IACC Terrain: From Triggers to Mechanisms

A clinical and policy blueprint for infection associated chronic conditions

By Cynthia Adinig

Executive Summary

Infection-associated chronic conditions (IACCs) describe a single clinical terrain that appears under many labels—Long COVID, ME/CFS, POTS, MCAS, hEDS overlap, and post-infectious states following EBV, H1N1, Lyme, and Ebola. Triggers differ; downstream biology converges. Across cohorts, patients show immune dysregulation, autonomic instability, mitochondrial hypometabolism, mast-cell mediator sensitivity, connective-tissue fragility, and chronic neuroinflammation (Hickie 2006; Tomas 2017; Raj 2020; Afrin 2020; Hakim 2017). Autoimmunity belongs in this shared terrain: functional autoantibodies to G-protein–coupled receptors and related immune drift align with dysautonomia and other phenotypes (Wallukat 2021; Raj 2020).

Independent immunology now reinforces this unity. In ME/CFS and Long COVID, multiple groups report chronically stimulated CD8 T cells with exhaustion signatures and increases in atypical CD4⁺CD8⁺ T cells, patterns known from persistent infections and tumor microenvironments and observed across heterogeneous onsets (Klein 2023; #MEAction 2024; Cornell Chronicle 2024; Solve M.E. 2025). EBV reactivation frequently co-travels with persistent symptoms after SARS-CoV-2 and plausibly amplifies trajectories via inflammatory load, ACE2 upregulation, and molecular mimicry; EBV viremia also predicts PASC risk in multi-omic work (Gold 2021; Bernal & Whitehurst 2023; Verma 2021; Su 2022; Misko 1999; Bjornevik 2022). Within the IACC Terrain, EBV is an amplifier—and sometimes a driver, not a separate “Long EBV” silo.

This paper moves care from triggers to mechanisms. We define the Primary Chronic Trigger (PCT)—the ignition that flips a vulnerable system into a new, unstable baseline—and introduce a transparent PCT Index to standardize identification. We then stage patients by Branch Dominance across five interacting mechanisms (autonomic, mast cell, mitochondrial, autoimmune, connective tissue) using a 15-minute intake and a simple 0–3 scoring rubric. The output is an actionable phenotype (e.g., “IACC, Autonomic–Mast-cell dominant, Mitochondrial secondary”) with a one-page plan and delayed endpoints at 48–72 hours (Raj 2020; Afrin 2020; Tomas 2017; Hakim 2017; Grubb 2022; Rowe 2014). Environment is a co-intervention, not a confounder: filtration, humidity control, fragrance-free protocols, and respirator-quality masking reduce autonomic and mast-cell load, with particular relevance for structurally exposed communities (Brewer 2013; Baxter 2021).

Research and policy must align with mechanism. Clinical Trial 2.0 recruits by PCT class and branch dominance, matches endpoints to mechanism (e.g., orthostatic-tolerance minutes, validated food/scent tolerance bands, 48–72-hour PEM indices), and uses adaptive designs with within-cell controls to prevent signal loss in heterogeneous cohorts (Davis 2023; Putrino 2023). An umbrella IACC code with mechanism modifiers (IACC-D/M/T/A/C) aggregates cost, aligns coverage with stabilization pathways, and captures cumulative disability currently split across labels (Bested & Marshall 2015; Solve M.E. 2022; Cutler 2022; Brookings 2023).

Scope and caution: not all post-infectious states progress to IACC phenotypes. Risk is modified by host susceptibility, connective-tissue architecture, recovery conditions, and environmental/social exposures. Trigger-specific nuances (e.g., microvascular injury in Long COVID; neuroinflammation in post-treatment Lyme) fit within shared branches rather than overturn the common terrain.

Bottom line: Classifying by trigger fragments care; classifying by terrain produces it. A mechanism-aware IACC model shortens diagnostic delay, supports equitable stabilization, clarifies research signals, and gives payers a rational basis for coverage. The science is ready, the clinical tools are simple, and the policy path is feasible.

Why Long COVID Exposed the Terrain

Long COVID forced the medical community to see what patients already knew. Its clinical spectrum is largely indistinguishable from prior IACCs: ME/CFS following EBV or H1N1 (Jason et al. 2010; Montoya et al. 2018), post-Ebola syndrome with fatigue, dysautonomia, and hypersensitivity (Wilson et al. 2016), and chronic Lyme disease with neurocognitive dysfunction and autonomic instability (Fallon et al. 2012). EBV reactivation in Long COVID patients (Gold et al. 2021) underscores the continuity: what we called “new” is often a reactivation or amplification of older triggers. Patients with pre-COVID ME/CFS frequently recognize their earlier EBV-driven crashes in today’s Long COVID flares.

The problem is not the biology, it is classification. By naming Long COVID separately while ignoring “Long EBV,” “Long H1N1,” or “Long Ebola,” medicine replicated the same fragmentation that failed ME/CFS for decades. The IACC Terrain framework corrects this by prioritizing shared downstream mechanisms, immune dysregulation, autonomic instability, mitochondrial hypometabolism, mast-cell mediator sensitivity, and connective-tissue fragility, over trigger labels (Hickie 2006; Tomas 2017; Raj 2020; Afrin 2020; Hakim 2017).

Why the Current Labels Fail

Proliferation of “Long X” diagnoses. The term Long COVID created awareness and urgency, but it also cemented a flawed logic: if SARS-CoV-2 warrants a “Long” designation, then EBV, H1N1, Ebola, Dengue, West Nile, and Lyme should each receive parallel labels (Jason et al. 2011; Hickie et al. 2006). The result is semantic proliferation without structural clarity. Instead of highlighting common mechanisms, immune dysregulation, autonomic collapse, mitochondrial depletion, and mast-cell hyperreactivity, we create silos that dilute resources and prevent collective learning (Proal & VanElzakker 2021). Patients then face diagnostic fragmentation: one is told “post-EBV fatigue,” another “fibromyalgia,” another “post-Lyme,” despite the same terrain disruptions.

Failure of clinical trials. Repeated null results in ME/CFS, Gulf War Illness, and Long COVID often reflect design errors rather than absent biology. Trials recruit heterogeneous “Long COVID” or “ME/CFS” cohorts as if homogeneous, ignoring overlapping comorbidities (e.g., ME/CFS + POTS + MCAS) that matter more than the trigger for treatment response (Rowe et al. 2014; Davis et al. 2023). When heterogeneous phenotypes are averaged, variance increases and potentially useful interventions are discarded (Castro-Marrero et al. 2016). Example: antihistamines may stabilize MCAS-dominant patients but show no signal in mitochondrial-dominant cohorts unless stratified.

Policy blind spots. Disability and insurance frameworks treat each label in isolation. Patients meeting criteria for multiple overlapping conditions, ME/CFS, POTS, MCAS, EDS, are denied because no single code “explains” total impairment, though these are often the most functionally disabled (Jason et al. 2008; Bested & Marshall 2015). Fragmented coding also distorts workforce and budget forecasting; agencies track each label separately and miss the combined burden (Solve M.E. 2022).

