CYNAERA Institute - MECFS Library
Analyzing One of the Most Misunderstood Illnesses of Our Time
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) affects millions, yet remains underdiagnosed, underfunded, and uncounted from public health infrastructure. CYNAERA’s ME/CFS research library offers the most advanced visibility framework in the world, designed to correct data suppression, diagnostic failures, and structural dysfunction in real time.
Explore how CYNAERA’s AI engines, predictive algorithms, and structural tools are transforming what we know about ME/CFS and who is being left out of diagnosis, treatment and research globally.
ME/CFS Research Library
Global Prevalence Index & Tier Weighting System
US Focused Prevalence
Demographic Factors
MECFS Catalyst, Progression and Flares
Study Design & Stratification
Treatments and Therapeutics
Tech + Blood-Based Diagnostics
Pathways to Remission
Cure Pathways
Why CYNAERA Chose ME/CFS First
CYNAERA wasn’t built for healthcare. It was built to correct broken logic across systems that erase the unseen.
ME/CFS was the clearest entry point:
A condition misunderstood for decades
A research field stalled by outdated models
A community forced to generate its own science in the absence of institutional support
We started here because ME/CFS is the archetype of what happens when illness doesn’t fit the mold. It revealed the failures across public health, research, care infrastructure, and policy, while also showing what becomes possible when patients lead.
This is not a library of documents. It's an operational blueprint for reform layered with predictive models, diagnostic tools, and structural insight built from the ground up.
This began as a commitment to those who waited too long for science to catch up. It's a system designed not just to represent them, but to ensure no one else is left waiting.
Upcoming Library Expansions
CYNAERA is actively expanding into adjacent terrain conditions where diagnostic erasure and systemic bias mirror ME/CFS:
Long COVID
US prevalence correction, diagnostics, flare mapping, and cancer risk modeling.
POTS (Postural Orthostatic Tachycardia Syndrome)
Genetic vs. infection-onset logic, school dropout forecasts, and access mapping.
MCAS (Mast Cell Activation Syndrome)
Air quality interaction modules, anaphylaxis risk terrain, and treatment audits.
PANS/PANDAS
Pediatric neuroimmune modeling, psychiatric misdiagnosis detection, and IEP pipeline mapping.
Fibromyalgia & Gulf War Illness
Cluster mapping, symptom fingerprinting, and VA-specific diagnostic logic.