The Human Variable 

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.

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

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 Underdiagnosis, outdated clinical criteria, systemic bias, and institutional inertia have obscured its true scale.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

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 .