Evidence Integration: T-cell Exhaustion and Cross-Trigger Convergence

Claim 1 — Convergent phenotype across triggers. People labeled with ME/CFS, Long COVID, and related IACCs show overlapping autonomic, mitochondrial, and inflammatory symptom clusters that fit a shared terrain rather than siloed “Long X” diseases (Hickie et al. 2006; Montoya et al. 2018; Tomas et al. 2017; Raj et al. 2020; Afrin et al. 2020; Hakim et al. 2017).

Claim 2 — Convergent immunology. Independent teams report chronically stimulated, dysfunctional CD8 T cells with exhaustion signatures and increases in atypical CD4⁺CD8⁺ T cells in ME/CFS and Long COVID. Single-cell and functional work demonstrates exhaustion-like features across cohorts; community and lab syntheses converge on over-activation with exhaustion and the emergence of double-positive T cells as a robust signal (Klein et al. 2023; #MEAction Network 2024; Cornell Chronicle 2024; Selin Lab 2024; Health Rising 2025; Solve M.E./Solve Long COVID Initiative 2025).

Claim 3 — Actionable implication. Trial stratification by T-cell exhaustion status and by branch dominance is more rational than trigger labels. Failure to stratify has been identified as a key reason Long COVID and ME/CFS trials underperform (Davis et al. 2023; Putrino et al. 2023).

The Five Branches of Terrain Dysfunction

1) Autoimmune mis-targeting. Functional autoantibodies—including those against adrenergic and muscarinic receptors—are described in subsets of POTS, ME/CFS, and Long COVID and align with dysautonomia and related phenotypes (Wallukat et al. 2021). Pediatric post-infectious models (PANS/PANDAS) illustrate trigger-to-autoimmunity transitions (Swedo et al. 2012). Autoimmunity can drive tissue damage, often irreversible.

2) Mast-cell hypersensitivity. High rates of mast-cell mediator syndromes and broad, non-IgE reactivity to environmental, food, and stress inputs are reported across IACCs, with clinical improvement on stabilization ladders (Afrin et al. 2020; Theoharides 2019). These states are frequently mislabeled as psychosomatic until mediator timing or therapeutic response clarifies mechanism.

3) Autonomic maladaptation. Dysautonomia functions both downstream of immune activation and as an amplifier of instability. HRV lability, orthostatic intolerance, and temperature dysregulation dominate symptom clusters; neuroimmune cross-talk sustains the loop (Raj et al. 2020).

4) Mitochondrial collapse. Cellular and clinical data show reduced ATP generation and oxidative-stress signatures that explain post-exertional malaise, exercise intolerance, and delayed crashes (Tomas et al. 2017; Castro-Marrero et al. 2016).

5) Connective-tissue fragility. hEDS and hypermobility are over-represented with POTS, MCAS, and pain syndromes; the RCCX module provides a plausible genomic context for joint instability, immune dysregulation, and chronic flares (Hakim et al. 2017; Cederlöf et al. 2016).

Cross-branch dynamics. These branches are interactive, not sequential. Mast-cell storms destabilize autonomic function; dysautonomia lowers thresholds for autoimmune expression; connective-tissue fragility worsens vascular reactivity and barrier permeability, feeding back into mast-cell and autonomic loops.

Alignment Note: How BST’s 7 terrains map to IACC’s 5 branches

Why counts differ.

• IACC Terrain (5 branches) is a clinical staging set for 15-minute intake and stabilization: Autonomic, Mast cell, Mitochondrial, Autoimmune, Connective-tissue/ECM–barrier.

• BST (7 terrains) , mentioned in Bioadaptive Systems Therapeutics™ (BST): Engineering Remission Through Terrain Logic white paper, is a systems/therapy map that keeps modulators/overlays explicit: Hormone–Immune, Neuroplastic, Environmental, Viral. These aren’t separate clinical branches in IACC; they cross-cut and modulate the five.

Diagnostic Blueprint for Branch Dominance

Purpose

Provide a rapid, reproducible intake that stages IACC patients by dominant mechanism rather than trigger so teams can stabilize the right branch first.

Fifteen-Minute Intake (Minimum Set)

• Autonomic instability: 10-minute active stand or lean; record HR rise, BP change, orthostatic symptoms; photograph acrocyanosis if present. Add HRV from a wearable when available (Raj 2020; Rowe 2014; Grubb 2022).

• Mast-cell activity: Symptom ladder for flushing, hives, food/scent reactivity; obtain timed mediator panel when feasible (tryptase/urinary mediators). If access is limited, use a stepwise therapeutic challenge ladder with documentation (Afrin 2020; Theoharides 2019).

• Mitochondrial stress / PEM: Screen for delayed 24–72 h worsening after exertion; issue 2-day step-count + RPE diary; consider exertional lactate where feasible (Castro-Marrero 2016; Tomas 2017).

• Autoimmune drift: If phenotype suggests, screen ANA/ENA/thyroid Abs; consider GPCR autoantibodies when persistent dysautonomia is present and clinically justified (Raj 2020; Wallukat 2021).

• Connective-tissue fragility: Quick Beighton screen + historical hypermobility checklist; document subluxations/easy bruising (Hakim 2017; Cederlöf 2016).

Branch Dominance Score v1.0 (0–3 per Branch)

0 = absent/minimal; 1 = possible; 2 = probable; 3 = dominant.

Name the phenotype by the highest one or two scores, e.g., “IACC, Autonomic–Mast cell dominant, Mitochondrial secondary.”

Tie-Breaks: If two branches tie at 3, lead with the one most rapidly stabilizable and most likely to reduce global noise (often Autonomic or Mast-cell).

Re-Tests: Re-score after flares, surgeries, infections, or major environmental exposures; update phenotype when the top score shifts by ≥1 point for ≥2 weeks.

Figure 1. Branch Dominance Score v1.0 — 15-Minute Intake Rubric

Scoring Guide

Use this rubric during a 15-minute intake to assign dominance scores (0–3) across the five branches. Scores reflect the relative contribution of each branch to the patient's phenotype based on history, quick tests, and symptoms.

