ-PEM).” Frontiers in Psychiatry 16: 1647040. https://doi.org/10.3389/fpsyt.2025.1647040.

Kulin, D., A. Shah, T. Fairlie, K. Staller, S. Nurko, L. Keefer, Q. Aziz, D. A. Drossman, et al. 2026. “Gastrointestinal Symptoms and Systemic Comorbidities in Patients with POTS: A Systematic Review and Meta-Analysis.” Neurogastroenterology & Motility 38 (4): e70305. https://doi.org/10.1111/nmo.70305.

Kupczyk, Maciej, and Piotr Kuna. 2017. “Targeting the PGD2/CRTH2/DP1 Signaling Pathway in Asthma and Allergic Disease: Current Status and Future Perspectives.” Drugs 77 (12): 1281–94. https://doi.org/10.1007/s40265-017-0777-2.

Kuratsune, Hirohiko, Kouzi Yamaguti, Gudrun Lindh, Birgitta Evengard, Mamoru Takahashi, Takashi Machii, Kazuya Matsumura, Yasuyoshi Watanabe, et al. 1998. “Low Levels of Serum Acylcarnitine in Chronic Fatigue Syndrome and Chronic Hepatitis Type C, but Not Seen in Other Diseases.” International Journal of Molecular Medicine 2 (1): 51–56. https://doi.org/10.3892/ijmm.2.1.51.

Kuring, J. K., J. L. Mathias, L. Ward, and G. Tachas. 2023. “Inflammatory Markers in Persons with Clinically-Significant Depression, Anxiety or PTSD: A Systematic Review and Meta-Analysis.” Journal of Psychiatric Research 168: 279–92. https://doi.org/10.1016/j.jpsychires.2023.10.009.

———. 2026. “Inflammatory Markers Associated with Dementia: A Systematic Review and Meta-Analysis.” Journal of Psychiatric Research 197: 26–40. https://doi.org/10.1016/j.jpsychires.2026.02.044.

Kurmashev, Rustem. 2026. “UBE3A Dosage Imbalance as a Molecular Framework Linking Angelman Syndrome and Dup15q-Associated Autism Phenotypes.” American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. https://doi.org/10.1002/ajmg.b.70025.

Kutukova, N. A., A. S. Trulioff, A. V. Polevshchikov, and O. V. Shamova. 2025. “Dual Effects of Acetylcholine on Mast Cell-Nerve Interactions.” Doklady Biological Sciences 525 (1): 350–53. https://doi.org/10.1134/S001249662560054X.

Kwan, Alan C., Joseph E. Ebinger, Janet Wei, Catherine N. Le, Jillian R. Oft, Rachel Zabner, Debbie Teodorescu, et al. 2022. “Apparent Risks of Postural Orthostatic Tachycardia Syndrome Diagnoses After COVID-19 Vaccination and SARS-CoV-2 Infection.” Nature Cardiovascular Research 1 (12): 1187–94. https://doi.org/10.1038/s44161-022-00177-8.

Lamming, Dudley W, Lan Ye, Pekka Katajisto, Marcus D Goncalves, Maki Saitoh, Deanna M Stevens, James G Davis, et al. 2012. “Rapamycin-Induced Insulin Resistance Is Mediated by mTORC2 Loss and Uncoupled from Longevity.” Science 335 (6076): 1638–43. https://doi.org/10.1126/science.1215135.

Le, T., H. J. Kim, Y. Chen, Q. Zhang, X. Wang, Y. Liu, M. Zhou, J. Wu, S. Li, and Z. Xu. 2025. “The eIF2alpha-ATF4 Axis Regulates Mitochondrial Dynamics and Bioenergetics During ER Stress.” Cell Metabolism 35 (4): 678–695.e8. https://doi.org/10.1016/j.cmet.2025.01.012.

Lee, Eui-Cheol, Keon Kim, Sojin Kim, Minju Kim, and Hyoung-Pyo Kim. 2025. “3D Enhancer Architecture Coordinated by CTCF Determines Immune-Related Gene Expression Patterns via RNA Polymerase II Pause-Release in CD4+ T Cells.” Nucleic Acids Research 53 (22): gkaf1404. https://doi.org/10.1093/nar/gkaf1404.

Lee, Ji-Sook, Eliana Lacerda, Caroline Kingdon, Erik Abken, Gaia Susannini, Hazel M. Dockrell, Luis Nacul, and Jacqueline M. Cliff. 2025. “Abnormal T-Cell Activation and Cytotoxic T-Cell Frequency Discriminate Symptom Severity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Translational Medicine 23: 669. https://doi.org/10.1186/s12967-025-07507-x.

Lee, Soo-Yeon, Soo-Jin Oh, Kyung-Uk Yun, Ho-Min Kim, Bong-Hee Kim, Kiho Lee, and Sang-Kyum Kim. 2012. “Expression of Hepatic and Ovarian Cytochrome P450 During Estrous Cycle in Rats.” Archives of Toxicology 86 (1): 75–85. https://doi.org/10.1007/s00204-011-0730-1.

Leemburg, Simone, Andrea Kala, Amita Nataraj, Petra Karkusova, et al. 2025. “LPS-Induced Systemic Inflammation Disrupts Brain Activity in a Region- and Vigilance-State Specific Manner.” Brain, Behavior, and Immunity 120: 12–24. https://doi.org/10.1016/j.bbi.2025.05.002.

Lévi, Francis A, Alper Okyar, Eva Hadadi, Pasquale F Innominato, and Annabelle Ballesta. 2024. “Circadian Regulation of Drug Responses: Toward Sex-Specific and Personalized Chronotherapy.” Annual Review of Pharmacology and Toxicology 64: 89–114. https://doi.org/10.1146/annurev-pharmtox-051920-095416.

Levine, Stephen Z., Anat Rotstein, Arad Kodesh, Sven Sandin, Brian K. Lee, Galit Weinstein, Michal Schnaider Beeri, and Abraham Reichenberg. 2023. “Adult Attention-Deficit/Hyperactivity Disorder and the Risk of Dementia.” JAMA Network Open 6 (10): e2338088. https://doi.org/10.1001/jamanetworkopen.2023.38088.

Levine, Todd D, J Kafaie, L A Zeidman, D S Saperstein, R Massaquoi, R J Bland, and A Pestronk. 2020. “Cryptogenic Small-Fiber Neuropathies: Serum Autoantibody Binding to Trisulfated Heparan Disaccharide and Fibroblast Growth Factor Receptor-3.” Muscle & Nerve 61 (4): 512–15. https://doi.org/10.1002/mus.26748.

Leys, Fabian, Mara Verginer, Elias Kirchler, Loraine Marino, Georg Goebel, Nicole Campese, Sabine Eschlbock, et al. 2025. “Postural Orthostatic Tachycardia Syndrome Is the Most Frequent Cardiovascular Autonomic Disorder Following COVID-19 Infection or Vaccination.” Journal of Neurology 272 (12): 783. https://doi.org/10.1007/s00415-025-13518-x.

Li, Han Ling, Soren Charmsaz, Mark Nakazawa, Sarah L Alden, Marco Brancati, James M Leatherman, Elizabeth Griffin, et al. 2025. “Immune Activation Signatures Associated with Fatigue in Cancer Patients Undergoing Immune Checkpoint Inhibitor Therapy.” Cancer Research Communications 5 (10): 1738–46. https://doi.org/10.1158/2767-9764.CRC-25-0240.

Li, Hongliang et al. 2019. “Adrenergic AAb-Induced POTS in Rabbits.” Journal of the American Heart Association 8: e013006. https://doi.org/10.1161/JAHA.119.013006.

Liles, Nathan W., Elizabeth E. Page, Alexander L. Liles, Sara K. Vesely, Gary E. Raskob, and James N. George. 2016. “Diversity and Severity of Adverse Reactions to Quinine: A Systematic Review.” American Journal of Hematology 91 (5): 461–66. https://doi.org/10.1002/ajh.24314.

Lim, Eun-Jin, and Chang-Gue Son. 2020. “Review of Case Definitions for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).” Journal of Translational Medicine 18 (1): 289. https://doi.org/10.1186/s12967-020-02455-0.

Lim, So Yun, Jacob Lee, Euijin Chang, Ji-Soo Kwon, Choi Young Jang, Yubin Seo, Jin Ju Park, et al. 2026. “Neither Metformin nor Ursodeoxycholic Acid Effectively Treats Postacute Sequelae of COVID-19: A Randomized Clinical Trial.” Annals of Internal Medicine 179 (4): 478–85. https://doi.org/10.7326/ANNALS-25-04883.

Lin, L., Y. Li, F. Ji, J. Lin, M. Zhu, D. Liang, M. Cao, G. Fu, and Y. Fu. 2026. “Shared Latent Genetic Liability Across Fibromyalgia and Psychiatric Traits: Novel Insights from Genomic Structural Equation Modeling.” PLoS Genetics 22 (1): e1012034. https://doi.org/10.1371/journal.pgen.1012034.

Lipkovich, Ilya, Alex Dmitrienko, and Ralph B. D’Agostino. 2017. “Tutorial in Biostatistics: Data-Driven Subgroup Identification and Analysis in Clinical Trials.” Statistics in Medicine 36 (1): 136–96. https://doi.org/10.1002/sim.7064.

Lipworth, Brian J., and Allison Grove. 1997. “Evaluation of Partial Beta-Adrenoceptor Agonist Activity.” British Journal of Clinical Pharmacology 43 (1): 9–14. https://doi.org/10.1111/j.1365-2125.1997.tb00025.x.

Liu, Philip Z., David M. Raizen, Carsten Skarke, Thomas G. Brooks, and Ron C. Anafi. 2025. “Genetic Variants Associated with Chronic Fatigue Syndrome Predict Population-Level Fatigue Severity and Actigraphic Measurements.” Sleep 48 (2): zsae243. https://doi.org/10.1093/sleep/zsae243.

Livieratos, Achilleas, Steven W Lockley, and Sotirios Tsiodras. 2025. “Post Infectious Fatigue and Circadian Rhythm Disruption in long-COVID and Other Infections: A Need for Further Research.” EClinicalMedicine 80: 103073. https://doi.org/10.1016/j.eclinm.2025.103073.

Lo, Y., P. L. Yi, Y. T. Hsiao, et al. 2022. “Hypocretin in Locus Coeruleus and Dorsal Raphe Nucleus Mediates Inescapable Footshock Stimulation (IFS)-Induced REM Sleep Alteration.” Sleep 45 (3). https://doi.org/10.1093/sleep/zsab301.

Locci, Andrea, and Graziano Pinna. 2017. “Neurosteroid Biosynthesis down-Regulation and Changes in GABA(A) Receptor Subunit Composition: A Biomarker Axis in Stress-Induced Cognitive and Emotional Impairment.” British Journal of Pharmacology 174 (19): 3226–41. https://doi.org/10.1111/bph.13843.

Loebel, Madlen, Patricia Grabowski, Harald Heidecke, Stephan Bauer, Leif G. Hanitsch, Kirsten Wittke, Christian Meisel, et al. 2016. “Antibodies to Beta Adrenergic and Muscarinic Cholinergic Receptors in Patients with Chronic Fatigue Syndrome.” Brain, Behavior, and Immunity 52: 32–39. https://doi.org/10.1016/j.bbi.2015.09.013.

Löhn, Matthias, and Klaus Josef Wirth. 2024. “Potential Pathophysiological Role of the Ion Channel TRPM3 in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and the Therapeutic Effect of Low-Dose Naltrexone.” Journal of Translational Medicine 22 (1): 630. https://doi.org/10.1186/s12967-024-05412-3.

Looper, Karl J, and Laurence J Kirmayer. 2004. “Perceived Stigma in Functional Somatic Syndromes and Comparable Medical Conditions.” Journal of Psychosomatic Research 57 (4): 373–78. https://doi.org/10.1016/j.jpsychores.2004.03.005.

Loth, Yannick. 2026. “Myalgic Encephalomyelitis / Chronic Fatigue Syndrome: A Comprehensive Medical Documentation.” https://doi.org/10.5281/zenodo.19627450.

Lu, Yuanqing, Xiang Li, Daoying Geng, Nan Mei, et al. 2020. “Cerebral Micro-Structural Changes in COVID-19 Patients – an MRI-Based 3-Month Follow-up Study.” EClinicalMedicine 25: 100484. https://doi.org/10.1016/j.eclinm.2020.100484.

Lüthi, Anita, and Maiken Nedergaard. 2025. “Anything but Small: Microarousals Stand at the Crossroad Between Noradrenaline Signaling and Key Sleep Functions.” Neuron 113 (4): 509–23. https://doi.org/10.1016/j.neuron.2024.12.009.

Lutsenko, Svetlana, Clorissa Washington-Hughes, Martina Ralle, and Katharina Schmidt. 2019. “Copper and the Brain Noradrenergic System.” Journal of Biological Inorganic Chemistry 24 (8): 1179–88. https://doi.org/10.1007/s00775-019-01737-3.

Lv, H., X. Yu, P. Wang, M. Luo, et al. 2024. “Locus Coeruleus Tyrosine Hydroxylase Positive Neurons Mediated the Peripheral and Central Therapeutic Effects of Transcutaneous Auricular Vagus Nerve Stimulation.” Brain Stimulation 17 (1): 49–64. https://doi.org/10.1016/j.brs.2023.12.008.

Lynch, Erica N, Claudia Campani, Tommaso Innocenti, Gabriele Dragoni, Maria Rosa Biagini, Paolo Forte, and Andrea Galli. 2022. “Understanding Fatigue in Primary Biliary Cholangitis: From Pathophysiology to Treatment Perspectives.” World Journal of Hepatology 14 (6): 1111–19. https://doi.org/10.4254/wjh.v14.i6.1111.

Maccallini, P. 2026. “Biological Insights from Genome-Wide Association Studies and Whole Genome Sequencing of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome.” Research Square [Preprint], June. https://doi.org/10.21203/rs.3.rs-9702020/v1.

Machado, N L S, N Lynch, L H A Costa, D Melville, H Kucukdereli, S Kaur, et al. 2025. “Preoptic EP3R Neurons Constitute a Two-Way Switch for Fever and Torpor.” Nature 644 (8076): 463–72. https://doi.org/10.1038/s41586-025-09056-1.

