Primary Chronic Trigger (PCT)

Oct 8, 2025
19 min read

Updated: 3 days ago

A Mathematical Blueprint for Infection Associated Chronic Condition Onset

Executive Summary

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. Names vary across infection associated chronic conditions ( IACC) cohorts such as Long COVID, ME/CFS, POTS, MCAS, hypermobility and connective tissue overlap, and post-infectious states after EBV or Lyme. The downstream biology shows clear convergence in immune dysregulation, autonomic instability, mitochondrial hypometabolism, mast-cell hypersensitivity, and connective-tissue fragility, with a measurable autoimmune subset (Hickie 2006; Raj 2020; Afrin 2020; Wallukat 2021). Independent immunology reports CD8 activation with exhaustion features and elevations in CD4+CD8+ cells in ME/CFS and Long COVID. EBV activity frequently co-travels after SARS-CoV-2 and plausibly links to autoimmunity through inflammatory load and molecular mimicry (Klein 2023; Su 2022; Bjornevik 2022; Verma 2021).

This white paper separates detection from burden. Layer 1 detection uses the PCT Index, a six-domain, normalized 0 to 10 score that estimates whether a PCT occurred, assigns confidence, and chooses a class. The domains are Temporal ignition, Delayed reactivity in the 24 to 72 hour window, Early 14 day window, timed Biomarker corroboration, Objective exposome context, and Recovery conditions. Classes include Infectious, Environmental, Surgical or Trauma, Toxicant mix, and Unknown. Layer 2 burden models why some people and communities carry heavier long-tail illness after a PCT. It uses three modulators that scale prevalence and severity at population and personal levels: Viral burden or reactivation, Environmental toxic load with climate volatility, and Recovery suppression.

This framework aligns with the IACC Terrain framework and the Bioadaptive Systems Therapeutics™ (BST) systems map. Branch biology in IACC is downstream expression after ignition and includes autonomic, mast cell, mitochondrial, autoimmune, and connective tissue domains. BST keeps hormone–immune timing, neuroplastic load, environment, and viral activity explicit as therapy modulators. Harmonized phrases are used so the same construct is named the same way across papers. The population and personal formulas are transparent and can be calibrated to public estimates. The goal is to provide an auditable ignition language that is simple enough for method sections and surveillance dashboards and precise enough to reduce category errors between trigger detection and chronic burden.

Text on teal background: "The Primary Chronic Trigger (PCT) Model Definition." White silhouette head with gear and lightning bolt icons. By CYNAERA

Key Terms

Primary Chronic Trigger (PCT): 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.
NRB (Non-Return to Baseline): A sustained post-trigger state in which pre-trigger homeostasis is not recovered.
Delayed Reactivity (DR): The characteristic 24 to 72 hour window for post-provocation worsening seen in post-exertional malaise and autonomic flares.
IACC (Infection-Associated Chronic Conditions): Convergent chronic phenotypes that follow infectious or environmental PCTs.
Five IACC Branches (Biology): Autonomic, mast cell, mitochondrial, autoimmune, connective tissue.
BST Seven Terrains (Systems map): Immune, autonomic, mitochondrial, hormone–immune, neuroplastic, environmental, viral.
PCT Index (PCTi): A six-domain, normalized score on a 0 to 10 scale used for PCT detection and confidence.
Layer 2 Burden Modulators: Viral burden or reactivation (V), environmental toxic load plus climate volatility (ENV/CLIM), and recovery suppression (RS).
ADLs: Activities of daily living.
US-CCUC: U.S. Chronic-Condition Undercount model and estimates.

What is a PCT?

The initiating biological or environmental event that shifts a vulnerable system into a new, unstable non-return to baseline (NRB)

Examples include:

Infectious: SARS-CoV-2, Epstein–Barr virus (EBV), influenza, West Nile virus, Borrelia burgdorferi (Lyme), Group A Streptococcus in PANDAS or PANS cohorts.
Environmental: Damp or mold-contaminated housing with mycotoxins, volatile organic compounds, wildfire smoke and particulate matter, complex urban pollution plumes.
Surgical or Trauma: Major procedures or injuries that produce physiologic jolts in terrain-fragile hosts.
Toxicant mix: Combined or sequential exposures, for example smoke plus heat plus VOCs.