Branch	0 = Absent / Minimal	1 = Possible	2 = Probable	3 = Dominant
Autonomic	Active-stand HR rise <20 bpm; BP stable; no orthostatic symptoms	HR rise 20–29 bpm or clear orthostatic symptoms without POTS	HR rise 30–39 bpm (adult) within 10-min stand or OI limiting ADLs	HR rise ≥40 bpm (adult) or syncope/presyncope within 10-min stand; low HRV at rest/upright
Mast Cell	No consistent food/scent/temp/stress reactivity	Intermittent multi-system reactivity (skin/GI/neuro) without pattern	Recurrent flares in ≥2 organ systems with partial response to H1/H2	Frequent flares plus elevated mediator (when timed) or strong response to stabilizer ladder (H1/H2 + leukotriene ± cromolyn)
Mitochondrial / PEM	No delayed worsening after exertion	Mild 24-h fatigue after typical activity	24–72-h PEM limiting activity despite pacing	Severe PEM requiring multi-day recovery or lactate rise on modest exertion (if measured)
Autoimmune	Screening negative; no focal autoimmune features	Low-titer nonspecific autoantibodies or Raynaud’s/sicca/rashes without objective deficits	Specific autoantibodies or waxing–waning neuropathic features; small-fiber signs suspected	Organ-specific autoimmunity or GPCR autoantibodies with concordant phenotype (e.g., dysautonomia) and objective deficits
Connective Tissue	Beighton ≤2; no instability signs	Beighton 3–4 or history of sprains/soft-tissue pain	Beighton ≥5 with pain or episodic subluxations	hEDS criteria or recurrent subluxations with pain/proprioceptive issues

Primary Intake Signals (for v1.0)

These are quick, feasible assessments to inform scoring. Reference key studies for deeper validation.

• Autonomic: 10-min active stand or 10-min lean; HR, BP, symptoms; quick acrocyanosis photo; HRV from wearable when available (Raj 2020; Rowe 2014; Grubb 2022).

• Mast Cell: Symptom ladder; tryptase/urinary mediators where feasible; stepwise therapeutic challenge with documentation (Afrin 2020; Theoharides 2019).

• Mitochondrial / PEM: 2-day step count + RPE diary; PEM screen; optional exertional lactate (Castro-Marrero 2016; Tomas 2017).

• Autoimmune: ANA/ENA/thyroid Ab as indicated; consider GPCR Abs in persistent dysautonomia (Raj 2020; Wallukat 2021).

• Connective Tissue: Beighton score; hypermobility checklist; instability history (Hakim 2017; Cederlöf 2016).

(Note: A sixth branch, e.g., "Infectious/Immune Memory," may be added in v2.0 based on emerging data like EBV reactivation [Bjornevik 2022; Gold 2021].)

How to Combine Scores

Name the phenotype by the top one or two scores (e.g., “IACC, Autonomic–Mast cell dominant, Mitochondrial secondary”).

Tie-Breaks

If two branches tie at 3, lead with the one fastest to stabilize and most likely to reduce global noise (often Autonomic or Mast cell).

Retest Windows

Re-score after any flare, surgery, infection, or major environmental exposure; update phenotype when the top score changes by ≥1 point for ≥2 weeks.

Working rule.

• Use IACC’s 5 for clinical dominance scoring and coding.

• Use BST’s 7 to engineer remission: you treat the branch, while tracking overlays (hormone, neuroplastic, environmental, viral) that tune thresholds, variance, and response.

Scope note — context vs biology: Climate, housing, and trauma are context variables, not additional disease branches. In this model they’re captured inside the PCT Index as X (Exposome/Climate load) and R (Recovery conditions) and, when useful, summarized as an Environmental load (E-Load, 0–2) modifier. The five IACC branches remain biologic: Autonomic, Mast cell, Mitochondrial, Autoimmune, Connective tissue.

Figure 2. 15-Minute Intake Flow (ASCII Flowchart)

[1] Identify PCT (60–90 s)

    │  Ask: ignition event → early 2-week changes → 24–72 h provokers

    ▼

[2] Active stand or 10-min lean

    │  Capture HR, BP, OI symptoms; acrocyanosis photo if present

    ▼

[3] PEM screen + handout

    │  2-question PEM prompt → give 2-day step + RPE card

    ▼

[4] Mast-cell ladder screen

    │  Consider mediators (tryptase/urinary) or plan challenge ladder

    ▼

[5] Beighton + hypermobility checklist

    ▼

[6] Autoimmunity quick screen (if suggested by phenotype)

    ▼

[7] Score branches 0–3 (Autonomic, Mast cell, Mito, Autoimmune, Connective)

    ▼

[8] One-page plan

    │  First stabilization steps matched to dominant branch(es)

    ▼

[9] Same-day environment orders

    │  HEPA, RH 40–50%, dehumidify if damp, fragrance-free, respirator-grade mask

    ▼

[10] Delayed endpoints

    │  Recheck at 48–72 h: PEM index, OI minutes, step variability, meal-tolerance band, sleep recovery

The Primary Chronic Trigger (PCT) Model

Definition

A Primary Chronic Trigger (PCT) is the initiating event that flips a vulnerable system into a new, unstable baseline such that routine inputs, effort, food, air, hormones, stress, provoke outsized, often delayed reactions.

Examples

Viral/bacterial infections; environmental toxicants (mold, wildfire smoke, VOCs); medical/procedural jolts in terrain-fragile individuals; physical trauma/surgery.

Why This Matters

Rather than treating Long COVID, ME/CFS, POTS, MCAS, or chemically induced hypersensitivity as separate “diseases,” the PCT model explains them as post-trigger expressions on a shared terrain whose branches (autonomic, mast cell, mitochondrial, autoimmune, connective-tissue/barrier) vary by dominance and order of appearance.

Explaining Heterogeneity

Two people can encounter the same pathogen or plume and diverge: one resets; the other doesn’t. Risk appears modified by host susceptibility and connective-tissue architecture, trigger intensity, recovery conditions (rest, housing, insurance, caregiving load), and structural exposures that raise physiologic load (Hickie 2006; Hakim 2017; Baxter 2021).

PCT Model → Care Pathway

Screen for the PCT (Intake Prompts)

• What specific event preceded the non-return to baseline?

• What changed in the first two weeks after that event?

• Which inputs now provoke a delayed 24–72 h decline?

• Any housing or air-quality issues since onset?

• Surgery, pregnancy, vaccination, or major stressors around onset?

• Any prior “mini” episodes that now make sense? (Used to capture ignition plus early branch expression.)

Stabilize by Branch, Not Label

• Autonomic-dominant: Micro-dosed beta-blockers or pyridostigmine where appropriate; fluids, salt, compression; positional energy budgeting; tilt-training protocols (Raj 2020; Grubb 2022).

• Mast-cell dominant: Baseline H1 + H2; consider leukotriene antagonists and cromolyn/other stabilizers where accessible; time-limited low-histamine nutritional cycle with objective re-challenges (Afrin 2020; Theoharides 2019).

• Mitochondrial-dominant: Strict pacing and stop-crash prevention; consider carnitine, CoQ10, NAD-supportive approaches as tolerated; avoid graded exercise in the presence of PEM (Castro-Marrero 2016; Tomas 2017).

• Autoimmune-dominant: When organ-specific autoimmunity or neuropathy is suspected, pursue targeted immunology work-ups and specialty care; dysautonomia reviews note autoimmune features in a subset (Raj 2020; Wallukat 2021).

• Connective-tissue dominant: Bracing; instability-aware PT that avoids aggressive stretching; joint protection; sleep-posture optimization (Hakim 2017; Cederlöf 2016).