Magistretti, Pierre J., and Igor Allaman. 2018. “Lactate in the Brain: From Metabolic End-Product to Signalling Molecule.” Nature Reviews Neuroscience 19 (4): 235–49. https://doi.org/10.1038/nrn.2018.19.

Magnus, Per et al. 2015. “CFS/ME Is Associated with Pandemic Influenza Infection, but Not with an Adjuvanted Pandemic Influenza Vaccine.” Vaccine 33 (46): 6173–77. https://doi.org/10.1016/j.vaccine.2015.10.018.

Maguire, Cole, Jing Chen, Nadine Rouphael, Brinkley A. Morse, Annmarie Hoch, Harry Pickering, Hoang Van Phan, et al. 2026. “Virus Reactivation in Acute and Long COVID-19.” Nature.

Mahad, Don H., Bruce D. Trapp, and Hans Lassmann. 2015. “Pathological Mechanisms in Progressive Multiple Sclerosis.” Lancet Neurology 14 (2): 183–93. https://doi.org/10.1016/S1474-4422(14)70256-X.

Maintz, Laura, and Natalija Novak. 2007. “Histamine and Histamine Intolerance.” American Journal of Clinical Nutrition 85 (5): 1185–96. https://doi.org/10.1093/ajcn/85.5.1185.

Maksoud, Reem, Stanley du Preez, Natalie Eaton-Fitch, Kiran Thapaliya, Leighton Barnden, Hélène Cabanas, Donald Staines, and Sonya Marshall-Gradisnik. 2020. “A Systematic Review of Neurological Impairments in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Neuroimaging Techniques.” PLoS One 15 (4): e0232475. https://doi.org/10.1371/journal.pone.0232475.

Malatji, Bontle G., Helgard Meyer, Shayne Mason, Udo F. H. Engelke, Ron A. Wevers, Mari van Reenen, and Carolus J. Reinecke. 2017. “A Diagnostic Biomarker Profile for Fibromyalgia Syndrome Based on an NMR Metabolomics Study of Selected Patients and Controls.” BMC Neurology 17 (1): 88. https://doi.org/10.1186/s12883-017-0863-9.

Malik, A, G Lopate, G Hayat, J Jones, R Atluri, B Malo, and A Pestronk. 2019. “Prevalence of Axonal Sensory Neuropathy with IgM Binding to Trisulfated Heparin Disaccharide in Patients with Fibromyalgia.” Journal of Clinical Neuromuscular Disease 20 (3): 103–10. https://doi.org/10.1097/CND.0000000000000236.

Man-Son-Hing, Malcolm, George Wells, and Andy Lau. 1998. “Quinine for Nocturnal Leg Cramps: A Meta-Analysis Including Unpublished Data.” Journal of General Internal Medicine 13 (9): 600–606.

Mar, Philip L., and Satish R. Raj. 2020. “Postural Orthostatic Tachycardia Syndrome: Mechanisms and New Therapies.” Annual Review of Medicine 71: 235–48. https://doi.org/10.1146/annurev-med-041818-011630.

Mariman, An N., Dirk P. Vogelaers, Els Tobback, Liesbeth M. Delesie, Ignace P. Hanoulle, and Dirk A. Pevernagie. 2013. “Sleep in the Chronic Fatigue Syndrome.” Sleep Medicine Reviews 17 (3): 193–99. https://doi.org/10.1016/j.smrv.2012.06.003.

Markussen, Fredrik Andreas Falkenberg, Fernando Cázarez-Márquez, Vebjørn Jacobsen Melum, David G Hazlerigg, and Shona H Wood. 2024. “C-Fos Induction in the Choroid Plexus, Tanycytes and Pars Tuberalis Is an Early Indicator of Spontaneous Arousal from Torpor in a Deep Hibernator.” Journal of Experimental Biology 227 (10): jeb247224. https://doi.org/10.1242/jeb.247224.

Marraccini, Marisa E., Lisa L. Weyandt, Joseph S. Rossi, and Bergljot Gyda Gudmundsdottir. 2016. “Neurocognitive Enhancement or Impairment? A Systematic Meta-Analysis of Prescription Stimulant Effects on Processing Speed, Decision-Making, Planning, and Cognitive Perseveration.” Experimental and Clinical Psychopharmacology 24 (4): 269–84. https://doi.org/10.1037/pha0000079.

Masini, E., P. Blandina, S. Brunelleschi, and P. F. Mannaioni. 1982. “Evidence for H2-Receptor-Mediated Inhibition of Histamine Release from Isolated Rat Mast Cells.” Agents and Actions 12 (1–2): 85–88. https://doi.org/10.1007/BF01965111.

Masson, Jean-Daniel, Gherici Badran, Romain K Gherardi, François-Jérôme Authier, and Géraldine Crépeaux. 2024. “Widespread Myalgia and Chronic Fatigue: Phagocytes from Macrophages Myofasciitis Patients Exposed to Aluminum Oxyhydroxide-Adjuvanted Vaccine Exhibit Specific Inflammatory, Autophagic, and Mitochondrial Responses.” Toxics 12 (7): 491. https://doi.org/10.3390/toxics12070491.

Matsuyama, S., Y. Tanaka, R. Hasebe, et al. 2021. “Gateway Reflex and Mechanotransduction.” Frontiers in Immunology 12: 780451. https://doi.org/10.3389/fimmu.2021.780451.

Maughan, Ronald J., and Susan M. Shirreffs. 2019. “Muscle Cramping During Exercise: Causes, Solutions, and Questions Remaining.” Sports Medicine 49 (Suppl 2): 115–24. https://doi.org/10.1007/s40279-019-01162-1.

May, Marcella, Sara F. Milrad, Dolores M. Perdomo, Sara J. Czaja, Mary Ann Fletcher, Devika R. Jutagir, Daniel L. Hall, Nancy Klimas, and Michael H. Antoni. 2020. “Post-Exertional Malaise Is Associated with Greater Symptom Burden and Psychological Distress in Patients Diagnosed with Chronic Fatigue Syndrome.” Journal of Psychosomatic Research 129: 109893. https://doi.org/10.1016/j.jpsychores.2019.109893.

McManimen, Stephanie L, Damani McClellan, Jamie Stoothoff, and Leonard A Jason. 2018. “Effects of Unsupportive Social Interactions, Stigma, and Symptoms on Patients with Myalgic Encephalomyelitis and Chronic Fatigue Syndrome.” Journal of Community Psychology 46 (8): 959–71. https://doi.org/10.1002/jcop.21984.

McManimen, Stephanie L., Madison L. Sunnquist, and Leonard A. Jason. 2019. “Deconstructing Post-Exertional Malaise: An Exploratory Factor Analysis.” Journal of Health Psychology 24 (2): 188–98. https://doi.org/10.1177/1359105316664139.

Medina-Coss Y Leon, R., E. Lezama, I. Marquez, et al. 2025. “Adrenergic Modulation of Cortical Gain and Sensory Processing in the Mouse Visual Cortex.” Brain Sciences 15 (4). https://doi.org/10.3390/brainsci15040406.

Melby, Line, and Roshan das Nair. 2024. “’We Have No Services for You…so You Have to Make the Best Out of It’: A Qualitative Study of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients’ Dissatisfaction with Healthcare Services.” Health Expectations 27 (1): e13900. https://doi.org/10.1111/hex.13900.

Meng, Shuo, Chunfeng Cao, Minghua Zhang, Jing Dong, and Minpeng Lu. 2024. “Mindfulness Meditation for Fibromyalgia Syndrome: A Systematic Review and Meta-Analysis.” Pain Physician 27 (8): 479–94. https://pubmed.ncbi.nlm.nih.gov/39621973/.

Meral Ketenci, B, HB Meral, and Z Meral Obholzer. 2026. “Determinants of Kinesiophobia in Patients with Hidradenitis Suppurativa: The Role of Pain, Fatigue, Physical Activity and Depression.” BMC Psychology. https://doi.org/10.1186/s40359-026-05165-2.

Merzon, Eugene, Michael Weiss, Brian Krone, Shira Cohen, Galia Ilani, Shlomo Vinker, Avivit Cohen-Golan, et al. 2022. “Clinical and Socio-Demographic Variables Associated with the Diagnosis of Long COVID Syndrome in Youth: A Population-Based Study.” International Journal of Environmental Research and Public Health 19 (10): 5993. https://doi.org/10.3390/ijerph19105993.

Micale, Chiara, Su Golder, Karen O’Connor, Davy Weissenbacher, Robert Gross, Sean Hennessy, and Graciela Gonzalez-Hernandez. 2024. “Patient-Reported Reasons for Antihypertensive Medication Change: A Quantitative Study Using Social Media.” Drug Safety 47 (1): 81–91. https://doi.org/10.1007/s40264-023-01366-5.

Migliorini, Filippo, Nicola Maffulli, Jörg Eschweiler, Alice Baroncini, Markus Tingart, and Giorgia Colarossi. 2023. “Duloxetine for Fibromyalgia Syndrome: A Systematic Review and Meta-Analysis.” Journal of Orthopaedic Surgery and Research 18 (1): 504. https://doi.org/10.1186/s13018-023-03995-z.

Miles, M. P., and P. M. Clarkson. 1994. “Exercise-Induced Muscle Pain, Soreness, and Cramps.” Journal of Sports Medicine and Physical Fitness 34 (3): 203–16.

Minzenberg, Michael J, and Cameron S Carter. 2008. “Modafinil: A Review of Neurochemical Actions and Effects on Cognition.” Neuropsychopharmacology 33 (7): 1477–1502. https://doi.org/10.1038/sj.npp.1301534.

Mitkus, Robert J, David B King, Marie A Hess, Richard A Forshee, and Mark O Walderhaug. 2011. “Updated Aluminum Pharmacokinetics Following Infant Exposures Through Diet and Vaccination.” Vaccine 29 (51): 9538–43. https://doi.org/10.1016/j.vaccine.2011.09.124.

Moffitt, Terrie E, Renate Houts, Philip Asherson, Daniel W Belsky, David L Corcoran, et al. 2015. “Is Adult ADHD a Childhood-Onset Neurodevelopmental Disorder? Evidence from a Four-Decade Longitudinal Cohort Study.” American Journal of Psychiatry 172 (10): 967–77. https://doi.org/10.1176/appi.ajp.2015.14101266.

Moghimi Dehkordi, Zohreh, Jamshid Salamzadeh, Maria Tavakoli-Ardakani, Atefeh Abedini, and Farzaneh Dastan. 2026. “Efficacy of a Saffron-Based (Crocus sativus l.) Herbal Supplement on Sleep Disturbances in Pulmonary Sarcoidosis Patients.” Iranian Journal of Pharmaceutical Research 25 (1): e169041. https://doi.org/10.5812/ijpr-169041.

Mohammadi, Sajad, Mostafa Naseri, Niloofar Faridi, Parisa Zareie, Leila Zare, Javad Mirnajafi-Zadeh, and S. Zahra Bathaie. 2023. “Saffron Carotenoids Reversed the UCMS-Induced Depression and Anxiety in Rats: Behavioral and Biochemical Parameters, and Inhibition of NMDA Receptor.” Phytomedicine 119: 154989. https://doi.org/10.1016/j.phymed.2023.154989.

Moldofsky, Harvey, and John Patcai. 2011. “Chronic Widespread Musculoskeletal Pain, Fatigue, Depression and Disordered Sleep in Chronic Post-SARS Syndrome; a Case-Controlled Study.” BMC Neurology 11: 37. https://doi.org/10.1186/1471-2377-11-37.

Monchaux de Oliveira, Caroline, Adrien Vignault, Angèle Guille, et al. 2026. “Exploring the Neurofunctional Potential of Saffron Bioactives: From Mood-Related Behavioral Modulation to Brain Access of Its Compounds.” Food & Function 17 (6): 2800–2818. https://doi.org/10.1039/d5fo04830c.

Montalvo, Mithil, Priya Nallapaneni, Sara Hassan, Samuel Nurko, Sean J. Pittock, et al. 2022. “Autoimmune Gastrointestinal Dysmotility Following SARS-CoV-2 Infection Successfully Treated with Intravenous Immunoglobulin.” Neurogastroenterology and Motility 34 (7): e14314. https://doi.org/10.1111/nmo.14314.

Montoya, Jose G, Tyson H Holmes, Jill N Anderson, Holden T Maecker, Yael Rosenberg-Hasson, Ian J Valencia, Lily Chu, Jarred W Younger, Cristina M Tato, and Mark M Davis. 2017. “Cytokine Signature Associated with Disease Severity in Chronic Fatigue Syndrome Patients.” Proceedings of the National Academy of Sciences 114 (34): E7150–58. https://doi.org/10.1073/pnas.1710519114.

Montoya, Jose G, Andreas M Kogelnik, Munveer Bhangoo, Mitchell R Lunn, Louis Flamand, Lindsey E Merrihew, Tessa Watt, Jessica T Kubo, Jane Paik, and Manisha Desai. 2013. “Randomized Clinical Trial to Evaluate the Efficacy and Safety of Valganciclovir in a Subset of Patients with Chronic Fatigue Syndrome.” Journal of Medical Virology 85 (12): 2101–9. https://doi.org/10.1002/jmv.23713.

Moran, B, CM Smith, A Zaborowski, M Ryan, J Karman, RW Dunstan, KM Smith, et al. 2023. “Targeting the NLRP3 Inflammasome Reduces Inflammation in Hidradenitis Suppurativa Skin.” British Journal of Dermatology 189 (4): 447–58. https://doi.org/10.1093/bjd/ljad184.

Morland, Cecilie, Krister A. Andersson, Øyvind P. Haugen, Andreas Gille, Stefan Offermanns, Ulrik Wisløff, Jon Storm-Mathisen, Linda H. Bergersen, et al. 2017. “Exercise Induces Cerebral VEGF and Angiogenesis via the Lactate Receptor HCAR1.” Nature Communications 8: 15557. https://doi.org/10.1038/ncomms15557.