A PCT is not a diagnosis. It is the ignition. The clinical phenotype that follows expresses through shared downstream biology. In IACC staging the five branches are autonomic, mast cell, mitochondrial, autoimmune, and connective tissue.

Evidence anchors: Post-infective cohorts and exposure studies describe time-proximal ignition, early multisystem shifts, and delayed reactivity windows that predict chronic trajectories (Hickie 2006; Rowe 2014; Tomas 2017; Raj 2020; Brewer 2013; Baxter 2021). Immune studies in ME/CFS and Long COVID report CD8 over-activation with exhaustion features and elevations in CD4+CD8+ cells consistent with persistent antigenic drive; EBV reactivation co-travels with symptoms and can bridge to autoimmunity via molecular mimicry (Klein 2023; Su 2022; Bjornevik 2022; Verma 2021).

Purpose of the PCT Framework

To prevent category errors, the framework cleanly separates:

Layer 1, Detection: Did a PCT occur, with what confidence, and of what class. Answered by the PCT Index (PCTi) using six domains: T, DR, E14, BIO, Xobj, RC.
Layer 2, Burden: Why some people and communities carry heavier long-tail illness after a PCT. Answered by three burden modulators: V, ENV/CLIM, RS.

Downstream branches are biology after ignition, not detection variables.

Causal Framework (ASCII)

[Trigger Event]

(infection, exposure, procedure, trauma)

[Primary Chronic Trigger (PCT) -> Layer 1 detection via PCTi]

[Non-Return to Baseline (NRB)]

|--> Layer 2 burden modulators: V | ENV/CLIM | RS

[IACC Burden (population and personal)]

The Nine Variables Across Both Layers

Layer 1, PCTi: Six Domains

T: Temporal ignition, 0 to 3
DR: Delayed reactivity 24 to 72 hours, 0 to 3
E14: Early 14 day window, 0 to 3
BIO: Timed biomarker corroboration, 0 to 3
Xobj: Objective exposome context, 0 to 2
RC: Recovery conditions, 0 to 2

Layer 2, Burden: Three Modulators

V: Viral burden or reactivation index, 0 to 1
ENV/CLIM: Environmental toxic load plus climate volatility composite, 0 to 1
RS: Recovery suppression, a structural or socioeconomic load index, 0 to 1

Important: Branch biology is not part of detection. It is the downstream expression after the PCT.

PCT Index (PCTi) Formula, Layer 1, v1.0 Public Spec

Weighted raw score, range 0.00 to 3.10

PCTi_raw = 0.35·T + 0.20·DR + 0.20·E14 + 0.15·BIO + 0.10·(Xobj + RC)

Normalized score, range 0.0 to 10.0

PCTi 10 = round(10 × PCTi_raw / 3.10, 1)

Confidence thresholds on the normalized scale

Definite PCT: PCTi 10 ≥ 8.0 and T ≥ 2 and DR ≥ 2
Probable PCT: 6.0 to 7.9 with at least two domains scored 2 or higher
Possible PCT: 4.0 to 5.9
Indeterminate: PCTi 10 < 4.0, keep “Unknown PCT,” reassess

Simple cross-check (sum, range 0 to 16)

Sum_16 = T + DR + E14 + BIO + Xobj + RC

Definite ≥ 12; Probable 9–11; Possible 6–8; Indeterminate < 6

Class assignment

Assign one of: Infectious, Environmental, Surgical or Trauma, Toxicant mix, Unknown. Tie-breaks favor higher T, then higher DR.

Layer 1 Domain-by-Domain Evidence and Scoring

T, Temporal ignition, 0 to 3

Why: A discrete, time-bound event anchors causal inference in post-infective and exposure cohorts.

Score: 0 none; 1 present but timing unclear; 2 within 0 to 6 weeks of NRB; 3 discrete day-level onset preceding NRB.

Evidence: Hickie 2006; Rowe 2014; Raj 2020.