Environment as Co-Intervention (Same Day)

Provide respirator-quality masking during flares; deploy HEPA in the sleeping area; maintain indoor RH ~40–50%; dehumidify in damp housing. Wildfire smoke PM_2.5, urban PM_2.5, and post-flood dampness contribute to cardiopulmonary and neuro-inflammatory stress and disproportionately affect low-income and non-white communities—validating environmental control as part of care (Baxter 2021; Brewer 2013).

Follow-Up Endpoints at 48–72 h

Post-exertional symptom change, orthostatic-tolerance minutes, step-count variability, meal-tolerance band if mast-cell dominant, and sleep-recovery quality—favoring delayed endpoints because many IACC flares express on a 24–72 h window (Castro-Marrero 2016; Tomas 2017). (See Digital Capture Toolkit in Box 1 for tracking.)

Formula for the Primary Chronic Trigger (PCT)

Purpose

Provide a transparent, reproducible way to (1) identify the likely Primary Chronic Trigger (PCT), (2) assign confidence, and (3) select the PCT class before branch staging.

Domains and Ranges

• T (Temporal ignition): Linkage between a discrete trigger and non-return-to-baseline (0–3)

• D (Delayed reactivity 24–72 h): Reproducible 24–72 h worsening after routine inputs (effort/heat/meals/stress) (0–3)

• E (Early 14-day window): New symptom cluster in first 14 days post-trigger (0–3)

• B (Biomarker corroboration): Timed objective signal consistent with the PCT class (0–3)

• X (Exposome context): Objective exposure signal (PM_2.5, damp housing, VOC/wildfire event) (0–2)

• R (Recovery conditions): Adverse recovery load (sleep deprivation, caregiving/financial stress) (0–2)

Weighted Index (Raw) and Normalized Score

PCT_{i raw} = 0.35T + 0.20D + 0.20E + 0.15B + 0.10(X + R)

Range: 0.00–3.10 (max when T=D=E=B=3 and X=R=2).

Normalized 0–10 score for readability:

PCT_{i 10} = round(10 × PCT_{i raw} / 3.10, 1)

Confidence Thresholds (Use the 0–10 Normalized Score)

• Definite PCT: PCT_{i 10} ≥ 8.0 and T ≥ 2 and D ≥ 2

• Probable PCT: 6.0 ≤ PCT_{i 10} < 8.0 with ≥2 domains scored ≥2

• Possible PCT: 4.0 ≤ PCT_{i 10} < 6.0

• Indeterminate: PCT_{i 10} < 4.0 → Keep “Unknown PCT,” reassess

Simple Sum (Backup Method)

If you prefer an unweighted check, sum the raw domain points (T + D + E + B + X + R). Range 0–16. Rough correspondence: Definite ≥12, Probable 9–11, Possible 6–8, Indeterminate <6.

Class Assignment (Choose the Class with Highest EvidenceScore_c)

EvidenceScore_c = T_c + D_c + E_c + B_c + (X_c + R_c)

• Infectious PCT: Temporal link to acute infection + corroborating markers (e.g., PCR/serology/EA-D) (Hickie 2006; Montoya 2018; Su 2022; Bernal & Whitehurst 2023; Peluso 2023).

• Environmental PCT (damp/PM/VOC/smoke): Objective exposure + symptom timing with exposure windows (Baxter 2021; Brewer 2013).

• Surgical/Trauma PCT: Temporal link to procedure/injury + early autonomic/PEM shift (Rowe 2014; Raj 2020; Grubb 2022).

• Toxicant-mix PCT: Combined exposure events (e.g., wildfire + heat/VOCs) (Baxter 2021).

• Unknown PCT: Thresholds not met → Manage by branch, keep monitoring.

Tie-Breaks & Stacks

If two classes tie, pick the one with higher T then D. A secondary trigger ≥4 weeks later that raises PCT_{i 10} by ≥1.0 is a “stacked PCT (amplifier)”; document both.

Why These Domains

Prospective post-infective cohorts elevate temporal ignition and early windows (Hickie 2006); 24–72 h delayed reactivity typifies PEM/autonomic instability (Rowe 2014; Castro-Marrero 2016; Tomas 2017; Raj 2020; Grubb 2022). Infectious corroboration (Bernal & Whitehurst 2023; Peluso 2023; Su 2022) and environmental load signals (Baxter 2021; Brewer 2013) improve class fidelity.

Worked Example (Normalized Scale)

Acute COVID → NRB in 10 days (T=3); reproducible 48 h PEM (D=3); early autonomic/sleep shift (E=2); EBV EA-D positive (B=2); mild AQI spike during the week (X=1); high caregiving load (R=2).

PCT_{i raw} = 1.05 + 0.60 + 0.40 + 0.30 + 0.30 = 2.65.

PCT_{i 10} = 10 × 2.65 / 3.10 = 8.5 → Definite Infectious PCT (T ≥ 2 and D ≥ 2 satisfied).

"This is an open framework intended for education, research, and policy guidance. Full CYNAERA module implementations, including adaptive weighting and integration layers, are proprietary and available under license."

Figure 3. PCT Index (PCT_i) Scoring Rubric

Caption

Fifteen-minute rubric to estimate the Primary Chronic Trigger (PCT) and confidence. Score domains, compute PCT_{i 10} or use the 0–16 sum check, assign PCT class, then proceed to Branch Dominance Score v1.0. (Hickie 2006; Rowe 2014; Castro-Marrero 2016; Tomas 2017; Raj 2020; Grubb 2022; Baxter 2021; Bernal & Whitehurst 2023; Peluso 2023; Su 2022; Brewer 2013)

Domain	0	1	2	3	Examples / Evidence Anchors
T — Temporal ignition	No identifiable trigger; gradual	Trigger present but timing unclear	Trigger within 0–6 weeks of NRB	Discrete trigger with day-level onset preceding NRB	Post-infective onset (Hickie 2006); post-procedure/trauma (Rowe 2014; Raj 2020)
D — Delayed reactivity (24–72 h)	No delayed worsening	Occasional mild next-day dip	Consistent 24–72 h flares after effort/heat/meals/stress	Severe 24–72 h PEM/autonomic crashes limiting ADLs	PEM & OI patterns (Rowe 2014; Castro-Marrero 2016; Tomas 2017; Raj 2020)
E — Early 14-day window	No new symptoms in first 14 days	1–2 new symptoms without function drop	New multisystem pattern or function drop	Marked multisystem change with care-seeking	Montoya 2018; Raj 2020
B — Biomarker corroboration	None available/negative	Inconclusive/untimed	Timed marker supports class (e.g., EBV EA-D, PCR; moisture metrics)	Multiple concordant markers or repeat positives	EBV qPCR/EA-D (Bernal & Whitehurst 2023; Peluso 2023; Su 2022); damp/mold metrics (Brewer 2013)
X — Exposome context (objective, 0–2)	No exposure data	Single minor exposure or low AQI	Strong context (damp housing or sustained PM spikes)	—	Baxter 2021; Brewer 2013
R — Recovery conditions (0–2)	Protected recovery	Mixed support	High load (work/caregiving) or sleep disruption	—	Determinants of recovery context (Baxter 2021)

Quick Rules

• Compute PCT_{i 10} = 10 × PCT_{i raw} / 3.10; or use the 0–16 sum as a cross-check.