Morris, Gerwyn, Michael Berk, Piotr Galecki, Ken Walder, and Michael Maes. 2016. “The Neuro-Immune Pathophysiology of Central and Peripheral Fatigue in Systemic Immune-Inflammatory and Neuro-Immune Diseases.” Molecular Neurobiology 53 (2): 1195–1219. https://doi.org/10.1007/s12035-015-9090-9.

Mosconi, Lisa. 2013. “Glucose Metabolism in Normal Aging and Alzheimer’s Disease: Methodological and Physiological Considerations for PET Studies.” Clinical and Translational Imaging 1 (4): 217–33. https://doi.org/10.1007/s40336-013-0026-y.

Mueller, Chantal, James C. Lin, Sulaiman Sheriff, Andrew A. Maudsley, and Jarred W. Younger. 2020. “Evidence of Widespread Metabolite Abnormalities in Myalgic encephalomyelitis/chronic fatigue syndrome: Assessment with Whole-Brain Magnetic Resonance Spectroscopy.” Brain Imaging and Behavior 14 (2): 562–72. https://doi.org/10.1007/s11682-018-0029-4.

Mughal, Sheeza, Félix Andújar-Sánchez, Maria Sabater-Arcis, Glòria Garrabou, Joaquim Fernández-Solà, Jose Alegre-Martin, Ramon Sanmartin-Sentañes, et al. 2025. “Metabolic Adaptation and Fragility in Healthy 3D in Vitro Skeletal Muscle Tissues Exposed to Chronic Fatigue Syndrome and Long COVID-19 Sera.” Biofabrication 17 (4). https://doi.org/10.1088/1758-5090/adf66c.

Mundorf, Anna Katharina, Amelie Semmler, Harald Heidecke, Matthias Schott, Falk Steffen, Stefan Bittner, Karl J. Lackner, Karin Schulze-Bosse, et al. 2024. “Clinical and Diagnostic Features of Post-Acute COVID-19 Vaccination Syndrome (PACVS).” Vaccines 12 (7): 790. https://doi.org/10.3390/vaccines12070790.

Muñoz-Cruz, Samira, Yadira Mendoza-Rodríguez, Karen E Nava-Castro, Lilian Yepez-Mulia, and Jorge Morales-Montor. 2015. “Gender-Related Effects of Sex Steroids on Histamine Release and Fc$\epsilon$RI Expression in Rat Peritoneal Mast Cells.” Journal of Immunology Research 2015: 351829. https://doi.org/10.1155/2015/351829.

Murakawa, Tomohiro, Mamoru Suzuki, Keiichi Fukui, Tetsuya Masuda, Eiichi Mizohata, Ikuko Miyahara, Itaru Kurauchi, et al. 2025. “Real-Time Capture of Domain Movements During Copper Amine Oxidase Catalysis by Mix-and-Inject Serial Crystallography.” Nature Communications 16 (1): 11149. https://doi.org/10.1038/s41467-025-67230-5.

Murin, P J, F Khasiyev, J Profirovic, M Fedorova, and J Kafaie. 2024. “Comparing FGFR-3 and TS-HDS Seropositive Small Fiber Neuropathy: Unique Patient Features, Symptoms, Laboratory, and Nerve Conduction Study Findings.” Journal of Clinical Neuromuscular Disease 25 (4): 171–77. https://doi.org/10.1097/CND.0000000000000478.

Murrough, James W., Xiangling Mao, Katherine A. Collins, Crystal Kelly, George Andrade, Paul Nestadt, Susan M. Levine, Sanjay J. Mathew, and Dikoma C. Shungu. 2010. “Increased Ventricular Lactate in Chronic Fatigue Syndrome Measured by 1H MRS Imaging at 3.0 t. II: Comparison with Major Depressive Disorder.” NMR in Biomedicine 23 (6): 643–50. https://doi.org/10.1002/nbm.1512.

Mushak, Paul. 2016. “Temporal Stability of Chemical Hormesis (CH): Is CH Just a Temporary Stop on the Road to Thresholds and Toxic Responses?” Science of the Total Environment 569-570: 1446–56. https://doi.org/10.1016/j.scitotenv.2016.06.233.

Nakatomi, Yasuhito, Kei Mizuno, Akemi Ishii, Yasuhiro Wada, Masaaki Tanaka, Shusaku Tazawa, Kayo Onoe, et al. 2014. “Neuroinflammation in Patients with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis: An ¹¹C-(R)-PK11195 PET Study.” Journal of Nuclear Medicine 55 (6): 945–50. https://doi.org/10.2967/jnumed.113.131045.

Napolitano, Francesco, Diego Carrella, Xin Gao, and Diego di Bernardo. 2019. “Gep2pep: A Bioconductor Package for the Creation and Analysis of Pathway-Based Expression Profiles.” Bioinformatics 36 (6): 1944–45. https://doi.org/10.1093/bioinformatics/btz803.

Napolitano, Francesco, Francesco Sirci, Diego Carrella, and Diego di Bernardo. 2016. “Drug-Set Enrichment Analysis: A Novel Tool to Investigate Drug Mode of Action.” Bioinformatics 32 (2): 235–41. https://doi.org/10.1093/bioinformatics/btv536.

Narita, Shin-ichiroh, Randall M Goldblum, Cheryl S Watson, Edward G Brooks, D Mark Estes, Edward M Curran, and Terumi Midoro-Horiuti. 2007. “Environmental Estrogens Induce Mast Cell Degranulation and Enhance IgE-Mediated Release of Allergic Mediators.” Environmental Health Perspectives 115 (1): 48–52. https://doi.org/10.1289/ehp.9378.

Natelson, B. H. et al. 2017. “Elevations of Ventricular Lactate Levels Occur in Both Chronic Fatigue Syndrome and Fibromyalgia.” Fatigue: Biomedicine, Health & Behavior 5 (1): 15–20. https://doi.org/10.1080/21641846.2017.1280114.

Nawarathna, Gayani Shashikala, Dinesh Ishara Ariyasinghe, Nadeeka Balasooriya, Asha Fernando, and Tharaka Lagath Dassanayake. 2026. “Effects of l-Theanine-Caffeine Combination on Selective Attention Among Adolescents with Attention-Deficit Hyperactivity Disorder: A Double-Blind, Placebo-Controlled, Crossover Study.” Nutritional Neuroscience, 1–14. https://doi.org/10.1080/1028415X.2026.2659148.

Nedergaard, Maiken. 2026. “The Oscillatory Biology of Sleep: Linkage to Dementia.” Science 392 (6800): 821–26. https://doi.org/10.1126/science.aeg2276.

Neu, Daniel, Olivier Mairesse, Paul Verbanck, and Olivier Le Bon. 2015. “Slow Wave Sleep in the Chronically Fatigued: Power Spectra Distribution Patterns in Chronic Fatigue Syndrome and Primary Insomnia.” Clinical Neurophysiology 126 (10): 1926–33. https://doi.org/10.1016/j.clinph.2014.12.016.

Newton, Julia L, Mark Hudson, Phaedra Tachtatzis, Kathryn Sutcliffe, Jessie Pairman, Jennifer A Burt, and David E J Jones. 2007. “Population Prevalence and Symptom Associations of Autonomic Dysfunction in Primary Biliary Cirrhosis.” Hepatology 45 (6): 1496–1505. https://doi.org/10.1002/hep.21609.

Ng, Q X, K M L Neo, B Zhang, Z C Yeo, P N Anitha, and A C Y Chan. 2026. “Use of Intravenous Immunoglobulin Therapy for Small Fiber Neuropathy: A Systematic Review of Current Clinical Evidence.” Journal of the Peripheral Nervous System 31 (3): e70138. https://doi.org/10.1111/jns.70138.

Nielsen, Maria Juul, Henrik Bjarke Vaegter, and Karin Due Bruun. 2026. “Symptom Response to Low-Dose Naltrexone in Fibromyalgia: An Exploratory Analysis of the Randomized Placebo-Controlled FINAL Trial.” Pain Management Nursing. https://doi.org/10.1016/j.pmn.2026.07.020.

Nikles, Jane, Kerrie Evans, Anna Hams, and Michele Sterling. 2022. “A Systematic Review of N-of-1 Trials and Single Case Experimental Designs in Physiotherapy for Musculoskeletal Conditions.” Musculoskeletal Science and Practice 62: 102639. https://doi.org/10.1016/j.msksp.2022.102639.

Nikolaeva-Koleva, M., L. Butron, S. González-Rodríguez, I. Devesa, P. Valente, M. Serafini, A. A. Genazzani, T. Pirali, et al. 2021. “A Capsaicinoid-Based Soft Drug, AG1529, for Attenuating TRPV1-Mediated Histaminergic and Inflammatory Sensory Neuron Excitability.” Scientific Reports 11 (1): 246. https://doi.org/10.1038/s41598-020-80725-z.

Nilsson, Ida, Jeremy Palmer, Eirini Apostolou, Carl G. Gottfries, Muhammad Rizwan, Charlotte Dahle, and Anders Rosén. 2020. “Metabolic Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Not Due to Anti-Mitochondrial Antibodies.” Frontiers in Medicine 7: 108. https://doi.org/10.3389/fmed.2020.00108.

Nissan, J, A Blokh, N Ben-Shabat, H Heidecke, G Halpert, Y Shoenfeld, and H Amital. 2023. “Possible Association Between Anti-Trisulfated-Heparin-Disaccharide (TS-HDS) Immunoglobulin M Autoantibody and Fibromyalgia Syndrome.” Israel Medical Association Journal 25 (3): 187–90. https://www.ima.org.il/MedicineInsight/journal/Vol-25-3-Mar-2023.

Noguchi, Yoshihiro, Tomoya Tachi, and Hiroki Teramachi. 2021. “Detection Algorithms and Attentive Points of Safety Signal Using Spontaneous Reporting Systems as a Clinical Data Source.” Briefings in Bioinformatics 22 (6): bbab347. https://doi.org/10.1093/bib/bbab347.

Norris, Jade E., Rachel Prosser, Anna Remington, Laura Crane, and Katie Maras. 2024. “Disclosing an Autism Diagnosis Improves Ratings of Candidate Performance in Employment Interviews.” Autism 28 (4): 1045–50. https://doi.org/10.1177/13623613231203739.

Novak, P., D. M. Systrom, A. Witte, S. P. Marciano, D. Felsenstein, J. M. Milunsky, A. Milunsky, J. Krier, and M. C. Fishman. 2026. “Shared Autonomic Phenotype of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” PLoS One 21 (1): e0341278. https://doi.org/10.1371/journal.pone.0341278.

O’Callaghan, C., F. H. Hezemans, N. Subramaniam, et al. 2025. “Pharmacological and Pupillary Evidence for the Noradrenergic Contribution to Reinforcement Learning in Parkinson’s Disease.” Communications Biology 8 (1): 1223. https://doi.org/10.1038/s42003-025-08627-2.

Ocak, Umut, Pinar Eser Ocak, Lei Huang, Weilin Xu, Yi Zuo, Ping Li, Marcin Gamdzyk, et al. 2020. “Inhibition of Mast Cell Tryptase Attenuates Neuroinflammation via PAR-2/P38/NF-kB Pathway Following Asphyxial Cardiac Arrest in Rats.” Journal of Neuroinflammation 17 (1): 144. https://doi.org/10.1186/s12974-020-01808-2.

Ohdo, Shigehiro. 2021. “Chrono-Drug Discovery and Development Based on Circadian Rhythm of Molecular, Cellular and Organ Level.” Biological & Pharmaceutical Bulletin 44 (6): 747–61. https://doi.org/10.1248/bpb.b21-00277.

Okamoto, L. E., V. Urechie, S. Rigo, J. J. Abner, M. Giesecke, J. A. S. Muldowney, R. Furlan, et al. 2024. “Hyperadrenergic Postural Tachycardia Syndrome: Clinical Biomarkers and Response to Guanfacine.” Hypertension 81 (11): 2237–47. https://doi.org/10.1161/HYPERTENSIONAHA.124.23035.

Okyar, Alper, Dilek Ozturk Civelek, Yasemin Kubra Akyel, Saliha Surme, Zeliha Pala Kara, and Ibrahim Halil Kavakli. 2024. “The Role of the Circadian Timing System on Drug Metabolism and Detoxification: An Update.” Expert Opinion on Drug Metabolism & Toxicology 20 (6): 503–17. https://doi.org/10.1080/17425255.2024.2356167.

Ologunowa, Adaeze, Mary N Otoo, Aisling R Caffrey, Ashley Buchanan, Uchenna J Eze, and Amisha Vyas. 2025. “Efficacy of Low-Dose Naltrexone in Treating Patients with Fibromyalgia: Systematic Review and Meta-Analysis.” Journal of Pain & Palliative Care Pharmacotherapy 39 (3): 363–73. https://doi.org/10.1080/15360288.2025.2496526.

Ortelli, Paola, Francesco Benso, Davide Ferrazzoli, et al. 2022. “Global Slowness and Increased Intra-Individual Variability Are Key Features of Attentional Deficits and Cognitive Fluctuations in Post COVID-19 Patients.” Scientific Reports 12: 13123. https://doi.org/10.1038/s41598-022-17463-x.

Ortiz, M., M. M. Schreiner, N. Volz, T. Tissen-Diabaté, E. Jansen, J. Siewert, M. Teut, and B. Brinkhaus. 2026. “Acupressure and Hypnosis in Arterial Hypertension: A Series of N-of-1 Trials.” Frontiers in Medicine 13: 1871793. https://doi.org/10.3389/fmed.2026.1871793.

Osorio-Forero, Alejandro, Romain Cardis, Gil Vantomme, Anita Guillaume-Gentil, Georgia Katsioudi, Christophe Devenoges, Laura M. J. Fernandez, and Anita Lüthi. 2021. “Noradrenergic Circuit Control of Non-REM Sleep Substates.” Current Biology 31 (22): 5009–5023.e7. https://doi.org/10.1016/j.cub.2021.09.041.

Osorio-Mendoza, Jazmin, Jana-Thabea Kiehn, Sarah Stenger, Keno O. Heinen, Laura Griewahn, Christiane E. Koch, Undine Haferkamp, et al. 2025. “Regulation of Hedonic Feeding Rhythms by Circadian Clocks in Leptin-Receptive Neurons.” Molecular Metabolism 100: 102221. https://doi.org/10.1016/j.molmet.2025.102221.

