The Human Variable 

The CYNAERA ME/CFS Individualized Regimen Engine™ is an educational modeling framework that simulates phased strategies for ME/CFS care. Built on CYNAERA’s ecosystem of validated modules — including Treatment Archetypes, Phenotyping Framework, Pathophysiology Drivers, and the Path of Remission Model — the Regimen Engine demonstrates how individualized patient care can be systematically structured.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is profoundly sensitive to environmental stressors. Pollutants such as polycyclic aromatic hydrocarbons (PAHs), particulate matter (PM₂.₅, PM₁₀), nitrogen dioxide (NO₂), ground-level ozone (O₃), sulfur dioxide (SO₂), and volatile organic compounds (VOCs), combined with weather triggers like heat, cold, barometric pressure swings, mold, humidity, and pollen, drive flares that push patients into cycles of severe disabi

CYNAERA has the first clinical intelligence system designed to simulate and stratify patient response across the full complexity of infection-associated chronic conditions (IACCs) such as ME/CFS, Long COVID, POTS, MCAS, EDS, Lyme, CRPS, and related syndromes. Unlike traditional models that flatten patients into crude categories, CYNAERA builds individualized profiles across 11 stratification axes and integrates them with six mechanistic phenotyping domains.

The Cultural Gendered Patriarchy Index (CGPI), developed by CYNAERA (2025), is the first cross-national system designed to quantify the effect of patriarchy on male diagnostic rates. By integrating data from 180 countries—including global gender indices, masculinity scales, health-seeking behavior datasets, and IACC prevalence studies—we reveal that millions of men are statistically invisible.

CYNAERA’s PHAROS™ + REWIRE™ framework introduces a longitudinal, multi-pathway diagnostic system. By tracking cytokine rhythms over days, mapping vagal-histaminergic amplification, and integrating viral reactivation profiles, PHAROS™ + REWIRE™ replaces linear models with terrain-aware detection. This paper expands on the biological foundations of this model, critiques failed legacy approaches, and outlines a replicable path to mechanism-specific, real-world diagnostics.

Using CYNAERA’s US-CCUC™ model, we present a corrected demographic landscape grounded in published science, epidemiological data, and terrain-calibrated prevalence modeling. For the first time, undocumented immigrant populations and ancestral dietary biology are incorporated as visible drivers of disease prevalence. Our analysis demonstrates that half of Americans with ME/CFS are non-white, and that prevalence in certain groups is likely higher when adjusted for baseline into