The Human Variable 

The Va-IRI™ (Vaccination Immune Readiness Index) provides a structured 0–100 readiness score derived from common labs, symptom baselines, and patient function. Built on the STAIR Stable Method™ (Stabilization, Tolerance, And Immune Readiness), Va-IRI™ adapts low-and-slow principles pioneered by patients and refined through collaboration with leading researchers.

Using CYNAERA’s US-CCUC™ (U.S. Chronic Condition Undercount Correction – Pediatric Edition) methodology, we estimate 1.5–3 million U.S. children and adolescents and 10–20 million globally meet ME/CFS criteria in 2025.

Using CYNAERA’s recalibrated tiered prevalence model, weighted for environmental exposure, access to paid sick leave, and diagnostic inequity, we estimate that around 14.4 million Americans currently meet the diagnostic criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS).

The Global-CCUC™ (Chronic Condition Undercount Correction – Global) model provides a recalibrated framework. By weighting diagnostic suppression, environmental terrain, social protections, clinical awareness, and pandemic burden, it reveals a truer picture: 94–127 million conservative cases and 220–290 million upper-bound cases worldwide.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating, multi-system neuroimmune disease on par with multiple sclerosis or congestive heart failure in severity, but for decades, it’s been sidelined by global health systems. Underdiagnosis, outdated clinical criteria, systemic bias, and institutional inertia have obscured its true scale. The result: tens of millions worldwide excluded from diagnosis, care, or recognition.

A Nobel-scale leap, built outside the system. This isn’t just another CRISPR project. CYNAERA’s AI-powered gene editing platform was designed by a patient, for patients—modeling remission across millions of synthetic profiles to restore immune stability in infection-associated chronic conditions like Long COVID, ME/CFS, and MCAS. It cuts years off clinical trial timelines. It eliminates $5–10 million in early development costs. It’s scalable and ethical globally. AI meets bio