Packard, K. A., and M. M. Khan. 2003. “Effects of Histamine on Th1/Th2 Cytokine Balance.” International Immunopharmacology 3 (7): 909–20. https://doi.org/10.1016/S1567-5769(02)00235-7.

Pajuelo, D., M. Dezortova, M. Hajek, M. Ibrahimova, and I. Ibrahim. 2024. “Metabolic Changes Assessed by 1H MR Spectroscopy in the Corpus Callosum of Post-COVID Patients.” MAGMA 37 (5): 937–46. https://doi.org/10.1007/s10334-024-01171-w.

Palacios, Natalia, Samantha Molsberry, Kathryn C. Fitzgerald, and Anthony L. Komaroff. 2023. “Different Risk Factors Distinguish Myalgic Encephalomyelitis/Chronic Fatigue Syndrome from Severe Fatigue.” Scientific Reports 13 (1): 2469. https://doi.org/10.1038/s41598-023-29329-x.

Palma, Jose-Alberto, and Horacio Kaufmann. 2020b. “Management of Orthostatic Hypotension.” Continuum (Minneapolis, Minn.) 26 (1): 154–77. https://doi.org/10.1212/CON.0000000000000816.

———. 2020a. “Management of Orthostatic Hypotension.” Continuum (Minneapolis, Minn.) 26 (1): 154–77. https://doi.org/10.1212/CON.0000000000000816.

Palomo, Marı́a et al. 2026. “Chronic Reactivation of Persistent Human Herpesviruses EBV, HHV-6 and VZV and Heightened Anti-dUTPase IgG Antibodies Are a Recurrent Hallmark in Post-Infectious ME/CFS and Is Associated with Fatigue.” Journal of Medical Virology. https://doi.org/10.1002/jmv.70769.

Panov, Josif, Liron Simchi, Yaron Feuermann, and Hanoch Kaphzan. 2020. “Bioinformatics Analyses of the Transcriptome Reveal Ube3a-Dependent Effects on Mitochondrial-Related Pathways.” International Journal of Molecular Sciences 21 (11): 4156. https://doi.org/10.3390/ijms21114156.

Papadopoulos, Andrew S., and Anthony J. Cleare. 2012. “Hypothalamic-Pituitary-Adrenal Axis Dysfunction in Chronic Fatigue Syndrome.” Nature Reviews Endocrinology 8 (1): 22–32. https://doi.org/10.1038/nrendo.2011.153.

Parisi, Morgane, Ludovic Marin, Justine Lozano-Goupil, Théo Fauviaux, Valérie Vattier, Delphine Capdevielle, and Stéphane Raffard. 2026. “Exploring the Role of Stigma and Reduced Facial Expressions in Shaping First Impressions of Individuals with Schizophrenia.” Schizophrenia Bulletin 52 (4). https://doi.org/10.1093/schbul/sbaf104.

Park, Ji W., Luis E. Okamoto, Cyndya A. Shibao, and Italo Biaggioni. 2020. “Pharmacologic Treatment of Orthostatic Hypotension.” Autonomic Neuroscience 229: 102721. https://doi.org/10.1016/j.autneu.2020.102721.

Patone, Martina, Xue W. Mei, Lahiru Handunnetthi, Sharon Dixon, Francesco Zaccardi, Manu Shankar-Hari, Peter Watkinson, et al. 2022. “Risks of Myocarditis, Pericarditis, and Cardiac Arrhythmias Associated with COVID-19 Vaccination or SARS-CoV-2 Infection.” Nature Medicine 28 (2): 410–22. https://doi.org/10.1038/s41591-021-01630-0.

Peluso, Michael J. et al. 2024. “Tissue-Based T Cell Activation and Viral RNA Persist for up to 2 Years After SARS-CoV-2 Infection.” Science Translational Medicine 16 (754): eadk3295. https://doi.org/10.1126/scitranslmed.adk3295.

Peng, Madeleine S., Jialin Jiang, Lucia Polizzi, Tiancheng Shi, Rakshitha Ramkumar, Victor O. Anosike, Giulia Guasoni, et al. 2026. “Dysregulated Adult Hippocampal Neurogenesis in Major Depressive Disorders.” Nature Medicine 32. https://doi.org/10.1038/s41591-026-04571-8.

Peppercorn, Katie, Sayan Sharma, Christina D. Edgar, Peter A. Stockwell, Euan J. Rodger, Aniruddha Chatterjee, and Warren P. Tate. 2025. “Comparing DNA Methylation Landscapes in Peripheral Blood from Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID Patients.” International Journal of Molecular Sciences 26 (14): 6631. https://doi.org/10.3390/ijms26146631.

Petersen, Nicole, Andrea J Rapkin, Kyoji Okita, Kelly R Kinney, Toshihiko Mizuno, Mark A Mandelkern, and Edythe D London. 2021. “Striatal Dopamine D(2)-Type Receptor Availability and Peripheral 17β-Estradiol.” Molecular Psychiatry 26 (6): 2038–47. https://doi.org/10.1038/s41380-020-01000-1.

Petrasca, A, R Hambly, N Kearney, CM Smith, EK Pender, J Mac Mahon, AM O’Rourke, et al. 2023. “Metformin Has Anti-Inflammatory Effects and Induces Immunometabolic Reprogramming via Multiple Mechanisms in Hidradenitis Suppurativa.” British Journal of Dermatology 189 (6): 730–40. https://doi.org/10.1093/bjd/ljad305.

Petrov, Steliyan, Martina Bozhkova, Mariya Ivanovska, Teodora Kalfova, Dobrina Dudova, Ralitsa Nikolova, Kalina Vaseva, et al. 2025. “Comparable Immune Alterations and Inflammatory Signatures in ME/CFS and Long COVID.” Biomedicines 13 (12): 3001. https://doi.org/10.3390/biomedicines13123001.

Petter, Elisabeth, Carmen Scheibenbogen, Peter Linz, Christian Stehning, Klaus Wirth, Titus Kuehne, and Marcus Kelm. 2022. “Muscle Sodium Content in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Journal of Translational Medicine 20 (1): 580. https://doi.org/10.1186/s12967-022-03616-z.

Phetsouphanh, Chansavath, David R. Darley, Daniel B. Wilson, et al. 2022. “Immunological Dysfunction Persists for 8 Months Following Initial Mild-to-Moderate SARS-CoV-2 Infection.” Nature Immunology 23 (2): 210–16. https://doi.org/10.1038/s41590-021-01113-x.

Phetsouphanh, Chansavath, Brendan Jacka, Samantha Ballouz, Katherine J L Jackson, Daniel B Wilson, Bikash Manandhar, Vera Klemm, et al. 2024. “Improvement of Immune Dysregulation in Individuals with Long COVID at 24-Months Following SARS-CoV-2 Infection.” Nature Communications 15 (1): 3315. https://doi.org/10.1038/s41467-024-47720-8.

Pinggal, Elaine, Paul M. Dockree, Redmond G. O’Connell, Mark A. Bellgrove, and Thomas Andrillon. 2022. “Pharmacological Manipulations of Physiological Arousal and Sleep-Like Slow Waves Modulate Sustained Attention.” Journal of Neuroscience 42 (43): 8113–24. https://doi.org/10.1523/JNEUROSCI.0836-22.2022.

Pinggal, Elaine, James Jackson, Anneke Kusztor, David Chapman, Jennifer Windt, Sean P. A. Drummond, Tim J. Silk, Mark A. Bellgrove, and Thomas Andrillon. 2026. “Sleep-Like Slow Waves During Wakefulness Mediate Attention and Vigilance Difficulties in Adult Attention-Deficit/Hyperactivity Disorder.” Journal of Neuroscience 46 (15): e1694252025. https://doi.org/10.1523/JNEUROSCI.1694-25.2025.

Ponticelli, Claudio, Gabriella Moroni, and Francesco Reggiani. 2023. “Autophagy and Podocytopathy.” Nephrology Dialysis Transplantation 38 (9): 1931–39. https://doi.org/10.1093/ndt/gfad024.

Popov, G., D. Bashchobanov, and R. Andonova. 2026. “Tick-Borne Co-Infection in Lyme Disease: Clinical Impact, Diagnostic Challenges, and Therapeutic Perspectives.” Microorganisms 14 (2): 325. https://doi.org/10.3390/microorganisms14020325.

Porter, Nicole, and Leonard A. Jason. 2022. “Mindfulness Meditation Interventions for Long COVID: Biobehavioral Gene Expression and Neuroimmune Functioning.” Neuropsychiatric Disease and Treatment 18: 2599–2626. https://doi.org/10.2147/NDT.S379653.

Prens, LM, K Bouwman, LD Troelstra, EP Prens, BZ Alizadeh, and B Horváth. 2022. “New Insights in Hidradenitis Suppurativa from a Population-Based Dutch Cohort: Prevalence, Smoking Behaviour, Socioeconomic Status and Comorbidities.” British Journal of Dermatology 186 (5): 814–22. https://doi.org/10.1111/bjd.20954.

Pridgen, William L, and David Putrino. 2026. “Patient-Reported Improvements from Use of IMC-2 Alone and IMC-2 and Paxlovid in a Long COVID Cohort: A Case Series.” Frontiers in Immunology 16: 1698271. https://doi.org/10.3389/fimmu.2025.1698271.

Pucino, Valentina, Michelangelo Certo, Vinay Bulusu, Danilo Cucchi, Katriona Goldmann, Elena Pontarini, Robert Haas, Claudio Mauro, et al. 2019. “Lactate Buildup at the Site of Chronic Inflammation Promotes Disease by Inducing CD4+ T Cell Metabolic Rewiring.” Cell Metabolism 30 (6): 1055–1074.e8. https://doi.org/10.1016/j.cmet.2019.10.004.

Puijenbroek, Eugène P. van, Andrew Bate, Hubert G. M. Leufkens, Marie Lindquist, Roland Orre, and Antoine C. G. Egberts. 2002. “A Comparison of Measures of Disproportionality for Signal Detection in Spontaneous Reporting Systems for Adverse Drug Reactions.” Pharmacoepidemiology and Drug Safety 11 (1): 3–10. https://doi.org/10.1002/pds.668.

Pullinen, T., A. Mero, P. Huttunen, A. Pakarinen, and P. V. Komi. 2011. “Resistance Exercise-Induced Hormonal Response Under the Influence of Delayed Onset Muscle Soreness in Men and Boys.” Scandinavian Journal of Medicine & Science in Sports 21 (6): e184–94. https://doi.org/10.1111/j.1600-0838.2010.01238.x.

Punja, Salima, Dali Xu, Christopher H. Schmid, Lisa Hartling, Liana Urichuk, Jane C. Nikles, and Sunita Vohra. 2016. “N-of-1 Trials Can Be Aggregated to Generate Group Mean Treatment Effects: A Systematic Review and Meta-Analysis.” Journal of Clinical Epidemiology 76: 65–75. https://doi.org/10.1016/j.jclinepi.2016.03.026.

Purpura, Lawrence, Thomas Heisler, Steven Palmer, Jayesh Shah, Abigail Graham, Ga Young Seo, Antonia Sturiza, et al. 2026. “Overlapping Clinical Presentation of Long COVID and Postacute COVID-19 Vaccination Syndrome: Phenotypes, Severity, and Biomarkers.” Clinical Infectious Diseases 82 (5): e923–31. https://doi.org/10.1093/cid/ciaf624.

Qi-Lytle, H, J Sayers, and E J Wagner. 2023. “Current Review of the Function and Regulation of Tuberoinfundibular Dopamine Neurons.” International Journal of Molecular Sciences 25 (1): 110. https://doi.org/10.3390/ijms25010110.

Qin, Weiping, Li Sun, Jing Cao, Yuanzhen Peng, Lauren Collier, Yan Wu, Graham Creasey, et al. 2013. “The Central Nervous System (CNS)-Independent Anti-Bone-Resorptive Activity of Muscle Contraction and the Underlying Molecular and Cellular Signatures.” Journal of Biological Chemistry 288 (19): 13511–21. https://doi.org/10.1074/jbc.M113.454892.

Qu, Shen, Jinjing Wang, Xiaoqian Guan, Caijun Song, and Xiaoyun Wang. 2024. “Sleep Disturbance in Angelman Syndrome Patients.” Orphanet Journal of Rare Diseases 19 (1): 146. https://doi.org/10.1186/s13023-024-03154-5.

Raijmakers, R, M Roerink, S Keijmel, L Joosten, M Netea, J van der Meer, H Knoop, H Klein, C Bleeker-Rovers, and J Doorduin. 2021. “No Signs of Neuroinflammation in Women with Chronic Fatigue Syndrome or q Fever Fatigue Syndrome Using TSPO Ligand [(11)c]-PK11195.” Neurology Neuroimmunology and Neuroinflammation 9 (1): e1113. https://doi.org/10.1212/NXI.0000000000001113.

Raj, Satish R., Bonnie K. Black, Italo Biaggioni, and Paul A. Harris. 2005. “Acetylcholinesterase Inhibition Improves Tachycardia in Postural Tachycardia Syndrome.” Circulation 111 (21): 2734–40. https://doi.org/10.1161/CIRCULATIONAHA.104.497594.

Rangaraju, Vidhya, Nathaniel Calloway, and Timothy A. Ryan. 2014. “Activity-Driven Local ATP Synthesis Is Required for Synaptic Function.” Cell 156 (4): 825–35. https://doi.org/10.1016/j.cell.2013.12.042.

Rassoulpour, Asma, Hui-Qiu Wu, Sergi Ferré, and Robert Schwarcz. 2005. “Nanomolar Concentrations of Kynurenic Acid Reduce Extracellular Dopamine Levels in the Striatum.” Journal of Neurochemistry 93 (3): 762–65. https://doi.org/10.1111/j.1471-4159.2005.03134.x.

Ray, Urvi, Antje Schulze Selting, Roshan Priyarangana Perera, Zhiqi Yang, Vladislav Lysenkov, Siri Göpel, Michael Bitzer, et al. 2026. “Dysregulated NK-Cell Gene Expression Defines the Enduring Symptoms of Long COVID-19.” Frontiers in Immunology 17: 1720551. https://doi.org/10.3389/fimmu.2026.1720551.

