The Human Variable 

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

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).

CYNAERA’s US-CCUC™ (Chronic Condition Undercount Correction – U.S.) model provides a corrected formula for estimating the real burden of ME/CFS. It builds on pre-pandemic cases, integrates the Long COVID surge, and corrects for the 80–90% of patients who were always there — but never diagnosed. Whether you lean on the cautious estimate of ~9.5 million Americans or the more realistic ~21.5 million, the message is the same: ME/CFS is a condition that rivals the scale of diabete

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 Underdiagnosis, outdated clinical criteria, systemic bias, and institutional inertia have obscured its true scale.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.

They called your symptoms anecdotal. They called your story rare. But inside your medical records, flare journals, and sleepless nights is the dataset no one bothered to decode. This isn’t just a blog post, it’s a call to reclaim your role as more than a subject. Inside, we share how a self-paced case study course helps patients and caregivers turn lived experience into lasting change.

Les Affections Chroniques Associées à une Infection (IACCs), y compris le Covid Long, l'Encéphalomyélite Myalgique/Syndrome de Fatigue Chronique (EM/SFC), le Syndrome de Tachycardie Orthostatique Posturale (POTS), le Syndrome d'Activation des Mastocytes (SAMA), les Affections Pédiatriques PANS/PANDAS et les syndromes post-infectieux persistants tels que le Lyme chronique, émergent de perturbations systémiques qui se chevauchent

AI hallucinations aren’t a quirk, they are a liability. You have seen the headlines about chatbots reinforcing delusions, inventing facts, and flattering users into risk. That is why my CYNAERA method turns lived patterns into public math, then runs a double blind, multi-model gauntlet before anything touches a client.

The CDF-ME framework captures between 88–94% of patients who meet International Consensus Criteria (ICC), Canadian Consensus Criteria (CCC), or the National Academies of Sciences, Engineering, and Medicine (NASEM) diagnostic criteria, regardless of whether their illness was triggered by infection, vaccination, physical trauma, or environmental exposure. Validated through CYNAERA’s AI engine and over 200,000 patient simulations, CDF-ME integrates high-specificity markers .

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

Infection-associated chronic conditions (IACCs) such as Long COVID, ME/CFS, POTS, and MCAS are not disparate diagnoses — they are systemic sequelae following immune injury. For too long, medicine has treated these illnesses as anomalies or mysteries, when in fact, they follow a biologically predictable pattern of dysregulation.