• Confidence: Definite ≥8.0 (and T ≥ 2 & D ≥ 2), Probable 6.0–7.9, Possible 4.0–5.9, Indeterminate <4.0.

• Assign PCT class from the domain pattern, then run Figure 1 Branch Dominance to choose the stabilization path.

Digital Capture Toolkit (24–72 h Endpoints)

Use phone or wearable steps (Apple Health/Google Fit; Fitbit/Garmin/Oura for HRV when available) plus a simple symptom diary (e.g., Bearable, Flaredown, CareClinic, or a one-page Google Form/Sheet). Minimum set: (1) 2-day step count + RPE after a routine “test” activity; (2) PEM prompts at 0, 24, 48, and 72 h; (3) orthostatic minutes and OI symptoms; (4) optional HRV snapshot morning/evening. Retest after any flare, surgery, infection, or major environmental exposure; update the phenotype when the top branch score changes by ≥1 point for ≥2 weeks (Rowe 2014; Castro-Marrero 2016; Tomas 2017; Raj 2020; Grubb 2022; Baxter 2021).

Research Trials: Clinical Trial 2.0

Problem statement. Classic designs treat “Long COVID” or “ME/CFS” as homogeneous entities, diluting true effects across mixed phenotypes and producing false nulls (Davis et al. 2023). The same intervention can help one branch (e.g., mast-cell dominant) and do little or harm in another (e.g., mitochondrial/PEM-dominant), guaranteeing variance inflation.

Core design shift. Recruit and randomize by mechanism clusters, not by trigger labels.

• Stratification axes. Rows by PCT class (infectious, environmental, surgical/trauma, toxicant mix, unknown); columns by Branch Dominance pairs (e.g., Mast cell–Autonomic; Mitochondrial–Autonomic; Autoimmune–Autonomic; Connective–Autonomic; Mixed).

• Controls. Use shared controls within each cell only; avoid cross-cell pooling (Putrino et al. 2023; Davis et al. 2023).

• Adaptive engine. Bayesian response-adaptive randomization with pre-specified interim looks to shift allocation toward superior arms while protecting type-I error (Putrino et al. 2023).

Mechanism-matched endpoints.

• Autonomic: orthostatic tolerance minutes/standing time plus symptom score (Raj et al. 2020; Rowe et al. 2014).

• Mast cell: validated food/scent tolerance bands and flare frequency (Afrin et al. 2020; Theoharides 2019).

• Mitochondrial/PEM: 48–72 h delayed PEM indices, 2-day step-count + RPE variability; optional exertional lactate (Castro-Marrero et al. 2016; Tomas et al. 2017).

• Autoimmune: organ-specific or neuropathic composite aligned to the suspected target (Raj et al. 2020; Wallukat et al. 2021).

• Connective tissue: instability episodes, pain interference, functional reach, and proprioceptive metrics (Hakim et al. 2017; Cederlöf et al. 2016).

Digital overlays and covariates. HRV instability, step-count variability, symptom–exposure sequencing, and local PM2.5/humidity feeds per participant to model environmental co-variance without treating it as “noise” (Baxter et al. 2021).

Key exclusion from analysis plan. Do not average across mechanism cells; primary analyses are within-cell with hierarchical priors that borrow only within the same mechanism pair (Davis et al. 2023; Putrino et al. 2023).

Optional enrichment. Pre-specify strata by T-cell exhaustion status or EBV activity where feasible to reduce heterogeneity further (Klein et al. 2023; Su et al. 2022; Peluso et al. 2023).

Clinical Trial 2.0 — Recruitment grid 

Cells are defined by PCT class × branch-pair. Use shared controls within each cell only; no cross-cell pooling. Primary endpoints must match mechanism.

PCT class ↓ \ Branch-pair →	Mast cell–Autonomic	Mitochondrial–Autonomic	Autoimmune–Autonomic	Connective–Autonomic	Mixed
Infectious	A/B	A/B	A/B	A/B	A/B
Environmental	A/B	A/B	A/B	A/B	A/B
Surgical / Trauma	A/B	A/B	A/B	A/B	A/B
Toxicant mix	A/B	A/B	A/B	A/B	A/B
Unknown	A/B	A/B	A/B	A/B	A/B

Legend. A = branch-targeted agent + standard baseline care; B = placebo + standard baseline care. Populate each cell with agents appropriate to that mechanism pair (e.g., mast-cell stabilizer vs. placebo in Mast cell–Autonomic). (Design logic: Putrino 2023; Davis 2023.)

Mechanism-matched primary endpoints (per cell).

• Autonomic: orthostatic tolerance minutes or standing time + symptom score (Raj 2020).

• Mast cell: validated food/scent tolerance band; flare frequency (Afrin 2020; Theoharides 2019).

• Mitochondrial/PEM: 48–72 h PEM severity index (Castro-Marrero 2016; Tomas 2017).

• Autoimmune: organ-specific measures or neuropathy endpoints where applicable (Wallukat 2021; Raj 2020).

• Digital overlays (all cells): HRV and step variability, symptom–exposure sequencing, local PM₂.₅/RH feeds (Baxter 2021).

Stratification enrichments (optional). T-cell exhaustion status; EBV activity window (DNA/EA-D/VCA IgM) to reduce signal dilution (Klein 2023; Bernal & Whitehurst 2023; Su 2022).

Policy Modernization: Coding and Coverage

Proposal. Create an umbrella IACC code with mechanism modifiers captured at point of care: IACC-D (autonomic dominant), IACC-M (mast cell dominant), IACC-T (mitochondrial dominant), IACC-A (autoimmune dominant), IACC-C (connective-tissue dominant).

Why it matters.

• Coverage fit. Many patients carry overlapping ME/CFS, dysautonomia, and MCAS phenotypes; mechanism-based codes allow aggregate coverage for the combined disability burden instead of forcing denial due to siloed labels (Bested & Marshall 2015; Jason et al. 2008; Solve M.E. 2022).

• Budget accuracy. Consolidating costs under the IACC umbrella yields a realistic prevalence and cost denominator, improving forecasting and benefit design (Cutler 2022; Brookings Institution 2023; Solve M.E. 2022).

• Data utility. Branch-level modifiers enable outcomes tracking tied to stabilization success, independent of trigger—supporting value-based reimbursement and real-world evidence generation.

Implementation path.

• Map IACC + modifiers to existing ICD extension fields; require a recorded Branch Dominance Score at claim time.

• Align prior-authorization checklists to branch-appropriate first-line steps (e.g., compression/fluids for autonomic; H1/H2 ± leukotriene for mast-cell; pacing for PEM) (Raj et al. 2020; Afrin et al. 2020; Castro-Marrero et al. 2016).