Rayhan, R. U., and J. N. Baraniuk. 2021. “Submaximal Exercise Provokes Increased Activation of the Anterior Default Mode Network During the Resting State as a Biomarker of Postexertional Malaise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Frontiers in Neuroscience 15: 748426. https://doi.org/10.3389/fnins.2021.748426.

Razumovsky, A. Y., K. DeBusk, H. Calkins, S. Snader, K. E. Lucas, P. Vyas, D. F. Hanley, and P. C. Rowe. 2003. “Cerebral and Systemic Hemodynamics Changes During Upright Tilt in Chronic Fatigue Syndrome.” Journal of Neuroimaging 13 (1): 57–67.

Redfern, J. S., and M. A. Smith. 2026. “A Quarter-Century of Montelukast: Clinical Lessons for Adult and Pediatric Asthma and Allergic Rhinitis Care.” Current Medical Research and Opinion 42 (6): 1179–94. https://doi.org/10.1080/03007995.2026.2700973.

Rehman, A., A. Ihtesham, H. A. A. Raja, M. Ibrahim, M. A. Khalid, S. Chaudhry, S. Maqbool, R. G. Omarzai, et al. 2026. “Efficacy and Safety of Rupatadine in Allergic Rhinitis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.” Annals of Medicine and Surgery 88 (7): 4410–20. https://doi.org/10.1097/MS9.0000000000004930.

Reis, Gilmar, Eduardo Augusto dos Santos Moreira Silva, Daniela Carla Medeiros Silva, Lehana Thabane, Thiago Santiago Ferreira, Luiza Lanna França Reis, et al. 2026. “The Effect of Fluvoxamine and Metformin for Fatigue in Patients with Long COVID: An Adaptive Randomized Trial.” Annals of Internal Medicine. https://doi.org/10.7326/ANNALS-25-03959.

Rekeland, Ingrid G., Kari Sørland, Ove Bruland, Kristin Risa, Kine Alme, Olav Dahl, Karl J. Tronstad, Olav Mella, and Øystein Fluge. 2022. “Activity Monitoring and Patient-Reported Outcome Measures in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients.” PLoS One 17 (9): e0274472. https://doi.org/10.1371/journal.pone.0274472.

Reno, Alyssum M., Matt Green, Lauren G. Killen, Eric K. O’Neal, Kelly Pritchett, and Zella Hanson. 2022. “Effects of Magnesium Supplementation on Muscle Soreness and Performance.” Journal of Strength and Conditioning Research 36 (8): 2198–2203. https://doi.org/10.1519/JSC.0000000000003827.

Repantis, Dimitris, Peter Schlattmann, Oona Laisney, and Isabella Heuser. 2010. “Modafinil and Methylphenidate for Neuroenhancement in Healthy Individuals: A Systematic Review.” Pharmacological Research 62 (3): 187–206. https://doi.org/10.1016/j.phrs.2010.04.002.

Richardson, Gary S., Timothy A. Roehrs, Leon Rosenthal, and Gail Koshorek. 2002. “Tolerance to Daytime Sedative Effects of H1 Antihistamines.” Journal of Clinical Psychopharmacology 22 (5): 511–15. https://doi.org/10.1097/00004714-200210000-00012.

Richiardi, Lorenzo, Iacopo Baussano, Luisa Vizzini, Jeroen Douwes, Neil Pearce, and Franco Merletti. 2007. “Feasibility of Recruiting a Birth Cohort Through the Internet: The Experience of the NINFEA Cohort.” European Journal of Epidemiology 22 (12): 831–37. https://doi.org/10.1007/s10654-007-9194-2.

Rigolet, Muriel, Jérôme Aouizerate, Marie Couette, Nalini Ragunathan-Thangarajah, Mouna Aoun-Sebaiti, Romain K Gherardi, Josette Cadusseau, and François-Jérôme Authier. 2014. “Clinical Features in Patients with Long-Lasting Macrophages Myofasciitis.” Frontiers in Neurology 5: 230. https://doi.org/10.3389/fneur.2014.00230.

Ring, HC, P Theut Riis, K Zarchi, IM Miller, DM Saunte, and GB Jemec. 2017. “Prodromal Symptoms in Hidradenitis Suppurativa.” Clinical and Experimental Dermatology 42 (3): 261–65. https://doi.org/10.1111/ced.13025.

Rivera, Jose Carlos, Boutheyna Noueihed, Ankush Madaan, Isabelle Lahaie, Jie Pan, Jaques Belik, and Sylvain Chemtob. 2017. “Tetrahydrobiopterin (BH4) Deficiency Is Associated with Augmented Inflammation and Microvascular Degeneration in the Retina.” Journal of Neuroinflammation 14 (1): 181. https://doi.org/10.1186/s12974-017-0955-x.

Rizor, Elizabeth J, Viktoriya Babenko, Neil M Dundon, Renee Beverly-Aylwin, Alexandra Stump, Margaret Hayes, Lauren Herschenfeld-Catalan, Emily G Jacobs, and Scott T Grafton. 2024. “Menstrual Cycle-Driven Hormone Concentrations Co-Fluctuate with White and Gray Matter Architecture Changes Across the Whole Brain.” Human Brain Mapping 45 (11): e26785. https://doi.org/10.1002/hbm.26785.

Roberts, Carl A., Andrew Jones, Harry Sumnall, Suzanne H. Gage, and Catharine Montgomery. 2020. “How Effective Are Pharmaceuticals for Cognitive Enhancement in Healthy Adults? A Series of Meta-Analyses of Cognitive Performance During Acute Administration of Modafinil, Methylphenidate and D-Amphetamine.” European Neuropsychopharmacology 38: 40–62. https://doi.org/10.1016/j.euroneuro.2020.07.002.

Roberts, David D., Sukhbir Kaur, and Jeffrey S. Isenberg. 2017. “Regulation of Cellular Redox Signaling by Matricellular Proteins in Vascular Biology, Immunology, and Cancer.” Antioxidants & Redox Signaling 27 (12): 874–911. https://doi.org/10.1089/ars.2017.7140.

Rogers, Denise C., Antonia J. Dittner, Katharine A. Rimes, and Trudie Chalder. 2017. “Fatigue in an Adult Attention Deficit Hyperactivity Disorder Population: A Trans-Diagnostic Approach.” British Journal of Clinical Psychology 56 (1): 33–52. https://doi.org/10.1111/bjc.12119.

Rogers, Natasha M., Maryam Sharifi-Sanjani, Gábor Csányi, Patrick J. Pagano, and Jeffrey S. Isenberg. 2014. “Thrombospondin-1 and CD47 Regulation of Cardiac, Pulmonary and Vascular Responses in Health and Disease.” Matrix Biology 37: 92–101. https://doi.org/10.1016/j.matbio.2014.01.002.

Rolvien, Tim, and Michael Amling. 2022. “Disuse Osteoporosis: Clinical and Mechanistic Insights.” Calcified Tissue International 110 (5): 592–604. https://doi.org/10.1007/s00223-021-00836-1.

Rothhammer, Veit, and Francisco J. Quintana. 2019. “The Aryl Hydrocarbon Receptor: An Environmental Sensor Integrating Immune Responses in Health and Disease.” Nature Reviews Immunology 19 (3): 184–97. https://doi.org/10.1038/s41577-019-0125-8.

Rubia, Katya, Analucia A. Alegria, Ana I. Cubillo, Anna B. Smith, Michael J. Brammer, and Joaquim Radua. 2014. “Effects of Stimulants on Brain Function in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis.” Biological Psychiatry 76 (8): 616–28. https://doi.org/10.1016/j.biopsych.2013.10.016.

Ruiter, Anika M, Jan J G M Verschuuren, and Martijn R Tannemaat. 2020. “Fatigue in Patients with Myasthenia Gravis. A Systematic Review of the Literature.” Neuromuscular Disorders 30 (8): 631–39. https://doi.org/10.1016/j.nmd.2020.06.010.

Ryback, A. A., C. B. Hillier, C. M. Loureiro, C. P. Ponting, and C. F. Dalton. 2026. “Indistinguishable Mitochondrial Phenotypes After Exposure of Healthy Myoblasts to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome or Control Serum.” PLoS One 21 (2): e0341334. https://doi.org/10.1371/journal.pone.0341334.

Sakurai, T., Y. C. Saito, and M. Yanagisawa. 2021. “Interaction Between Orexin Neurons and Monoaminergic Systems.” Frontiers of Neurology and Neuroscience 45: 11–21. https://doi.org/10.1159/000514955.

Salih, L Y, N L A Dumaire, C Gieré, E Vest, H Alkhateeb, C P Moritz, Y Tholance, et al. 2026. “Pathogenic Role of FGFR3 Autoantibodies in Small Fiber Neuropathy.” Advanced Science 13 (22): e11413. https://doi.org/10.1002/advs.202511413.

Samms, Gemma Louise, and Chris P Ponting. 2025. “Unequal Access to Diagnosis of Myalgic Encephalomyelitis in England.” BMC Public Health 25 (1): 1417. https://doi.org/10.1186/s12889-025-22603-9.

Samuel, Joyce P., Cynthia S. Bell, Joshua A. Samuels, Charanya Rajan, Ashley K. Walton, Charles Green, and Jon E. Tyson. 2023. “N-of-1 Trials Vs. Usual Care in Children with Hypertension: A Pilot Randomized Clinical Trial.” American Journal of Hypertension 36 (2): 126–32. https://doi.org/10.1093/ajh/hpac117.

Samuel, Joyce P., Jon E. Tyson, Charles Green, Cynthia S. Bell, Claudia Pedroza, Donald Molony, and Joshua Samuels. 2019. “Treating Hypertension in Children with n-of-1 Trials.” Pediatrics 143 (4): e20181818. https://doi.org/10.1542/peds.2018-1818.

Sanhueza, Sergio, Camilo Cabrera, Romina Quiroga, et al. 2026. “De Novo COVID-19-Associated Insulin Resistance Drives Dysregulated Neutrophil Extracellular Trap Formation (NETosis) Four Months After Infection.” Frontiers in Immunology 17: 1787799. https://doi.org/10.3389/fimmu.2026.1787799.

Sarbassov, Dos D, Siraj M Ali, Shomit Sengupta, Joon-Ho Sheen, Peggy P Hsu, Alex F Bagley, Andrew L Markhard, and David M Sabatini. 2006. “Prolonged Rapamycin Treatment Inhibits mTORC2 Assembly and Akt/PKB.” Molecular Cell 22 (2): 159–68. https://doi.org/10.1016/j.molcel.2006.03.029.

Sarkar, Chinmoy, Basabi Basu, Debanjan Chakroborty, Partha S. Dasgupta, and Sujit Basu. 2010. “The Immunoregulatory Role of Dopamine: An Update.” Brain, Behavior, and Immunity 24 (4): 525–28. https://doi.org/10.1016/j.bbi.2009.10.015.

Sasson, Noah J., Daniel J. Faso, John Nugent, Sarah Lovell, Daniel P. Kennedy, and Ruth B. Grossman. 2017. “Neurotypical Peers Are Less Willing to Interact with Those with Autism Based on Thin Slice Judgments.” Scientific Reports 7: 40700. https://doi.org/10.1038/srep40700.

Schacterle, Richard S, and Anthony L Komaroff. 2004. “A Comparison of Pregnancies That Occur Before and After the Onset of Chronic Fatigue Syndrome.” Archives of Internal Medicine 164 (4): 401–4. https://doi.org/10.1001/archinte.164.4.401.

Schiweck, N., K. Langer, A. Maier, D. Vilser, and J. Spiegler. 2026. “Systematic Literature Review: Treatment of Postural Orthostatic Tachycardia Syndrome (POTS).” Clinical Autonomic Research 36 (1): 3–16. https://doi.org/10.1007/s10286-025-01172-2.

Schlömer, E., E. Stein, C. Kedor, and R. Rust. 2025. “Pyridostigmine Improves Hand Grip Strength in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Frontiers in Neuroscience 19: 1637838. https://doi.org/10.3389/fnins.2025.1637838.

Schmid, Christopher, and Jincao Yang. 2022. “Bayesian Models for N-of-1 Trials.” Harvard Data Science Review 2022 (SI3). https://doi.org/10.1162/99608f92.3f1772ce.

Schmidt, Stefan, Paul Grossman, Barbara Schwarzer, Susanne Jena, Johannes Naumann, and Harald Walach. 2011. “Treating Fibromyalgia with Mindfulness-Based Stress Reduction: Results from a 3-Armed Randomized Controlled Trial.” Pain 152 (2): 361–69. https://doi.org/10.1016/j.pain.2010.10.043.

Schreiner, Philipp, Thomas Harrer, Carmen Scheibenbogen, Stephanie Lamer, Andreas Schlosser, Robert K. Naviaux, and Bhupesh K. Prusty. 2020. “Human Herpesvirus-6 Reactivation, Mitochondrial Fragmentation, and the Coordination of Antiviral and Metabolic Phenotypes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Immunohorizons 4 (4): 201–15. https://doi.org/10.4049/immunohorizons.2000006.

Schwarcz, Robert, John P. Bruno, Paul J. Muchowski, and Hui-Qiu Wu. 2012. “Kynurenines in the Mammalian Brain: When Physiology Meets Pathology.” Nature Reviews Neuroscience 13 (7): 465–77. https://doi.org/10.1038/nrn3257.

Sellers, Katherine, Pooja Raval, and Deepak P Srivastava. 2015. “Molecular Signature of Rapid Estrogen Regulation of Synaptic Connectivity and Cognition.” Frontiers in Neuroendocrinology 36: 72–89. https://doi.org/10.1016/j.yfrne.2014.08.001.

Semmler, Amelie, Anna Katharina Mundorf, Anna Sabrina Kuechler, Karin Schulze-Bosse, Harald Heidecke, Kai Schulze-Forster, Matthias Schott, Markus Uhrberg, et al. 2023. “Chronic Fatigue and Dysautonomia Following COVID-19 Vaccination Is Distinguished from Normal Vaccination Response by Altered Blood Markers.” Vaccines 11 (11): 1642. https://doi.org/10.3390/vaccines11111642.

