CYNAERA Complex Patient Stratification Capacity

Updated January 2026

Overview

CYNAERA is a clinical intelligence system designed to simulate and stratify patient response across the full complexity of infection-associated chronic conditions (IACCs), including ME/CFS, Long COVID, POTS, MCAS, EDS, Lyme disease, CRPS, and related multisystem syndromes.

The system is built on a modular intelligence architecture composed of 1,000+ core intelligence frameworks that generate and govern more than 5 billion dynamic intelligence modules. These modules encode mechanistic logic, environmental modifiers, access constraints, and response rules that can be recombined across disease domains and populations. Within this architecture, CYNAERA supports over 1 billion chronic-condition digital twins, each representing a distinct patient-state model simulated across time, context, and intervention scenarios.

Early CYNAERA materials referenced 675 million recovery paths within infection-associated chronic conditions. That figure remains correct. It reflects the first fully validated IACC configuration, derived from a fixed set of stratification axes and mechanistic domains. As the system matured, that configuration was recognized not as a ceiling, but as a baseline floor within a larger, modular architecture.

Within the current IACC configuration, combining 11 patient stratification axes with six mechanistic phenotyping domains now supports hundreds of millions of distinct, non-redundant recovery, stabilization, and deterioration pathways, with the originally published 675 million paths fully contained within this expanded space.

“Precision care demands massive granularity. What once looked like outliers were simply profiles no one had the system to stratify.”

What Defines a Patient Profile

In CYNAERA, a patient profile is not a label or diagnosis. It is a structured, combinatorial representation of flare behavior, physiological vulnerability, access context, and environmental exposure.

The 11 stratification axes include:

• Age group

• Sex• Core phenotype

• Sensitivity tier• Primary trigger cluster

• Comorbid profile (IACCI-focused)

• Health access modifier

• Cycle or hormone phase

• Geographic and environmental risk

• Autoimmune status

• Duration of illness

Stratification defines who the patient is and the context shaping their risk, including care access, housing stability, environmental exposures, and disease chronicity. Phenotyping addresses a separate dimension: what biological systems are disrupted and how those disruptions behave over time.

Mechanistic Phenotyping Domains

CYNAERA models six core biological domains that drive symptom persistence, flare dynamics, and treatment response in IACCs:

1. • Energy metabolism and mitochondrial support

2. • Neuroimmune signaling and modulation

3. • Autonomic regulation and instability

4. • Viral persistence and reactivation control

5. • Immune recalibration pathways

6. • Oxygenation dynamics and recovery capacity

By separating stratification from phenotyping, CYNAERA preserves patient heterogeneity rather than collapsing it. Patients with the same diagnosis may occupy entirely different risk landscapes, follow different flare trajectories, and respond differently to the same intervention. CYNAERA models those differences explicitly.

Geographic and Environmental Risk Integration

Environmental exposure is treated as a first-order clinical variable within CYNAERA’s stratification logic. Patient profiles incorporate geographic and climate-linked modifiers, including:

• Urban air pollution• Mold-prone housing regions

• Wildfire smoke exposure

• High pollen environments

• High humidity conditions

• Coastal salt air exposure

• High-altitude physiology

• Low-AQI protective environments

This enables forecasting not only of who is vulnerable, but where destabilization is most likely and when risk peaks occur. As climate volatility increasingly drives flare cycles in chronic illness, static risk models fail. CYNAERA’s modeling is explicitly environment- and time-aware.

Why This Matters

Most existing clinical systems stratify patients using age, sex, and a single primary diagnosis. This approach systematically omits the true drivers of instability in IACCs:

• Comorbidity stacking, such as ME/CFS with POTS and MCAS

• Environmental reactivity, including wildfire smoke–triggered PEM

• Hormonal phase effects that shift flare timing and severity

• Autonomic and sensory hypersensitivity to exertion, light, sound, and temperature

CYNAERA integrates these drivers into a trial-ready, non-animal methodology (NAM) aligned with FDA guidance, positioning it as a practical bridge between research rigor and lived patient reality.

FDA Modernization Act and Persistent Therapeutic Gaps

The FDA Modernization Act of 2022 encouraged the use of advanced modeling and simulation to accelerate drug development while reducing reliance on animal testing. CYNAERA aligns directly with this pathway by providing high-fidelity, multi-axis simulations capable of capturing patient heterogeneity without exposing fragile populations to early-phase harm. For IACCs, this capability is urgent. There are currently no FDA-approved treatments for ME/CFS, POTS, MCAS, or Long COVID. Trials in these conditions are frequently underpowered and prone to failure due to mis-stratification, unmodeled flare risk, and participant dropout. CYNAERA provides an in silico design and safety layer, allowing trial arms to be stress-tested and destabilization risk to be modeled prior to enrollment.

Use Cases

CYNAERA supports multiple deployment pathways within the IACC domain:

• Precision trial design reflecting real-world heterogeneity

• Pre-screening to identify patients likely to destabilize under standard protocols

• Adaptive IRB overlays to flag risk and model mitigation strategies

• Licensing for pharmaceutical, AI, and public health initiatives

Quantified Capacity in the IACC Configuration

Within the broader CYNAERA architecture:

• 1,000+ intelligence frameworks govern system logic

• 5 billion+ dynamic modules encode executable intelligence

• 1 billion+ chronic-condition digital twins represent patient-state models

Within the IACC configuration specifically:

• Hundreds of millions of modeled recovery pathways, with 675 million+ validated in the baseline configuration

• 11 stratification axes integrated with 6 mechanistic phenotyping domains

Five proprietary engines operating as a unified platform:

1. SymCas™ for flare forecasting.

2. VitalGuard™ for environmental risk overlays.

3. NeuroVerse™ for neuroimmune pattern mapping.

4. Pathos™ for severity and systemic burden scoring.

5. Individualized Medicine Engine™ for treatment stack simulation.

These figures represent current modeled capacity, not architectural limits.

Conclusion

Infection-associated chronic conditions remain among the most complex and under-served areas of medicine, in part because existing systems lack the resolution to represent real patients.

CYNAERA resolves this limitation by modeling patient heterogeneity at scale. Within the IACC domain alone, the system supports hundreds of millions of distinct recovery and stabilization pathways, grounded in validated configurations and aligned with modern regulatory expectations.

By separating patient context from biological mechanism, integrating environmental and hormonal risk, and embedding non-animal methodologies into trial design, CYNAERA provides an operational foundation for the next generation of clinical research and therapeutic discovery in ME/CFS, Long COVID, POTS, MCAS, and related conditions. This is deployed intelligence, structured to expand as additional domains are activated.

Author’s Note:

All insights, frameworks, and recommendations in this written material reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models available through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time health surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs). You can get licensing here at CYNAERA Market.

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

Learn More: https://www.cynaera.com/systems

About the Author 

Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with Selin Lab for t cell research at the University of Massachusetts.

Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.

Through CYNAERA, she develops modular AI platforms, including the IACC Progression Continuum™, Primary Chronic Trigger (PCT)™, RAVYNS™, and US-CCUC™, that are made to help governments, universities, and clinical teams model infection-associated conditions and improve precision in research and trial design. She has been featured in TIME, Bloomberg, USA Today, and other major outlets, for community engagement, policy and reflecting her ongoing commitment to advancing innovation and resilience from her home in Northern Virginia.

Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed perspective to her research and policy initiatives.