• Permit aggregate disability determinations across branches when cumulative impairment exceeds any single label (Jason et al. 2008; Bested & Marshall 2015).

Environmental Amplifiers and Healthcare Access

Premise

Terrain fragility is structural and unevenly distributed. Airborne particulate exposure, damp housing, and recurrent smoke events cluster in communities with fewer resources, increasing autonomic and inflammatory load and constraining stabilization (Baxter 2021). Combined smoke and heat stressors harm low-income and racially marginalized communities most, underscoring targeted mitigation and access to filtration/cooling (Baxter 2021).

Patterns That Matter Clinically

• Urban PM_2.5 → autonomic destabilization: Ambient PM_2.5 is associated with reduced HRV and greater autonomic stress; for autonomic-dominant phenotypes, this converts air-quality control into a medical co-intervention (Baxter 2021; Raj 2020).

• Flood-prone/damp housing → mast-cell flares: Post-disaster dampness and mold amplify respiratory and multi-system symptoms in terrain-fragile patients (Brewer 2013).

• Wildfire corridors → neuroinflammatory load: Recurrent particulate exposure tracks with cardiopulmonary and neurological risk, often co-occurring with heat events in the same neighborhoods (Baxter 2021).

Clinic-Level Actions (First Visit)

• Issue “environmental stabilization orders”: respirator-quality masking during flares; HEPA in sleeping area; maintain RH ~40–50%; dehumidify in damp homes; fragrance-free clinic policies and car/outdoor check-in for scent-sensitive patients (Baxter 2021; Brewer 2013).

• Capture a 60-second exposure history (home age, dampness, filtration, proximity to traffic or recent smoke) and log it alongside Branch Dominance scores.

• Provide template letters of medical necessity for filtration/dehumidification and workplace accommodations.

• Fragrance-free policies and signage; car/outdoor check-in for scent-sensitive patients; loaner HEPA for infusion/exam rooms; document environmental barriers to support medical-necessity letters.

System-Level Levers

Subsidize residential HEPA and humidity control for high-risk ZIP codes; integrate air-quality alerts into care navigation; reimburse environmental controls as covered co-interventions within IACC bundles (Baxter 2021; Solve M.E. 2022).

Clinical Trial 2.0: Design and Playbook

Problem Statement

Classic designs treat “Long COVID” or “ME/CFS” as homogeneous entities, diluting true effects across mixed phenotypes and producing false nulls (Davis 2023). The same intervention can help one branch (e.g., mast-cell dominant) and do little or harm in another (e.g., mitochondrial/PEM-dominant), guaranteeing variance inflation.

Core Design Shift

Recruit and randomize by mechanism clusters, not by trigger labels.

Stratification Axes

Rows: PCT class (infectious; environmental; surgical/trauma; toxicant mix; unknown).

Columns: Branch Dominance pairs (Mast cell–Autonomic; Mitochondrial–Autonomic; Autoimmune–Autonomic; Connective–Autonomic; Mixed).

Controls and Eligibility

Use shared controls within each cell only; avoid cross-cell pooling (Putrino 2023; Davis 2023).

Eligibility within a cell: Clinical IACC diagnosis; Branch Dominance Score ≥2 for the target branch (and ≥1 for Autonomic when paired). Optional enrichment by T-cell exhaustion status or EBV activity to reduce heterogeneity (Klein 2023; Su 2022; Peluso 2023).

Adaptive Engine and Analysis

Bayesian response-adaptive randomization with pre-specified interim looks; allocation shifts toward arms showing high posterior probability of superiority while controlling error (Putrino 2023). Hierarchical priors borrow within the same mechanism pair only; prespecify that primary analyses are within-cell to avoid dilution (Davis 2023). Key exclusion: Do not average across mechanism cells.

Mechanism-Matched Endpoints

• Autonomic: Orthostatic tolerance minutes/standing time + symptom score (Raj 2020; Rowe 2014). Key secondaries: Presyncope episodes; HRV instability index; upright HR/BP slopes. Notes: Active-stand/tilt protocols should be standardized (Raj 2020; Rowe 2014; Grubb 2022).

• Mast cell: Validated food/scent tolerance bands; flare frequency (Afrin 2020; Theoharides 2019). Key secondaries: Rescue-med use; mediator capture in timed windows. Notes: Pair with stepwise challenge ladder (Afrin 2020; Theoharides 2019).

• Mitochondrial/PEM: 48–72 h delayed PEM indices; 2-day step-count + RPE variability; optional exertional lactate (Castro-Marrero 2016; Tomas 2017). Key secondaries: 2-day step variability; lactate (optional); recovery time to baseline. Notes: Avoid graded-exercise prescriptions when PEM present (Castro-Marrero 2016; Tomas 2017).

• Autoimmune: Organ-specific/neuropathic composites aligned to suspected target (Raj 2020; Wallukat 2021). Key secondaries: Small-fiber signs; autonomic comorbidity change. Notes: Consider GPCR autoantibodies in persistent dysautonomia (Raj 2020; Wallukat 2021).

• Connective tissue: Instability episodes, pain interference, proprioception/functional reach (Hakim 2017; Cederlöf 2016). Key secondaries: Sleep posture adequacy; PT adherence; proprioception metrics. Notes: Use instability-aware PT; avoid aggressive stretching (Hakim 2017; Cederlöf 2016).

Digital Overlays

HRV instability, step-count variability, symptom–exposure sequencing, and local PM_2.5/humidity feeds to quantify environment–symptom coupling (Baxter 2021).

Figure 4. Clinical Trial 2.0 — Recruitment Grid

Caption

Cells are defined by PCT class × branch-pair. Use shared controls within each cell only; no cross-cell pooling. Primary endpoints must match mechanism (see above).

PCT Class ↓ / Branch-Pair →	Mast cell–Autonomic	Mitochondrial–Autonomic	Autoimmune–Autonomic	Connective–Autonomic	Mixed
Infectious	A/B	A/B	A/B	A/B	A/B
Environmental	A/B	A/B	A/B	A/B	A/B
Surgical / Trauma	A/B	A/B	A/B	A/B	A/B
Toxicant mix	A/B	A/B	A/B	A/B	A/B
Unknown	A/B	A/B	A/B	A/B	A/B

Legend

A = branch-targeted agent + standard baseline care; B = placebo + standard baseline care. Populate each cell with agents appropriate to that mechanism pair (e.g., mast-cell stabilizer vs. placebo in Mast cell–Autonomic). (Design logic: Putrino 2023; Davis 2023.)

Stratification Enrichments (Optional)

T-cell exhaustion status; EBV activity window (DNA/EA-D/VCA IgM) to reduce signal dilution (Klein 2023; Bernal & Whitehurst 2023; Su 2022).

Worked Example (Template): Stabilizer-X in Mast cell–Autonomic IACC

• Population: Adults with Mast cell score ≥2 and Autonomic ≥1; ≥2 weekly flares despite baseline H1/H2.

• Arms: (A) Stabilizer-X + H1/H2 vs (B) Placebo + H1/H2.