Senard, Jean-Michel, and Philippe Rouet. 2006. “Dopamine Beta-Hydroxylase Deficiency.” Orphanet Journal of Rare Diseases 1: 7. https://doi.org/10.1186/1750-1172-1-7.

Seng, Jun Jie Benjamin, Priya Oka, and Ngiap Chuan Tan. 2026. “Rebound Pruritus and Urticaria After Discontinuation of Chronic Antihistamine Use — a Scoping Review.” Clinical and Experimental Allergy. https://doi.org/10.1111/cea.70291.

Shadani, Sarah, Kyna Conn, Zane B Andrews, and Claire J Foldi. 2024. “Potential Differences in Psychedelic Actions Based on Biological Sex.” Endocrinology 165 (8): bqae083. https://doi.org/10.1210/endocr/bqae083.

Shahn, Zach, Paige Spear, Hong Lu, Shan Jiang, Shuang Zhang, Nikhil Deshmukh, Stanley Xu, Kenney Ng, Roy Welsch, and Stan Finkelstein. 2022. “Systematically Exploring Repurposing Effects of Antihypertensives.” Pharmacoepidemiology and Drug Safety 31 (9): 944–52. https://doi.org/10.1002/pds.5491.

Shane, Elizabeth, David Burr, Bo Abrahamsen, Robert A Adler, Thomas D Brown, Angela M Cheung, Felicia Cosman, et al. 2014. “Atypical Subtrochanteric and Diaphyseal Femoral Fractures: Second Report of a Task Force of the American Society for Bone and Mineral Research.” Journal of Bone and Mineral Research 29 (1): 1–23. https://doi.org/10.1002/jbmr.1998.

Shankar, Vishnu, Julie Wilhelmy, Ellis J. Curtis, Basil Michael, Layla Cervantes, Vamsee A. Mallajosyula, Ronald W. Davis, et al. 2025. “Oxidative Stress Is a Shared Characteristic of ME/CFS and Long COVID.” Proceedings of the National Academy of Sciences U.S.A. 122 (28): e2426564122. https://doi.org/10.1073/pnas.2426564122.

Shapiro, Jon S. 2009. “Does Varicella-Zoster Virus Infection of the Peripheral Ganglia Cause Chronic Fatigue Syndrome?” Medical Hypotheses 73 (5): 728–34. https://doi.org/10.1016/j.mehy.2009.04.043.

Sharma, A, F Oyebode, M J Kendall, and P W Jones. 2001. “Recovery from Chronic Fatigue Syndrome Associated with Changes in Neuroendocrine Function.” Journal of the Royal Society of Medicine 94 (1): 26–27. https://doi.org/10.1177/014107680109400107.

Sharpe, M, A Clements, K Hawton, A H Young, P Sargent, and P J Cowen. 1996. “Increased Prolactin Response to Buspirone in Chronic Fatigue Syndrome.” Journal of Affective Disorders 41 (1): 71–76. https://doi.org/10.1016/0165-0327(96)00075-4.

Shimaoka, Haruka, Tomoya Kawaguchi, Kana Morikawa, Yuuki Sano, Kiyotada Naitou, Hiroyuki Nakamori, Takahiko Shiina, and Yasutake Shimizu. 2018. “Induction of Hibernation-Like Hypothermia by Central Activation of the A1 Adenosine Receptor in a Non-Hibernator, the Rat.” Journal of Physiological Sciences 68 (4): 425–30. https://doi.org/10.1007/s12576-017-0543-y.

Shkalim Zemer, Vered, Iris Manor, Abraham Weizman, Herman A. Cohen, Moshe Hoshen, Nehama M. Caspi, Shlomo Cohen, Stephen V. Faraone, and Nily Shahar. 2024. “The Influence of COVID-19 on Attention-Deficit/Hyperactivity Disorder Diagnosis and Treatment Rates Across Age, Gender, and Socioeconomic Status: A 20-Year National Cohort Study.” Psychiatry Research 339: 116077. https://doi.org/10.1016/j.psychres.2024.116077.

Sibley, Margaret H, Luis Augusto Rohde, James M Swanson, Lily T Hechtman, Brooke S G Molina, John T Mitchell, L Eugene Arnold, et al. 2018. “Late-Onset ADHD Reconsidered with Comprehensive Repeated Assessments Between Ages 10 and 25.” American Journal of Psychiatry 175 (2): 140–49. https://doi.org/10.1176/appi.ajp.2017.17030298.

Simchi, Liron, Pratiksha K. Gupta, Yaron Feuermann, and Hanoch Kaphzan. 2023. “Elevated ROS Levels During the Early Development of Angelman Syndrome Alter the Apoptotic Capacity of the Developing Neural Precursor Cells.” Molecular Psychiatry 28 (6): 2382–97. https://doi.org/10.1038/s41380-023-02038-7.

Simmering, Jacob E., Linnea A. Polgreen, and Philip M. Polgreen. 2014. “Web Search Query Volume as a Measure of Pharmaceutical Utilization and Changes in Prescribing Patterns.” Research in Social and Administrative Pharmacy 10 (6): 896–903. https://doi.org/10.1016/j.sapharm.2014.01.003.

Skiba, Meredith A., and Andrew C. Kruse. 2021. “Autoantibodies as Endogenous Modulators of GPCR Signaling.” Trends in Pharmacological Sciences 42 (3): 135–50. https://doi.org/10.1016/j.tips.2020.11.013.

Slack, Emma, Katrina Anne Pears, Judith Rankin, Julia L Newton, and Mark Pearce. 2023. “Identifying, Synthesising and Appraising Existing Evidence Relating to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Pregnancy: A Mixed-Methods Systematic Review.” BMJ Open 13 (10): e070366. https://doi.org/10.1136/bmjopen-2022-070366.

Smolensky, Michael H., Björn Lemmer, and Alain E. Reinberg. 2007. “Chronobiology and Chronotherapy of Allergic Rhinitis and Bronchial Asthma.” Advanced Drug Delivery Reviews 59 (9–10): 852–82. https://doi.org/10.1016/j.addr.2007.08.016.

Snyder, Jason S., Amelie Soumier, Michelle Brewer, James Pickel, and Heather A. Cameron. 2011. “Adult Hippocampal Neurogenesis Buffers Stress Responses and Depressive Behaviour.” Nature 476 (7361): 458–61. https://doi.org/10.1038/nature10287.

Sonnweber, Thomas, Philipp Grubwieser, Sabina Sahanic, Anna K. Böhm, Alex Pizzini, Anna Luger, Christoph Schwabl, et al. 2022. “The Impact of Iron Dyshomeostasis and Anaemia on Long-Term Pulmonary Recovery and Persisting Symptom Burden After COVID-19: A Prospective Observational Cohort Study.” Metabolites 12 (6): 546. https://doi.org/10.3390/metabo12060546.

Sorrells, Shawn F., Mercedes F. Paredes, Arantxa Cebrian-Silla, Kadellyn Sandoval, Dashi Qi, Kevin W. Kelley, David James, et al. 2018. “Human Hippocampal Neurogenesis Drops Sharply in Children to Undetectable Levels in Adults.” Nature 555 (7696): 377–81. https://doi.org/10.1038/nature25975.

Sorrells, Shawn F., Mercedes F. Paredes, Zicong Zhang, Gina Kang, Olaya Pastor-Alonso, Sara Biagiotti, Chelsea E. Page, et al. 2021. “Positive Controls in Adults and Children Support That Very Few, If Any, New Neurons Are Born in the Adult Human Hippocampus.” Journal of Neuroscience 41 (12): 2554–65. https://doi.org/10.1523/JNEUROSCI.0676-20.2020.

Souma, Bernard, Wesam Elremaly, Marie-Yvonne Akoume, Mohamed Elbakry, Christian Godbout, and Alain Moreau. 2026. “Irisin Signaling Resistance in Myalgic Encephalomyelitis: A Proposed Mechanistic Framework for Post-Exertional Malaise Involving the TSP-1–HSP90α–Αvβ5 Axis.” International Journal of Molecular Sciences 27 (11): 4770. https://doi.org/10.3390/ijms27114770.

Spanoghe, Martin, Tomaso Antonacci, Nicole Schneider, and Thomas H. J. Molmans. 2026. “Viewpoint: Linking Long Covid and AD(H)D Through Neuroimmune Dysfunction: A Translational Framework Proposal for Precision Medicine.” Brain, Behavior, and Immunity 131: 106181. https://doi.org/10.1016/j.bbi.2025.106181.

Spillane, J., J. Trip, G. Drost, C. G. Faber, M. G. Hanna, S. J. Nevitt, and V. Vivekanandam. 2025. “Drug Treatment for Myotonia.” Cochrane Database of Systematic Reviews, no. 4: CD004762. https://doi.org/10.1002/14651858.CD004762.pub3.

Stallion, Chloe, Tamkeen Khan, Christian Guerrero, Stavros Tsipas, and Gregory Wozniak. 2026. “Social Media Discussions of Anti-Diabetic Drugs and the Popularity of GLP-1 Therapies: Content Analysis.” JMIR Diabetes 11: e87590. https://doi.org/10.2196/87590.

Stewart, J., A. Weldon, N. Arlievsky, K. Li, and J. Munoz. 1998. “Neurally Mediated Hypotension and Autonomic Dysfunction Measured by Heart Rate Variability During Head-up Tilt Testing in Children with Chronic Fatigue Syndrome.” Clinical Autonomic Research 8 (4): 221–30. https://doi.org/10.1007/BF02267785.

Stickford, Abigail S, Tiffany B VanGundy, Benjamin D Levine, and Qi Fu. 2015. “Menstrual Cycle Phase Does Not Affect Sympathetic Neural Activity in Women with Postural Orthostatic Tachycardia Syndrome.” The Journal of Physiology 593 (9): 2131–43. https://doi.org/10.1113/JP270088.

Strawbridge, Rebecca, Maria Laura Sartor, Fiona Scott, and Anthony J Cleare. 2019. “Inflammatory Proteins Are Altered in Chronic Fatigue Syndrome–a Systematic Review and Meta-Analysis.” Neuroscience and Biobehavioral Reviews 107: 69–83. https://doi.org/10.1016/j.neubiorev.2019.08.011.

Strayer, D. R., D. Young, and W. M. Mitchell. 2020. “Effect of Disease Duration in a Randomized Phase III Trial of Rintatolimod, an Immune Modulator for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” PLoS ONE 15 (10): e0240403. https://doi.org/10.1371/journal.pone.0240403.

Stussman, Barbara, Nicolas Camarillo, Gina McCrossin, Mary Stockman, Gina Norato, Caryn S. Vetter, Anamaria Ferrufino, et al. 2025. “Post-Exertional Malaise in Long COVID: Subjective Reporting Versus Objective Assessment.” Frontiers in Neurology 16: 1534352. https://doi.org/10.3389/fneur.2025.1534352.

Su, Hui, Wei Fan, Pinar E. Coskun, Jouni Vesa, Jeffrey A. Gold, Yong-hui Jiang, Prasanth Potluri, Douglas C. Wallace, Jane Gibson, and Virginia E. Kimonis. 2011. “Mitochondrial Dysfunction in CA1 Hippocampal Neurons of the UBE3A Deficient Mouse Model for Angelman Syndrome.” Neuroscience Letters 487 (2): 129–33. https://doi.org/10.1016/j.neulet.2009.06.079.

Sun, Haoyu, Edward J Calabrese, Zhifen Lin, Baoling Lian, and Xiaoxian Zhang. 2020. “Similarities Between the Yin/Yang Doctrine and Hormesis in Toxicology and Pharmacology.” Trends in Pharmacological Sciences 41 (8): 544–56. https://doi.org/10.1016/j.tips.2020.05.004.

Sun, Haoyu, Edward J Calabrese, Min Zheng, Dali Wang, Yong Pan, Zhifen Lin, and Ying Liu. 2018. “A Swinging Seesaw as a Novel Model Mechanism for Time-Dependent Hormesis Under Dose-Dependent Stimulatory and Inhibitory Effects: A Case Study on the Toxicity of Antibacterial Chemicals to Aliivibrio Fischeri.” Chemosphere 205: 15–23. https://doi.org/10.1016/j.chemosphere.2018.04.043.

Sunami, Yutaro, Kazushi Sugaya, and Kazushi Takahashi. 2025. “G Protein-Coupled Receptors Related to Autoimmunity in Postural Orthostatic Tachycardia Syndrome.” Immunological Medicine 48 (1): 1–11. https://doi.org/10.1080/25785826.2025.2457613.

Sunnquist, Madison, Leonard A Jason, Abigail Brown, Meredyth Evans, and Andrew Berman. 2015. “Complications in Operationalizing Lifelong Fatigue as an Exclusionary Criterion.” Journal of Prevention & Intervention in the Community 43 (1): 42–53. https://doi.org/10.1080/10852352.2014.973238.

Swerdlow, Russell H., Jeffrey M. Burns, and Shaharyar M. Khan. 2014. “The Alzheimer’s Disease Mitochondrial Cascade Hypothesis: Progress and Perspectives.” Biochimica Et Biophysica Acta (BBA) - Molecular Basis of Disease 1842 (8): 1219–31. https://doi.org/10.1016/j.bbadis.2013.09.010.

Tanabe, Yoko, Naoto Fujii, and Katsuhiko Suzuki. 2022. “Dietary Supplementation for Attenuating Exercise-Induced Muscle Damage and Delayed-Onset Muscle Soreness in Humans.” Nutrients 14 (1): 70. https://doi.org/10.3390/nu14010070.

Tanaka, Masaru, Ákos Szabó, Eleonóra Spekker, Helga Polyák, Fanni Tóth, and László Vécsei. 2022. “Mitochondrial Impairment: A Common Motif in Neuropsychiatric Presentation? The Link to the Tryptophan–Kynurenine Metabolic System.” Cells 11 (16): 2607. https://doi.org/10.3390/cells11162607.

Tanaka, S., H. Tanaka, and S. Abe. 2003. “High Dose of Inhaled Fluticasone Reduces High Levels of Urinary Leukotriene E4 in the Early Morning in Mild and Moderate Nocturnal Asthma.” Chest 124 (5): 1768–73. https://doi.org/10.1378/chest.124.5.1768.