Common pitfalls: Missing “minor” infections; recall bias; anchoring only to severe events.

DR, Delayed reactivity 24 to 72 hours, 0 to 3

Why: The 24 to 72 hour window for post-exertional malaise and autonomic flares is a robust signature across cohorts.

Score: 0 none; 1 mild next-day dip; 2 consistent delayed flares after effort, heat, meals, or stress; 3 severe crashes limiting ADLs.

Evidence: Rowe 2014; Castro-Marrero 2016; Tomas 2017.

Common pitfalls: Confusing same-day fatigue with delayed reactivity and failing to collect delayed endpoints.

E14, Early 14 day window, 0 to 3 Why: New multisystem patterns within two weeks predict NRB trajectories.

Score: 0 none; 1 one or two new symptoms without functional drop; 2 new multisystem pattern or functional drop; 3 marked change with care-seeking.

Evidence: Montoya 2018; Raj 2020.

Common pitfalls: Missing subtle autonomic, sleep, or thermoregulatory shifts.

BIO, Timed biomarker corroboration, 0 to 3

Why: Time-aligned markers such as EBV qPCR or early antigen, acute PCR or serology, or moisture and VOC metrics strengthen class fidelity.

Score: 0 none or negative; 1 inconclusive or untimed; 2 timed marker supports class; 3 multiple concordant markers or repeat positives.

Evidence: Su 2022; Bernal and Whitehurst 2023; Brewer 2013.

Common pitfalls: Untimed testing and over-weighting nonspecific labs.

Xobj, Objective exposome context, 0 to 2

Why: Particulate exposure, damp housing and mold, VOCs, and smoke plumes elevate autonomic and mast cell load. Score: 0 no exposure data; 1 minor exposure or low air-quality index; 2 strong context such as sustained PM spikes, damp housing metrics, or occupational VOCs.

Evidence: Baxter 2021; Brewer 2013.

Common pitfalls: Treating environment as a confounder and not logging housing or air history.

RC, Recovery conditions, 0 to 2

Why: Sleep debt, care denial, unsafe housing, and uncompensated caregiving extend chronicity.

Score: 0 protected recovery; 1 mixed support; 2 high load such as work or caregiving or sustained sleep disruption. Evidence: Baxter 2021.

Common pitfalls: Assuming equal recovery capacity and ignoring leave or access context.

Data-Capture Minimums for Reproducible PCTi

Trigger timeline: Day-level grid from 14 days before to 14 days after the suspected ignition.
Delayed endpoints: Symptom prompts at 0, 24, 48, and 72 hours after a routine activity that represents a typical provocation.
Objective environment: Local AQI and particulate data; basic moisture or VOC inspection notes if relevant.
Recovery load: Leave status, caregiving responsibilities, housing stability, and sleep disruption.

The Nine Variables: Selection Rationale

Why these six PCTi domains? They capture time-proximal, causal signals that distinguish PCTs from background noise and were chosen for reproducibility in clinics, surveillance, and research. Timing, a delayed window, early multisystem patterns, timed corroboration, objective exposures, and real recovery capacity all recur across post-infective and environmental cohorts (Hickie 2006; Rowe 2014; Tomas 2017; Raj 2020; Baxter 2021; Brewer 2013; Su 2022).

Why these three burden modulators? They scale post-PCT illness by accounting for ongoing antigenic, environmental, and structural pressures. V tracks persistent or reactivated virology, ENV/CLIM tracks exposure intensity and volatility, and RS captures the structural capacity to recover. This aligns with IACC’s branch biology and BST’s explicit therapy overlays.

Per-variable rationale

T: Discrete ignition improves causal inference and reduces recall drift.
DR: The 24 to 72 hour lag is distinctive for post-exertional and autonomic phenomena and is measurable with simple diaries.
E14: Early multisystem signals bridge the trigger to later branch dominance.
BIO: Timed markers increase class fidelity and reduce sole reliance on symptoms.
Xobj: External load is measurable and often decisive in both ignition and amplification.
RC: Recovery capacity is unevenly distributed and matters for chronicity.
V: Persistent antigenic drive commonly co-travels with symptoms and explains burden variation.
ENV/CLIM: Heat, smoke, dampness, VOCs, and urban PM patterns amplify autonomic and inflammatory load and vary by season and location.
RS: Leave, caregiving, and housing conditions predict prolonged NRB and reflect equity considerations.