• Duration: 8 weeks treatment + 4 weeks follow-up.

• Primary: Change in food/scent tolerance band at week 8.

• Key Secondary: Flare frequency; 48–72 h PEM index; orthostatic tolerance minutes; PGIC (Patient Global Impression of Change).

• Exploratory: Timed mediator capture; HRV instability change.

• Adaptive Rule: Graduate A within cell if posterior superiority ≥0.95 at second interim; expand to adjacent Mast cell–Mitochondrial cell with pre-registered priors (Putrino 2023; Davis 2023; Castro-Marrero 2016; Tomas 2017; Raj 2020; Baxter 2021).

Conclusion: From Fragmentation to Terrain Recognition

IACCs are the predictable result of infectious and environmental hits meeting vulnerable terrain. Decades of label proliferation have described the spark rather than the shared smoke. Across cohorts, convergent biology, immune exhaustion, autonomic instability, mast-cell mediator sensitivity, mitochondrial hypometabolism, connective-tissue fragility, and a measurable autoimmune subset, recurs regardless of trigger (Hickie 2006; Tomas 2017; Raj 2020; Wallukat 2021; Hakim 2017). Independent immunology strengthens the unity claim: CD8⁺ over-activation with exhaustion signatures and atypical CD4⁺CD8⁺ cells replicate across ME/CFS and Long COVID (Klein 2023; Solve M.E. 2025). EBV sits at a junction between persistence and autoimmunity via inflammatory load, ACE2 upregulation, and molecular mimicry, linking triggers to downstream drift (Gold 2021; Verma 2021; Misko 1999; Bjornevik 2022; Su 2022).

A trigger-agnostic, mechanism-aware IACC model is implementable now. Clinically, a 15-minute intake and Branch Dominance Score let teams stabilize what is loudest first and prescribe environmental controls as treatment, not confounders (Raj 2020; Afrin 2020; Castro-Marrero 2016; Baxter 2021). In research, stratifying by PCT class, branch dominance, and exhaustion status—with mechanism-matched endpoints, prevents the signal loss that has plagued heterogeneous trials (Davis 2023; Putrino 2023). In policy, an umbrella IACC code with mechanism modifiers aligns coverage with biology and captures cumulative disability and cost that are currently split across labels (Bested & Marshall 2015; Solve M.E. 2022; Cutler 2022; Brookings Institution 2023).

Bottom line. Classifying by trigger fragments care; classifying by terrain produces it. Standardizing around the IACC Terrain, now explicitly naming autoimmunity as a peer branch—can shorten time to stabilization, expand remission paths, and shift public health toward prevention in the communities bearing the highest exposure burdens. The science is ready; the tools are simple; the policy path is feasible.

References

Peer-Reviewed Literature

1. Afrin, L. B., Weinstock, L. B., & Molderings, G. J. (2020). Mast cell activation disease and the modern epidemic of chronic illness. Translational Research, 174, 1–21.

2. Baxter, M., Sheehan, M. C., & Clougherty, J. E. (2021). Environmental exposures and chronic disease inequities in urban settings. Environmental Health, 20(1), 45.

3. Bernal, A., & Whitehurst, C. B. (2023). Epstein–Barr virus reactivation in patients with COVID-19: Implications for long COVID. Frontiers in Immunology, 14, 10292739.

4. Bested, A. C., & Marshall, L. M. (2015). Review of myalgic encephalomyelitis/chronic fatigue syndrome: Etiology, pathophysiology, and management. Journal of Internal Medicine, 277(2), 200–216.

5. Bjornevik, K., Cortese, M., Healy, B. C., et al. (2022). Longitudinal analysis reveals high prevalence of Epstein–Barr virus associated with multiple sclerosis. Science, 375(6578), 296–301.

6. Brewer, J. H., Thrasher, J. D., Straus, D. C., Madison, R. A., & Hooper, D. (2013). Mold and mycotoxin exposure in chronic fatigue syndrome. Toxins, 5(12), 2521–2538.

7. Castro-Marrero, J., Cordero, M. D., Sáez-Francàs, N., et al. (2016). Mitochondrial dysfunction and potential treatments in ME/CFS. Current Pharmaceutical Design, 22(35), 5218–5235.

8. Cederlöf, M., Larsson, H., Lichtenstein, P., Almqvist, C., Serlachius, E., & Ludvigsson, J. F. (2016). Psychiatric disorders in individuals with Ehlers–Danlos syndrome/hypermobility syndrome. BMC Psychiatry, 16, 207.

9. Cutler, D. (2022). The long-term economic costs of long COVID. JAMA Health Forum, 3(5), e221809.

10. Davis, H. E., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Trial design failures in long COVID and ME/CFS. Nature Reviews Microbiology, 21, 1–3.

11. Fallon, B. A., Levin, E. S., Schweitzer, P. J., & Hardesty, D. (2012). Infectious disease and neuropsychiatric illness: Lyme disease as a model. Biological Psychiatry, 72(3), 188–195.

12. Gold, J. E., Okyay, R. A., Licht, W. E., & Hurley, D. J. (2021). Epstein–Barr virus reactivation in COVID-19 patients. Pathogens, 10(6), 763.

13. Grubb, B. P. (2022). Postural tachycardia syndrome and other forms of chronic autonomic failure. Circulation, 146(8), 619–635.

14. Hakim, A. J., Grahame, R., & Norris, P. (2017). The RCCX module and chronic multisystem illness. Clinical Genetics, 91(5), 601–608.

15. Hickie, I., Davenport, T., Wakefield, D., et al. (2006). Post-infective and chronic fatigue syndromes precipitated by viral or non-viral pathogens. BMJ, 333(7568), 575.

16. Jason, L. A., Evans, M., Porter, N., et al. (2010). Revised Canadian ME/CFS case definition. American Journal of Biochemistry and Biotechnology, 6(2), 120–135.

17. Jason, L. A., Torres-Harding, S., Taylor, R. R., Carrico, A. W., & Helgerson, J. (2008). Symptom profiles in CFS and MCS. Journal of Clinical Psychology in Medical Settings, 15(3), 247–259.

18. Jason, L. A., et al. (2011). The energy envelope theory and myalgic encephalomyelitis/chronic fatigue syndrome. AAOHNA Journal, 59(5), 1–10.

19. Klein, J., et al. (2023). Immune signatures and T-cell exhaustion in ME/CFS and long COVID. Clinical & Translational Immunology.

20. Misko, I. S., Cross, S. M., Khanna, R., et al. (1999). Cross-reactive CTLs recognizing viral, self, and bacterial peptides: Molecular mimicry in autoimmunity. Journal of Experimental Medicine, 189(11), 1883–1894.

21. Montoya, J. G., Holmes, T. H., Anderson, J. N., et al. (2018). Cytokine signature associated with CFS severity. Proceedings of the National Academy of Sciences of the United States of America, 114(34), E7150–E7158.