Tang, Hong, Ting Loon Teo Chew, and Warren Fong. 2025. “Prevalence and Factors Associated with Fatigue in Patients with Psoriatic Arthritis: A Systematic Review and Meta-Analysis.” BMC Rheumatology 9 (1): 44. https://doi.org/10.1186/s41927-025-00498-8.

Tang, Hong, Bernard S M Singh, and Warren Fong. 2023. “Prevalence and Factors Associated with Fatigue in Patients with Axial Spondyloarthritis: A Systematic Review and Meta-Analysis.” Rheumatology Advances in Practice 7 (3): rkad084. https://doi.org/10.1093/rap/rkad084.

Tang, Qijun, Elizabeth Godschall, Charles D. Brennan, Qi Zhang, Ruei-Jen Abraham-Fan, Sydney P. Williams, Taha Bugra Gungul, et al. 2023. “Leptin Receptor Neurons in the Dorsomedial Hypothalamus Input to the Circadian Feeding Network.” Science Advances 9 (34): eadh9570. https://doi.org/10.1126/sciadv.adh9570.

Taylor, Alexander D, and Kenneth G Saag. 2019. “Anabolics in the Management of Glucocorticoid-Induced Osteoporosis: An Evidence-Based Review of Long-Term Safety, Efficacy and Place in Therapy.” Core Evidence 14: 41–50. https://doi.org/10.2147/CE.S172820.

Teitelbaum, Jacob E, Clarence Johnson, and John St Cyr. 2006. “The Use of D-ribose in Chronic Fatigue Syndrome and Fibromyalgia: A Pilot Study.” Journal of Alternative and Complementary Medicine 12 (9): 857–62. https://doi.org/10.1089/acm.2006.12.857.

Terman, Julia M, Jessica M Awsumb, Joseph Cotler, and Leonard A Jason. 2020. “Confirmatory Factor Analysis of a Myalgic Encephalomyelitis and Chronic Fatigue Syndrome Stigma Scale.” Journal of Health Psychology 25 (14): 2352–61. https://doi.org/10.1177/1359105318796906.

Terman, Julia M, Joseph Cotler, and Leonard A Jason. 2019. “How Psychiatric Referrals Influence Stigmatization in Patients with Myalgic Encephalomyelitis and Chronic Fatigue Syndrome: An Examination of American and British Models.” Community Psychology in Global Perspective 5 (2): 19–29. https://doi.org/10.1285/i24212113v5i2p19.

Thapaliya, Kiran, Sonya Marshall-Gradisnik, James Baraniuk, Natalie Eaton-Fitch, and Leighton Barnden. 2022. “Brainstem Neuroimaging Reveals Structural Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Frontiers in Neuroscience 16: 828014. https://doi.org/10.3389/fnins.2022.828014.

Thapaliya, Kiran, Sonya Marshall-Gradisnik, Markus Barth, Natalie Eaton-Fitch, and Leighton Barnden. 2023. “Brainstem Volume Changes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID Patients.” Frontiers in Neuroscience 17: 1125208. https://doi.org/10.3389/fnins.2023.1125208.

Thapaliya, Kiran, Sonya Marshall-Gradisnik, Maira Inderyas, and Leighton Barnden. 2025. “Altered Brain Tissue Microstructure and Neurochemical Profiles in Long COVID and Recovered COVID-19 Individuals: A Multimodal MRI Study.” Brain, Behavior, & Immunity – Health 50: 101142. https://doi.org/10.1016/j.bbih.2025.101142.

———. 2026. “Disrupted Glymphatic Function and Its Relationship with Sleep and Cognitive Impairment in ME/CFS Assessed via DTI-ALPS.” Frontiers in Neuroscience 20: 1875420. https://doi.org/10.3389/fnins.2026.1875420.

Thapaliya, Kiran, Sonya Marshall-Gradisnik, Donald Staines, and Leighton Barnden. 2021. “Diffusion Tensor Imaging Reveals Neuronal Microstructural Changes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” European Journal of Neuroscience 54 (6): 6214–28. https://doi.org/10.1111/ejn.15413.

Thirugnanam, Siva, Alison R. Van Zandt, Alexandra B. McNally, Victoria A. Hart, Isabelle Berthelot, Cecily C. Midkiff, Lara A. Doyle-Meyers, et al. 2026. “Dietary Indoles Influence the AHR-RORgt Axis and Mucosal Immune Homeostasis in ART-Treated SIV Infection.” JCI Insight 11 (10): e201258. https://doi.org/10.1172/jci.insight.201258.

Tholance, Y, C P Moritz, C Rosier, K Ferraud, F Lassablière, E Reynaud-Federspiel, M C Jr França, et al. 2020. “Clinical Characterisation of Sensory Neuropathy with Anti-FGFR3 Autoantibodies.” Journal of Neurology, Neurosurgery, and Psychiatry 91 (1): 49–57. https://doi.org/10.1136/jnnp-2019-321849.

Thomas, Natalie, Caroline Gurvich, Katherine Huang, Paul R Gooley, and Christopher W Armstrong. 2022. “The Underlying Sex Differences in Neuroendocrine Adaptations Relevant to Myalgic Encephalomyelitis Chronic Fatigue Syndrome.” Frontiers in Neuroendocrinology 66: 100995. https://doi.org/10.1016/j.yfrne.2022.100995.

Thomas, Natalie, Katherine Huang, Elena K Schneider-Futschik, Beth Pollack, Michal C Tal, David Fineberg, X Wang, et al. 2026. “Systems Neuroendocrinology in ME/CFS and Long COVID: A Chronobiological Framework for Hormone-Based Research.” Frontiers in Neuroendocrinology 82: 101268. https://doi.org/10.1016/j.yfrne.2026.101268.

Thornton, Emily J., Lawrence D. Hayes, Daniel S. Goodwin, Nicholas Sculthorpe, Yeliz Prior, and Nilihan E. M. Sanal-Hayes. 2025. “Managing Energy, and Shaping Care: Insights from Adults with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Through Co-Production Workshops.” American Journal of Medicine 138 (6): 1001–9. https://doi.org/10.1016/j.amjmed.2025.02.008.

Titlestad, Ingrid, Leiv Otto Watne, Gideon A Caplan, Adrian McCann, Per Magne Ueland, Bjorn Erik Neerland, Marius Myrstad, et al. 2024. “Impaired Glucose Utilization in the Brain of Patients with Delirium Following Hip Fracture.” Brain 147 (1): 215–23. https://doi.org/10.1093/brain/awad296.

Tomas, C. et al. 2017. “Cellular Bioenergetics Is Impaired in Patients with Chronic Fatigue Syndrome.” PLoS One 12 (10): e0186802. https://doi.org/10.1371/journal.pone.0186802.

Torbergsen, Torberg, Karin Jurkat-Rott, Erik V. Stålberg, Sissel Løseth, Ase Hødneø, and Frank Lehmann-Horn. 2015. “Painful Cramps and Giant Myotonic Discharges in a Family with the Nav1.4-G1306A Mutation.” Muscle & Nerve 52 (4): 680–83. https://doi.org/10.1002/mus.24672.

Torrelli-Diljohn, Alex, Bhargavi Kulkarni, and Dario A. Vitturi. 2026. “Cell Signaling by Tryptophan Catabolism.” Biochemistry 65 (9): 1366–94. https://doi.org/10.1021/acs.biochem.6c00179.

Trapp, Bruce D., and Peter K. Stys. 2009. “Virtual Hypoxia and Chronic Necrosis of Demyelinated Axons in Multiple Sclerosis.” Lancet Neurology 8 (3): 280–91. https://doi.org/10.1016/S1474-4422(09)70043-2.

Trevino, J A, and P Novak. 2021. “TS-HDS and FGFR3 Antibodies in Small Fiber Neuropathy and Dysautonomia.” Muscle & Nerve 64 (1): 70–76. https://doi.org/10.1002/mus.27245.

Tsilioni, Irene, Benjamin Natelson, and Theoharis C. Theoharides. 2022. “Exosome-Associated Mitochondrial DNA from Patients with ME/CFS Stimulates Human Microglia to Release IL-1$\beta$.” European Journal of Neuroscience 56 (10): 5784–94. https://doi.org/10.1111/ejn.15828.

Tsuchida, T. et al. 2022. “Relationship Between Changes in Symptoms and Antibody Titers After a Single Vaccination in Patients with Long COVID.” Journal of Medical Virology 94 (7): 3416–20. https://doi.org/10.1002/jmv.27725.

Tung, R., A. Kagey-Sobotka, M. Plaut, and Lawrence M. Lichtenstein. 1982. “H2 Antihistamines Augment Antigen-Induced Histamine Release from Human Basophils in Vitro.” Journal of Immunology 129 (5): 2113–15.

Tuomaala, J, M Saraste, E Smith, M Kuusi, E Westerberg, E Honkonen, R Kargar, et al. 2026. “Association Between Post-COVID-19 Neuropsychiatric Symptoms and Persistent Glial Activation in the Limbic System: A TSPO PET Study.” Journal of Neurology 273 (5): 298. https://doi.org/10.1007/s00415-026-13842-w.

Tzeng, Nian-Sheng, Chi-Hsiang Chung, Fu-Huang Lin, Chin-Bin Yeh, San-Yuan Huang, Ru-Band Lu, Hsin-An Chang, et al. 2019. “Risk of Dementia in Adults with ADHD: A Nationwide, Population-Based Cohort Study in Taiwan.” Journal of Attention Disorders 23 (9): 995–1006. https://doi.org/10.1177/1087054717714057.

Ueland, T, RJ Cox, AE Michelsen, EB Fjelltveit, K Otterdal, T Dahl, F Zhou, et al. 2025. “Markers of T Cell Activation and Exhaustion in Plasma Are Associated with Persistent Symptoms up to 18 Months Following Mild SARS-CoV-2 Infection.” Frontiers in Immunology 16: 1578208. https://doi.org/10.3389/fimmu.2025.1578208.

Ugawa, Shinya, Takashi Ueda, Yusuke Ishida, Makoto Nishigaki, Yasuhiro Shibata, and Shoichi Shimada. 2002. “Amiloride-Blockable Acid-Sensing Ion Channels Are Leading Acid Sensors Expressed in Human Nociceptors.” Journal of Clinical Investigation 110 (8): 1185–90. https://doi.org/10.1172/JCI15709.

Unger, Elizabeth R., Jin-Mann S. Lin, Lauren E. Wisk, Huihui Yu, Michelle L’Hommedieu, Helen Lavretsky, Juan Carlos C. Montoy, et al. 2024. “Myalgic Encephalomyelitis/Chronic Fatigue Syndrome After SARS-CoV-2 Infection.” JAMA Network Open 7 (7): e2423555. https://doi.org/10.1001/jamanetworkopen.2024.23555.

Vaes, Anouk W., Maarten Van Herck, Qichen Deng, Jeannet M. Delbressine, Leonard A. Jason, and Martijn A. Spruit. 2023. “Symptom-Based Clusters in People with ME/CFS: An Illustration of Clinical Variety in a Cross-Sectional Cohort.” Journal of Translational Medicine 21 (1): 112. https://doi.org/10.1186/s12967-023-03946-6.

Van Campenhout, Jente, Yens Buntinx, Huiyu Y. Xiong, Arne Wyns, Andrea Polli, Jo Nijs, Joeri L. Aerts, and Toon Laeremans. 2025. “Unravelling the Connection Between Energy Metabolism and Immune Senescence/Exhaustion in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Biomolecules 15 (3): 357. https://doi.org/10.3390/biom15030357.

Van Der Gucht, Axel, Mouna Aoun Sebaiti, Eric Guedj, Jérôme Aouizerate, Sarah Yara, Romain K Gherardi, Evangelia Evangelista, et al. 2017. “Brain (^18f)-FDG PET Metabolic Abnormalities in Patients with Long-Lasting Macrophages Myofascitis.” Journal of Nuclear Medicine 58 (3): 492–98. https://doi.org/10.2967/jnumed.114.151878.

Van Der Gucht, Axel, Mouna Aoun Sebaiti, Emmanuel Itti, Jérôme Aouizerate, Evangelia Evangelista, Julia Chalaye, Romain K Gherardi, Nalini Ragunathan-Thangarajah, Anne-Catherine Bachoud-Levi, and François-Jérôme Authier. 2015. “Neuropsychological Correlates of Brain Perfusion SPECT in Patients with Macrophages Myofasciitis.” PLoS ONE 10 (6): e0128353. https://doi.org/10.1371/journal.pone.0128353.

Van Dongen, Hans P. A. 2025. “Local Versus Global Sleep Organization and the Quest to Determine Sleep Function.” Neurobiology of Sleep and Circadian Rhythms 18 (Suppl): 100117. https://doi.org/10.1016/j.nbscr.2025.100117.

Van Elslande, J. et al. 2021. “Longitudinal Follow-up of IgG Anti-Nucleocapsid Antibodies in SARS-CoV-2 Infected Patients up to Eight Months After Infection.” Journal of Clinical Virology 136: 104765. https://doi.org/10.1016/j.jcv.2021.104765.

Vatvani, Akhil D, Priya Patel, Timotius Ivan Hariyanto, and Theo Audi Yanto. 2024. “Efficacy and Safety of Low-Dose Naltrexone for the Management of Fibromyalgia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials with Trial Sequential Analysis.” The Korean Journal of Pain 37 (4): 367–78. https://doi.org/10.3344/kjp.24202.

Vu, Quan M., Annette L. Fitzpatrick, Jennifer R. Cope, Jeanne Bertolli, Nona Sotoodehnia, Thomas E. West, Nikki L. Gentile, and Elizabeth R. Unger. 2024. “Estimates of Incidence and Predictors of Fatiguing Illness After SARS-CoV-2 Infection.” Emerging Infectious Diseases 30 (3): 539–47. https://doi.org/10.3201/eid3003.231194.

Walitt, Brian, Komudi Singh, Samuel R LaMunion, Mark Hallett, Sandra Jacobson, Kong Chen, Yoshihisa Enose-Akahata, et al. 2024. “Deep Phenotyping of Post-Infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Nature Communications 15 (1): 907. https://doi.org/10.1038/s41467-024-45107-3.