Layer 2 Burden Modulators: Definitions and Inputs

V, Viral burden or reactivation, 0 to 1: Inputs include community case waves, reinfection rates, and EBV or HHV-6 markers. Role is to increase persistent antigenic drive and interact with DR and BIO timing.
ENV/CLIM, Environmental toxic load plus climate volatility, 0 to 1: Inputs include PM2.5, smoke days, VOC indices, dampness or flood exposure, and heatwave counts. Role is to elevate autonomic and mast cell load and increase flare frequency and severity.
RS, Recovery suppression, 0 to 1: Inputs include paid leave coverage, shift scheduling and caregiving load, housing instability, and sleep deficits. Role is to extend NRB and chronicity.

Population and Personal Burden Formulas

Preferred composite population form

IACC_Risk(t) = B × (1 + V(t)·β1 + ENV/CLIM(t)·β2 + RS(t)·β3)

Where B is baseline predisposition in the population, for example 0.12 to 0.15, and β1 to β3 are amplification coefficients estimated from observed data such as survey and claims syntheses and exposure indices.

Legacy split form for continuity

IACC_Risk(t) = B × (1 + V(t)·β1 + E(t)·β2 + C(t)·β3 + R(t)·β4)

Equivalence note: After re-estimation, β2′·ENV/CLIM approximates β2·E plus β3·C, and RS corresponds to R. Once coefficients are refit, the two forms produce equivalent forecasts.

Personal form

IACC_Risk(person) = Bi × (1 + Vi·β1 + ENV/CLIMi·β2 + RSi·β3)

Where Bi reflects individual fragility such as connective tissue architecture or prior immune hits, and the modulators are scaled to the 0 to 1 range.

Interactions and Dynamics

Stacked PCTs: A second trigger at least four weeks later that raises PCTi 10 by at least 1.0 constitutes a stacked PCT or amplifier. Keep both on record.
Coupling: V interacts with BIO, E14, and DR because viral waves and reactivation strengthen the delayed window and raise class confidence when timed biomarker support appears. ENV/CLIM interacts with Xobj and RC because high exposures combined with poor recovery conditions raise symptom variance and persistence.
Time updating: Recompute PCTi around major events such as surgery, new infection, or relocation. Update burden modulators with seasonal exposure shifts and new public data.

Worked Examples

All examples use the v1.0 public PCTi weights and the normalized scale PCTi 10 equals 10 × PCTi_raw / 3.10 rounded to one decimal.

Example 1, Infectious PCT with EBV activity near a COVID wave

Inputs, Layer 1: T 3, DR 3, E14 2, BIO 2, Xobj 1, RC 2
Compute: PCTi_raw equals 0.35×3 plus 0.20×3 plus 0.20×2 plus 0.15×2 plus 0.10×(1+2) equals 2.65
Normalize: PCTi 10 equals 10 × 2.65 ÷ 3.10 equals 8.5
Confidence and class: Definite PCT because PCTi 10 is at least 8.0 and T and DR are at least 2. Infectious class supported by BIO timing.

Population burden snapshot, same period

Inputs: B 0.15; V 0.30; ENV/CLIM 0.20; RS 0.25; β1 1.2; β2 0.8; β3 0.9
Amplifier: 1 plus 0.36 plus 0.16 plus 0.225 equals 1.745
IACC_Risk: 0.15 × 1.745 equals 0.2618 or 26.2 percent

Example 2, Environmental PCT with damp plus smoke

Inputs, Layer 1: T 2, DR 2, E14 2, BIO 1, Xobj 2, RC 2
Compute: PCTi_raw equals 0.35×2 plus 0.20×2 plus 0.20×2 plus 0.15×1 plus 0.10×(2+2) equals 2.05
Normalize: PCTi 10 equals 10 × 2.05 ÷ 3.10 equals 6.6
Confidence and class: Probable PCT. Environmental class supported by high Xobj and inspection evidence.