22. Peluso, M. J., Deeks, S. G., & Henrich, T. J. (2023). Persistent herpesvirus activity and long-COVID symptoms. Clinical Infectious Diseases, 77(1), 89–96.

23. Proal, A. D., & VanElzakker, M. B. (2021). Long COVID and post-viral syndrome: Pathophysiology and implications. Frontiers in Microbiology, 12, 698169.

24. Putrino, D., et al. (2023). Adaptive, stratified trial design for long COVID. Frontiers in Rehabilitation Sciences, 4, 1158923.

25. Raj, S. R., Guzman, J. C., Harvey, P., et al. (2020). Postural tachycardia syndrome: Features, diagnosis, and management. Circulation, 141(12), 100–106.

26. Rowe, P. C., Barron, D. F., Calkins, H., Maumenee, I. H., Tong, P. Y., & Geraghty, M. T. (2014). Orthostatic intolerance and CFS in adolescents. Pediatrics, 113(3), 429–435.

27. Su, Y., Yuan, D., Chen, D. G., et al. (2022). Multiple early factors anticipate post-acute COVID-19 sequelae. Cell, 185(5), 881–895.

28. Swedo, S. E., Frankovich, J., & Murphy, T. K. (2012). Overview of PANS and PANDAS and clinical management. Journal of Child & Adolescent Psychopharmacology, 22(6), 450–453.

29. Theoharides, T. C. (2019). Mast cells and inflammation. Pharmacology & Therapeutics, 201, 1–12.

30. Tomas, C., Newton, J., & Watson, S. (2017). Bioenergetic dysfunction in ME/CFS. Metabolic Brain Disease, 32(2), 427–434.

31. Verma, D., Church, T. M., & Swaminathan, S. (2021). EBV lytic cycle and ACE2 upregulation: Implications for COVID-19. Journal of Virology, 95(6), e01984-20.

32. Wallukat, G., Hohberger, B., Wenzel, K., et al. (2021). Functional autoantibodies to GPCRs in persistent long COVID. Journal of Translational Autoimmunity, 4, 100100.

33. Wilson, H. W., Amo-Addae, M., Kenu, E., et al. (2016). Post-Ebola syndrome in Sierra Leone survivors. BMJ Global Health, 1(3), e000118.

Reports, Economic Analyses, and Organizational Summaries

34. Brookings Institution. (2023). The economic burden of long COVID: Implications for labor force participation and policy.

35. Solve M.E. (2022). ME/CFS and long COVID: Prevalence and economic burden.

36. Solve M.E. / Solve Long COVID Initiative. (2025). CD8+ T-cell over-activation and exhaustion in ME/CFS and long COVID: Synthesis report.

Lab, Center Pages, and Research News

37. #MEAction Network. (2024). Selin and Gil T-cell findings in ME/CFS (CD4+CD8+ elevation; CD8+ exhaustion) — research brief.

38. Cornell Chronicle. (2024). Long COVID study: Key CD8+ T-cell subsets show pronounced dysregulation consistent with exhaustion (research news).

39. Health Rising. (2025). Selin, Gil and Kumar: Systemic T-cell exhaustion as a unifying mechanism in ME/CFS and long COVID (expert interview and summary).

40. Selin Lab (UMass Chan). (2024). T-cell exhaustion markers in ME/CFS across heterogeneous triggers: Lab update.

Glossary

This glossary defines key terms and acronyms used in the white paper, drawing from standard medical and scientific sources for clarity and accuracy.

Term/Acronym	Definition
Autoimmune drift	Gradual immune dysregulation leading to autoantibodies that target self-tissues, often triggered by infections and contributing to chronic symptoms like dysautonomia (Wallukat 2021).
EBV reactivation	The resurgence of latent Epstein-Barr Virus (EBV), a herpesvirus, which can amplify post-infectious symptoms through inflammation and molecular mimicry; common in Long COVID and ME/CFS cohorts (Gold 2021; Bernal & Whitehurst 2023).
hEDS	Hypermobile Ehlers-Danlos Syndrome: A heritable connective tissue disorder characterized by joint hypermobility, skin fragility, chronic pain, and overlap with dysautonomia and MCAS (Hakim 2017).
HRV	Heart Rate Variability: A measure of the variation in time between heartbeats, reflecting autonomic nervous system balance; reduced HRV is linked to dysautonomia in IACCs (Raj 2020).
IACC	Infection-Associated Chronic Conditions: A spectrum of long-term, multi-system illnesses triggered by acute infections (viral, bacterial, or fungal), including ME/CFS, Long COVID, POTS, and MCAS; unified by shared mechanisms like immune dysregulation rather than isolated labels (Bateman Horne Center, 2025).
Long COVID	Post-acute sequelae of SARS-CoV-2 infection (PASC): Persistent symptoms (e.g., fatigue, dysautonomia) lasting beyond 4 weeks post-infection, often overlapping with other IACCs (Su 2022).
MCAS	Mast Cell Activation Syndrome: A condition where mast cells inappropriately release chemical mediators, causing multi-system flares (e.g., flushing, GI issues) in response to triggers like food or stress (Afrin 2020).
ME/CFS	Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A complex, debilitating illness marked by profound fatigue, PEM, cognitive impairment, and immune/autonomic dysfunction, often post-infectious (Jason 2010; IOM, 2015).
PEM	Post-Exertional Malaise: A hallmark symptom of ME/CFS and IACCs, involving delayed (24–72 h) worsening of symptoms after minimal physical/mental exertion (Tomas 2017).
PCT	Primary Chronic Trigger: The initiating event (e.g., infection, trauma) that shifts a vulnerable system into chronic instability, provoking outsized responses to routine inputs (custom framework in this white paper).
POTS	Postural Orthostatic Tachycardia Syndrome: A form of dysautonomia with ≥30

Author’s Note:

All insights, frameworks, and recommendations in this white paper reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models developed through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs).

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

Learn More: https://www.cynaera.com/systems

About the Author 

Cynthia Adinig is an internationally recognized systems strategist, health policy advisor, and the founder of CYNAERA, an AI-powered intelligence platform advancing diagnostic reform, clinical trial simulation, and real-world modeling for infection-associated chronic conditions (IACCs). She has developed 400+ Core AI Frameworks, 1 Billion + Dynamic AI Modules. including the IACC Progression Continuum™, US-CCUC™, and RAEMI™, which reveal hidden prevalence, map disease pathways, and close gaps in access to early diagnosis and treatment.

Her clinical trial simulator, powered by over 675 million synthesized individual profiles, offers unmatched modeling of intervention outcomes for researchers and clinicians.

Cynthia has served as a trusted advisor to the U.S. Department of Health and Human Services, collaborated with experts at Yale and Mount Sinai, and influenced multiple pieces of federal legislation related to Long COVID and chronic illness. 

She has been featured in TIME, Bloomberg, USA Today, and other leading publications. Through CYNAERA, she develops modular AI platforms that operate across 32+ sectors and 180+ countries, with a local commitment to resilience in the Northern Virginia and Washington, D.C. region.