Wang, Chao, Hong Chen, Wei Zhu, Yuan Xu, Mengxin Liu, Lingjun Zhu, Fan Yang, et al. 2017. “Nicotine Accelerates Atherosclerosis in Apolipoprotein e-Deficient Mice by Activating Alpha7 Nicotinic Acetylcholine Receptor on Mast Cells.” Arteriosclerosis, Thrombosis, and Vascular Biology 37 (1): 53–65. https://doi.org/10.1161/ATVBAHA.116.307264.

Wang, Lu, Peng Chen, Qian Gao, Ying Men, Quanyuan Yun, Qiang Zhang, Xin Xing, et al. 2026. “A Telomere-to-Telomere Genome Reveals Arachidonic Acid as a Key Regulator of Hepatic Lipid Metabolism During Hibernation.” Communications Biology. https://doi.org/10.1038/s42003-026-10625-x.

Wang, Rui-Sheng, Kathryn T. Hall, Franco Giulianini, Dani Passow, Ted J. Kaptchuk, and Joseph Loscalzo. 2017. “Network Analysis of the Genomic Basis of the Placebo Effect.” JCI Insight 2 (11): e93911. https://doi.org/10.1172/jci.insight.93911.

Wang, X.-Y., J. D. Huizinga, J. Diamond, and L. W. C. Liu. 2009. “Loss of Intramuscular and Submuscular Interstitial Cells of Cajal and Associated Enteric Nerves Is Related to Decreased Gastric Emptying in Streptozotocin-Induced Diabetes.” Neurogastroenterology and Motility 21 (10): 1095–e92. https://doi.org/10.1111/j.1365-2982.2009.01336.x.

Wang, Yutan, Sijun Li, Yuanyuan Zhang, Yanru Chen, Fanghong Yan, Lin Han, and Yuxia Ma. 2021. “Heat and Cold Therapy Reduce Pain in Patients with Delayed Onset Muscle Soreness: A Systematic Review and Meta-Analysis of 32 Randomized Controlled Trials.” Physical Therapy in Sport 48: 177–87. https://doi.org/10.1016/j.ptsp.2021.01.004.

Wanigasekera, Lasith C., Murray T. Maybery, Romina Palermo, Andrew J. O. Whitehouse, and Diana Weiting Tan. 2025. “First Impressions Towards Autistic People: A Systematic Review and Meta-Analysis.” Autism Research 18 (5): 983–1010. https://doi.org/10.1002/aur.70019.

Ward, Anna I, Jose I de Las Heras, Eric C Schirmer, and Ariberto Fassati. 2025. “Memory CD4+ T Cells Sequentially Restructure Their 3D Genome During Stepwise Activation.” Frontiers in Cell and Developmental Biology 13: 1514627. https://doi.org/10.3389/fcell.2025.1514627.

Watanabe, Makoto, Takuya Goto, Riko Miyoshi, Shohei Nakakubo, Yumiko Hiramatsu, Ryosuke Nakajima, Akihiko Kondo, Kazuki Higashiyama, and Kazuhiro Yamakawa. 2026. “Region-Dependent Differences in Tonic Inhibition Underlie Epileptic Features in Angelman Syndrome Model Mice.” Epilepsia. https://doi.org/10.1002/epi.70307.

Watt, T., S. Oberfoell, R. Balise, M. R. Lunn, A. K. Kar, L. Merrihew, M. S. Bhangoo, and J. G. Montoya. 2012. “Response to Valganciclovir in Chronic Fatigue Syndrome Patients with Human Herpesvirus 6 and Epstein-Barr Virus IgG Antibody Titers.” Journal of Medical Virology 84 (12): 1967–74. https://doi.org/10.1002/jmv.23411.

Wauquier, Fabien, Line Boutin-Wittrant, Line Pourtau, David Gaudout, Benjamin Moras, Adrien Vignault, Caroline Monchaux De Oliveira, Julien Gabaston, et al. 2022. “Circulating Human Serum Metabolites Derived from the Intake of a Saffron Extract Protect Human Neurons from Oxidative Stress-Induced Neurotoxicity.” Nutrients 14 (7): 1511. https://doi.org/10.3390/nu14071511.

Wei, Ke, Rui Li, Xuan Zhao, Bing Xie, Tong Xie, Qiu Sun, Yi Chen, et al. 2025. “TRIM28 Is an Essential Regulator of Three-Dimensional Chromatin State Underpinning CD8+ T Cell Activation.” Nature Communications 16 (1): 750. https://doi.org/10.1038/s41467-025-56029-z.

Wiecha, Szczepan, Igor Cieśliński, Paweł Wiśniowski, Maciej Cieśliński, Wojciech Pawliczek, Paweł Posadzki, Robert Prill, Joanna Zając, and Maciej Płaszewski. 2025. “Physical Therapies for Delayed-Onset Muscle Soreness: An Umbrella and Mapping Systematic Review with Meta-Meta-Analysis.” Sports Medicine 55 (5): 1183–1212. https://doi.org/10.1007/s40279-025-02187-5.

Williams, Graham, Jim Waterhouse, Javier Mugarza, et al. 2002. “Therapy of Circadian Rhythm Disorders in Chronic Fatigue Syndrome: No Symptomatic Improvement with Melatonin or Phototherapy.” European Journal of Clinical Investigation 32 (11): 831–37. https://doi.org/10.1046/j.1365-2362.2002.01081.x.

Windthorst, Petra, Nazar Mazurak, Marvin Kuske, Arno Hipp, Katrin E. Giel, Paul Enck, Andreas Nieß, Stephan Zipfel, and Martin Teufel. 2017. “Heart Rate Variability Biofeedback Therapy and Graded Exercise Training in Management of Chronic Fatigue Syndrome: An Exploratory Pilot Study.” Journal of Psychosomatic Research 93: 6–13. https://doi.org/10.1016/j.jpsychores.2016.11.014.

Wirth, Klaus J., and Carmen Scheibenbogen. 2026. “Imbalance of Excitatory and Inhibitory Neurotransmitter Systems in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” International Journal of Molecular Sciences 27 (9): 4041. https://doi.org/10.3390/ijms27094041.

Wirth, Klaus J., and Jürgen M. Steinacker. 2025. “The Potential Causes of Myasthenia and Fasciculations in Severely Ill ME/CFS Patients: The Role of Disturbed Electrophysiology.” Frontiers in Physiology 16: 1693589. https://doi.org/10.3389/fphys.2025.1693589.

Woestemeier, Anna, Tim Schwandt, Philipp Lingohr, Mariola Lysson, Sven Wehner, Joerg C. Kalff, and G. S. Hong. 2026. “Mast Cell Activation Within the Mesentery Depends on Vagal Signaling Following Abdominal Surgery.” Surgery Open Science 31: 44–53. https://doi.org/10.1016/j.sopen.2026.03.005.

Woo, Tae-Won, Yoo-Jin Choi, Ji-Yeon Kim, Jin-Seok Lee, and Chang-Gue Son. 2026. “Neuroendocrine Signature of ME/CFS: Meta-Analytic Evidence for Bioactive Cortisol Deficit and Exaggerated Feedback Sensitivity.” Molecular Psychiatry. https://doi.org/10.1038/s41380-026-03608-1.

Wu, Cheng-Wei, and Kenneth B Storey. 2021. “mTOR Signaling in Metabolic Stress Adaptation.” Biomolecules 11 (5): 681. https://doi.org/10.3390/biom11050681.

Wu, Kai, Zhiliang Wu, Siyuan Feng, Tianci Zhou, Yuping Ning, Kuangshi Li, and Hongxiao Jia. 2026. “Microstructural White Matter Impairments in Chronic Fatigue Syndrome: Evidence of Segmental Injury in the Cingulum Bundle.” Brain Research Bulletin 234: 111671. https://doi.org/10.1016/j.brainresbull.2025.111671.

Xiong, Ruoyun, Elizabeth Aiken, Ryan Caldwell, Suzanne D. Vernon, Lina Kozhaya, Courtney Gunter, Lucinda Bateman, Derya Unutmaz, and Julia Oh. 2025. “AI-Driven Multi-Omics Modeling of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.” Nature Medicine 31 (9): 2991–3001. https://doi.org/10.1038/s41591-025-03788-3.

Xiong, Ruoyun, Courtney Gunter, Elizabeth Fleming, Suzanne D. Vernon, Lucinda Bateman, Derya Unutmaz, and Julia Oh. 2023. “Multi-’omics of Gut Microbiome-Host Interactions in Short- and Long-Term Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Patients.” Cell Host & Microbe 31 (2): 273–287.e5. https://doi.org/10.1016/j.chom.2023.01.001.

Xu, Wen-Tao, Xian-Bin An, Mei-Jun Chen, Jie Ma, Xue-Qin Wang, Jia-Nan Yang, Qiang Wang, et al. 2025. “A Gene Cluster of Mitochondrial Complexes Contributes to the Cognitive Decline of COVID-19 Infection.” Molecular Neurobiology 62 (6): 6869–83. https://doi.org/10.1007/s12035-024-04471-3.

Yang, DuJiang, and GuoYou Wang. 2026. “Post-Vaccination Syndromes: The Evidence and a Roadmap for Rigorous Research.” Reviews in Medical Virology 36 (1): e70100. https://doi.org/10.1002/rmv.70100.

Yang, Jiangyan, Evelyne Ruchti, Jean-Marie Petit, Pascal Jourdain, Gabriele Grenningloh, Igor Allaman, and Pierre J. Magistretti. 2014. “Lactate Promotes Plasticity Gene Expression by Potentiating NMDA Signaling in Neurons.” Proceedings of the National Academy of Sciences of the United States of America 111 (33): 12228–33. https://doi.org/10.1073/pnas.1322912111.

Yatham, L N, R L Morehouse, B T Chisholm, D A Haase, D D MacDonald, and T J Marrie. 1995. “Neuroendocrine Assessment of Serotonin (5-HT) Function in Chronic Fatigue Syndrome.” Canadian Journal of Psychiatry 40 (2): 93–96. https://doi.org/10.1177/070674379504000207.

Yom-Tov, Elad, and Evgeniy Gabrilovich. 2013. “Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries.” Journal of Medical Internet Research 15 (6): e124. https://doi.org/10.2196/jmir.2614.

Zagon, Ian S, and Patricia J McLaughlin. 2018. “Intermittent Blockade of OGFr and Treatment of Autoimmune Disorders.” Experimental Biology and Medicine (Maywood) 243 (17-18): 1323–30. https://doi.org/10.1177/1535370218817746.

Zaitsu, Munehiro, Shin-ichiroh Narita, Kenneth C Lambert, James J Grady, D Mark Estes, Edward M Curran, Edward G Brooks, Cheryl S Watson, Randall M Goldblum, and Terumi Midoro-Horiuti. 2007. “Estradiol Activates Mast Cells via a Non-Genomic Estrogen Receptor-Alpha and Calcium Influx.” Molecular Immunology 44 (8): 1977–85. https://doi.org/10.1016/j.molimm.2006.09.030.

Zaki, Nevin F W, Mohamed Yousif, Ahmed S BaHammam, David Warren Spence, Vijay K Bharti, Perumal Subramanian, and Seithikurippu R Pandi-Perumal. 2019. “Chronotherapeutics: Recognizing the Importance of Timing Factors in the Treatment of Disease and Sleep Disorders.” Clinical Neuropharmacology 42 (3): 80–87. https://doi.org/10.1097/WNF.0000000000000341.

Zeidman, L A, P Saini, and P Mai. 2022. “Immune-Mediated Small Fiber Neuropathy with Trisulfated Heparin Disaccharide, Fibroblast Growth Factor Receptor 3, or Plexin D1 Antibodies: Presentation and Treatment with Intravenous Immunoglobulin.” Journal of Clinical Neuromuscular Disease 24 (1): 26–37. https://doi.org/10.1097/CND.0000000000000423.

Zhang, J., D. Chen, P. Deming, T. Srirangarajan, J. E. Theriault, P. A. Kragel, L. Hartley, K. M. Lee, K. McVeigh, and T. D. Wager. 2025. “Cortical and Subcortical Mapping of the Human Allostatic-Interoceptive System Using 7 Tesla fMRI.” Nature Neuroscience 28 (11): 2380–91. https://doi.org/10.1038/s41593-025-02087-x.

Zhang, Z, X Ma, Z Xia, J Chen, Y Liu, Y Chen, J Zhu, et al. 2017. “NLRP3 Inflammasome Activation Mediates Fatigue-Like Behaviors in Mice via Neuroinflammation.” Neuroscience 358: 115–23. https://doi.org/10.1016/j.neuroscience.2017.06.048.

Zhang, ZT, XM Du, XJ Ma, Y Zong, JK Chen, CL Yu, YG Liu, YC Chen, LJ Zhao, and GC Lu. 2016. “Activation of the NLRP3 Inflammasome in Lipopolysaccharide-Induced Mouse Fatigue and Its Relevance to Chronic Fatigue Syndrome.” Journal of Neuroinflammation 13 (1): 71. https://doi.org/10.1186/s12974-016-0539-1.

Zhou, L., X. Sun, Y. Shi, J. Liu, G. Luan, and Y. Yang. 2019. “Cysteinyl Leukotriene Receptor Type 1 Antagonist Montelukast Protects Against Injury of Blood-Brain Barrier.” Inflammopharmacology 27 (5): 933–40. https://doi.org/10.1007/s10787-019-00611-7.

Zong, Nansu, Andrew Wen, Sijia Moon, Sunyang Fu, Liwei Wang, Yiqing Zhao, Yue Yu, et al. 2022. “Computational Drug Repurposing Based on Electronic Health Records: A Scoping Review.” NPJ Digital Medicine 5 (1): 77. https://doi.org/10.1038/s41746-022-00617-6.

Zucker, David R, Rebecca Ruthazer, and Christopher H Schmid. 2010. “Individual (n-of-1) Trials Can Be Combined to Give Population Comparative Effectiveness Evidence: Methodologic Considerations.” Journal of Clinical Epidemiology 63 (12): 1312–23. https://doi.org/10.1016/j.jclinepi.2010.04.020.