Population burden snapshot, heavy smoke period

Inputs: B 0.15; V 0.10; ENV/CLIM 0.40; RS 0.30; β1 1.2; β2 0.8; β3 0.9
Amplifier: 1 plus 0.12 plus 0.32 plus 0.27 equals 1.71
IACC_Risk: 0.15 × 1.71 equals 0.2565 or 25.7 percent

Calibration and Sensitivity

Goal: Choose β coefficients so model outputs match observed totals from sources such as US-CCUC estimates across time and regions.

Inputs and features: Targets include monthly or quarterly IACC prevalence from survey and claims syntheses. Features include V, ENV/CLIM, and RS indices by region and time, scaled to 0 to 1 or standardized. Baseline B often ranges from 0.12 to 0.15 and may vary by ancestry, age, and connective tissue architecture.

Procedure: Assemble a panel of regions by time with targets and features. Fit β coefficients using constrained regression with non-negative priors or Bayesian half-normal priors. Back-test with rolling windows and compute mean absolute error and mean absolute percentage error. For the equivalence check, verify that β for ENV/CLIM approximates the split coefficients on held-out data. Document priors with citations that link each modulator to burden.

FAQ

Why use a 0 to 10 normalized scale for PCTi 10 instead of the raw 0 to 3.10? Human classification is easier on a 10-point scale and the thresholds map cleanly to Definite, Probable, and Possible. The math is identical.

Is the environment a confounder? No. In this frame ENV/CLIM co-determines outcomes. Treat it as a burden modulator and a target for prevention.

Why exactly these six domains in the PCTi? They reflect consistent near-event evidence across post-infective and exposure cohorts: timing, the delayed window, early patterns, timed corroboration, objective exposures, and recovery capacity.

Where do autoimmunity, mitochondria, and dysautonomia fit? They are downstream branch biology. PCTi answers whether a PCT occurred and of what class. It does not stage branch dominance.

How do Proal and VanElzakker’s ideas relate? Their synthesis foregrounds persistent immune perturbation and neuroimmune loops in post-viral states. PCT formalizes the ignition, the time-proximal evidence, and explicit burden modulators as auditable inputs.

Harmony Annex

A. Core phrases to use verbatim

Triggers differ; downstream biology converges.
Environment is a co-intervention, not a confounder.
Primary Chronic Trigger, PCT, is the initiating event that flips a vulnerable system into a new, unstable baseline such that routine inputs such as effort, food, air, hormones, or stress provoke outsized and often delayed reactions.

B. Branch and terrain counts

IACC, five branches, biologic: Autonomic, mast cell, mitochondrial, autoimmune, connective tissue.
BST, seven terrains, systems and therapy: Immune, autonomic, mitochondrial, hormone–immune, neuroplastic, environmental, viral. In IACC papers, the last four operate as modulators rather than branches. In BST papers they remain explicit to engineer remission across systems.

C. Crosswalk, text table

PCT (Detection) -> IACC Branching (Biology) -> BST Terrains (Systems)

Temporal, DR, E14, BIO, Autonomic / Mast cell / Mitochondrial Immune / Autonomic / Mitochondrial /

Xobj, RC / Autoimmune / Connective tissue Hormone–Immune / Neuroplastic /

Environmental / Viral

Layer 2: V, ENV/CLIM, RS -> Not branches; burden scalers -> Tracked as explicit terrains or overlays

Appendix: Figures

Figure A1, PCTi Rubric, six domains and ranges

Domain	0	1	2	3
T	No identifiable trigger	Trigger, timing unclear	Trigger within 0 to 6 weeks of NRB	Discrete day-level onset preceding NRB
DR, 24 to 72 hours	No delayed worsening	Mild next-day dip	Consistent delayed flares after effort, heat, meals, or stress	Severe crashes limiting ADLs
E14	No new symptoms	One to two symptoms without function drop	New multisystem pattern or function drop	Marked change with care-seeking
BIO	None or negative	Inconclusive or untimed	Timed marker supports class	Multiple concordant or repeat positives
Xobj	No exposure data	Minor exposure or low AQI	Strong context such as damp housing, sustained particulate spikes, or VOC metrics	—
RC	Protected recovery	Mixed support	High work or caregiving load or persistent sleep loss	—

Figure A2, Detection workflow, ASCII

Trigger timeline

-> Score T, DR, E14, BIO, Xobj, RC

-> Compute PCTi_raw

-> Normalize to PCTi 10

-> Classify: Definite, Probable, Possible, Indeterminate

-> Assign class: Infectious / Environmental / Surgical or Trauma / Toxicant mix / Unknown

Figure A3, Burden modulators, definitions and scaling

V: 0 to 1, inputs include case or serology waves, reinfections, and EBV or HHV-6 markers.
ENV/CLIM: 0 to 1, inputs include PM2.5, dampness or VOC indices, smoke days, heatwaves, and flood recovery.
RS: 0 to 1, inputs include paid leave coverage, shift or caregiving load, housing instability, and sleep deficits.

Figure A4, Equivalent population forms

Composite: IACC_Risk = B × (1 + V·β1 + ENV/CLIM·β2 + RS·β3)

Split: IACC_Risk = B × (1 + V·β1 + E·β2 + C·β3 + R·β4)

Equivalence after re-estimation: β2′·ENV/CLIM ≈ β2·E + β3·C and RS ≡ R

Figure A5, Early and delayed windows, ASCII timeline

Day 0: Trigger

Days 0 to 14: E14 signals (sleep, thermoregulation, autonomic shifts, new multisystem pattern)

Provocation -> 0 h / 24 h / 48 h / 72 h prompts -> DR scoring for PEM and autonomic expression

Conclusion

The Primary Chronic Trigger framework provides a clear separation between detecting the ignition event and explaining the burden that follows. Layer 1 pinpoints when a PCT likely occurred, assigns confidence on a 0 to 10 scale, and selects a class using six time-proximal domains that recur across post-infective and exposure cohorts. Layer 2 explains unequal burden after ignition by modeling viral drivers, environmental and climate load, and recovery suppression. This structure clarifies what belongs in detection and what belongs in downstream biology.

In this framing, autonomic dysregulation, mast-cell hypersensitivity, mitochondrial hypometabolism, autoimmune drift, and connective-tissue fragility are branch mechanisms that express after ignition. They are not part of PCT detection. In BST, hormone–immune timing, neuroplastic load, environment, and viral activity are kept explicit because they shape how remission is engineered. In IACC staging they act as modulators rather than additional branches. Shared phrasing across frameworks prevents confusion and keeps the counts consistent.

The math is practical. PCTi can be scored with routine timelines, delayed symptom prompts, and timed markers. The burden modulators can be fed by public data on infections, particulate exposure, heat and smoke days, moisture and VOC signals, and administrative indicators of recovery capacity. Coefficients can be calibrated to public prevalence syntheses and evaluated with rolling back-tests.

The evidence base supports each step. Prospective post-infective cohorts justify the early window. PEM and orthostatic signatures justify delayed endpoints. EBV and HHV-6 activity as well as T-cell exhaustion features support infectious classes. Urban particulate exposure, damp housing, wildfire smoke, heatwaves, and flood recovery plausibly amplify burden. Recovery suppression such as lack of leave, uncompensated caregiving, and unsafe housing predicts longer chronicity. Where uncertainty persists, the framework is explicit about assumptions and highlights which timing-sensitive studies can reduce it.

In short, triggers differ and downstream biology converges. PCT gives a shared ignition language that is auditable and quantitative. It is harmonized with IACC branch biology and the BST systems map, and it is ready to be used in dashboards, prevalence forecasting, and method sections. Clinical protocols and policy mechanics remain in their own papers. The intended result is fewer category errors, faster signal accumulation, and a common foundation for prevention in the communities that bear the highest exposure burdens.

CYNAERA Frameworks Referenced in This Paper

This paper draws on a defined subset of CYNAERA Institute white papers that establish the methodological and analytical foundations of CYNAERA’s prevalence correction frameworks. These publications provide deeper context on prevalence reconstruction, diagnostic suppression, population correction, and disease-burden modeling approaches referenced in this analysis.

Author’s Note:

All insights, frameworks, and recommendations in this written material 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.

Patent-Pending Systems

Bioadaptive Systems Therapeutics™ (BST) and all affiliated CYNAERA frameworks, including Pathos™, VitalGuard™, CRATE™, SymCas™, TrialSim™, and BRAGS™, are protected under U.S. Provisional Patent Application No. 63/909,951.

Licensing and Integration

CYNAERA partners with universities, research teams, federal agencies, health systems, technology companies, and philanthropic organizations. Partners can license individual modules, full suites, or enterprise architecture. Integration pathways include research co-development, diagnostic modernization projects, climate-linked health forecasting, and trial stabilization for complex cohorts. You can get basic licensing here at CYNAERA Market.

Support structures are available for partners who want hands-on implementation, long-term maintenance, or limited-scope pilot programs.

About the Author

Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with Selin Lab for t cell research at the University of Massachusetts.

Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.

Through CYNAERA, she develops modular AI platforms, including the IACC Progression Continuum™, Primary Chronic Trigger (PCT)™, RAVYNS™, and US-CCUC™, that are made to help governments, universities, and clinical teams model infection-associated conditions and improve precision in research and trial design. US-CCUC™ prevalence correction estimates have been used by patient advocates in congressional discussions related to IACC research funding and policy priorities. Cynthia has been featured in TIME, Bloomberg, USA Today, and other major outlets, for community engagement, policy and reflecting her ongoing commitment to advancing innovation and resilience from her home in Northern Virginia.

Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed perspective to her research and policy initiatives.

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Raj, S. R., Guzman, J. C., Harvey, P., et al. (2020). Postural tachycardia syndrome: Features, diagnosis, and management. Circulation, 141(12), 100–106.
Rowe, P. C., Barron, D. F., Calkins, H., Maumenee, I. H., Tong, P. Y., & Geraghty, M. T. (2014). Orthostatic intolerance and chronic fatigue syndrome in adolescents. Pediatrics, 113(3), 429–435.
Su, Y., Yuan, D., Chen, D. G., et al. (2022). Multiple early factors anticipate post-acute COVID-19 sequelae. Cell, 185(5), 881–895.
Swedo, S. E., Frankovich, J., & Murphy, T. K. (2012). Overview of PANS and PANDAS and clinical management. Journal of Child and Adolescent Psychopharmacology, 22(6), 450–453.
Theoharides, T. C. (2019). Mast cells and inflammation. Pharmacology and Therapeutics, 201, 1–12.
Tomas, C., Newton, J., & Watson, S. (2017). Bioenergetic dysfunction in ME/CFS. Metabolic Brain Disease, 32(2), 427–434.
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.
Wallukat, G., Hohberger, B., Wenzel, K., et al. (2021). Functional autoantibodies to GPCRs in persistent long COVID. Journal of Translational Autoimmunity, 4, 100100.
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 and organizational summaries
Brookings Institution. (2023). The economic burden of long COVID: Implications for labor force participation and policy.
Institute of Medicine. (2015). Beyond Myalgic Encephalomyelitis or Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press.
Solve M.E. (2022). ME/CFS and long COVID: Prevalence and economic burden.
Solve M.E. and Solve Long COVID Initiative. (2025). CD8+ T-cell over-activation and exhaustion in ME/CFS and long COVID: Synthesis report.

Research briefs and news
#MEAction Network. (2024). Selin and Gil T-cell findings in ME/CFS: CD4+CD8+ elevation and CD8+ exhaustion.
Cornell Chronicle. (2024). Long COVID study: Key CD8+ T-cell subsets show pronounced dysregulation consistent with exhaustion.
Health Rising. (2025). Selin, Gil, and Kumar: Systemic T-cell exhaustion as a unifying mechanism in ME/CFS and long COVID.
Selin Lab, UMass Chan. (2024). T-cell exhaustion markers in ME/CFS across heterogeneous triggers: Lab update.
Bateman Horne Center. (2025). IACC overview and clinical education resources.

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