Why Drug Approval for ME/CFS Was Always a Setup
- Aug 26, 2025
- 14 min read
Updated: May 21
By Cynthia Adinig
Executive Summary
Clinical trials for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) have repeatedly failed to produce durable therapeutic breakthroughs, not necessarily because candidate therapies lacked biologic potential, but because the underlying clinical trial architecture was poorly aligned with the disease itself. Traditional FDA-oriented trial systems were largely designed for relatively stable illnesses with predictable progression patterns, limited physiologic fluctuation, and uniform treatment tolerance. ME/CFS and related infection-associated chronic conditions (IACCs) do not behave this way. These illnesses are characterized by post-exertional symptom exacerbation (PEM), autonomic instability, neuroinflammation, mast-cell activation, fluctuating immune signaling, mitochondrial dysfunction, sensory hypersensitivity, and highly variable relapse-sensitive trajectories that often change across time, environment, exertion, infection status, hormonal state, and cumulative physiologic stress (Institute of Medicine, 2015; Komaroff and Lipkin, 2021; Wirth and Scheibenbogen, 2021).
The result has been a long history of expensive clinical trials built on assumptions fundamentally mismatched to the biology of ME/CFS. Many legacy studies failed to account for heterogeneity in immune subtype, autonomic instability, mast-cell-sensitive populations, mitochondrial fragility, endocrine fluctuation, or overlapping diagnoses including POTS, MCAS, connective tissue disorders, migraine, gastrointestinal dysmotility, and post-viral inflammatory syndromes (Hornig et al., 2015; Raj et al., 2020; Afrin et al., 2017). Conventional trial structures also frequently ignored delayed PEM timing, exertional crash dynamics, environmental sensitivity, and nonlinear recovery behavior, leading to distorted endpoint interpretation, elevated dropout rates, false negatives, and widespread difficulty reproducing findings across studies (Bateman et al., 2021; Davenport et al., 2019).
This paper argues that many historical ME/CFS trial failures were not inevitable therapeutic failures, but failures of trial logic itself. Using retrospective analysis of legacy studies including Rituximab and Ampligen, this paper explores how static enrollment models, rigid endpoint structures, non-stratified cohorts, and failure to incorporate neuroimmune terrain contributed to misleading outcomes and poor translational scalability. The paper further proposes that many repurposed or partially effective therapeutics may perform substantially better when trial architecture reflects the biologic reality of relapsing neuroimmune disease rather than attempting to force ME/CFS into conventional chronic disease frameworks.
The CYNAERA approach introduces a terrain-adaptive clinical trial model designed specifically for dynamic neuroimmune illness. Rather than assuming therapeutic response is diagnosis-dependent alone, the framework approaches treatment response as state-dependent, influenced by autonomic stability, immune signaling, mitochondrial resilience, environmental exposure, mast-cell activity, endocrine fluctuation, exertional load, and recovery capacity. The framework integrates AI-supported phenotype stratification, flare-aware endpoint logic, PEM-sensitive scheduling, remote biomarker collection, simulation modeling, adaptive trial arms, and longitudinal resilience tracking to improve signal detection and reduce avoidable trial distortion.
Particular emphasis is placed on the importance of stratifying patients according to biologic subtype rather than treating ME/CFS as a single homogeneous entity. Viral-onset and trauma-associated populations may demonstrate different inflammatory signatures, autonomic profiles, and treatment response patterns. Patients with MCAS overlap may tolerate interventions differently than metabolically dominant or autonomic-dominant subgroups. Similarly, severe PEM populations may require fundamentally different scheduling, pacing accommodations, and endpoint interpretation than milder or more stable cohorts (Hornig et al., 2015; Tomas et al., 2017; Fluge et al., 2016).
The framework also addresses the growing role of digital biomarkers, wearable technologies, adaptive analytics, and virtual trial systems in reducing participant burden and improving longitudinal interpretation. Heart rate variability (HRV), autonomic metrics, mitochondrial stress indicators, cytokine fluctuation, environmental exposure overlays, exertional recovery windows, and flare-prediction systems may all improve therapeutic interpretation when integrated into dynamic trial architecture rather than treated as secondary exploratory variables (Kogelnik et al., 2020; Raj et al., 2020).
Importantly, this paper situates ME/CFS within the broader evolution of FDA modernization and AI-assisted trial infrastructure. FDA Modernization Acts 2.0 and 3.0 have increasingly opened pathways for adaptive modeling, simulation-supported design, decentralized monitoring, and AI-assisted interpretation within pharmaceutical development (FDA, 2022). Oncology and rare disease research have already begun incorporating these approaches, while ME/CFS and related neuroimmune conditions continue to lag despite their biologic complexity and substantial public health burden.
Recent post-viral illness research further reinforces the need for adaptive, terrain-aware systems. The Long COVID characteristics study co-authored by Wong, Adinig, Putrino, and colleagues demonstrated extensive heterogeneity in symptom burden, autonomic instability, neurologic dysfunction, and longitudinal recovery trajectories across post-viral populations (Wong et al., 2023). Similarly, emerging work examining vaccine injury and Long COVID inflammatory signatures identified complex immune and inflammatory variability frequently overlooked by static clinical trial frameworks (Iwasaki, Putrino, Adinig, et al., 2025). Together, these findings support the argument that future neuroimmune therapeutic development will require systems capable of interpreting fluctuating biologic terrain rather than relying on rigid one-size-fits-all enrollment and endpoint logic.
The CYNAERA framework therefore positions stabilization, biologic stratification, flare-aware interpretation, adaptive modeling, and longitudinal resilience as foundational pillars of future ME/CFS therapeutic development. The central argument of this paper is straightforward: ME/CFS trials have often failed not because patients are untreatable, but because the systems used to study them were never designed for dynamic neuroimmune disease in the first place.
The Problem: Trials Designed for Failure
Legacy ME/CFS trials such as Rituximab and Ampligen illustrate systemic flaws. Rigid FDA protocols don’t account for heterogeneity, immune subtypes, PEM variability, or multi-system sensitivities (Fluge & Mella, 2019; Peterson, 2013). The result has been high costs, high dropout rates, misleading conclusions, and no FDA approvals (Straus, 1991; Komaroff, 2021).
Case Study 1: Rituximab (Phase 3, 2015–2017)
Cost: $10–13M (public and private support).
What Went Wrong:
No immune subtype stratification.
MCAS/cytokine variability ignored (Hornig et al., 2015).
PEM episodes confounded outcomes (Bateman et al., 2021).
Static metrics missed terrain-adaptive endpoints.
Dropouts/adverse events in severe patients.
CYNAERA Redesign:
AI-driven immune + mast cell clustering.
PEM-aware scheduling, remote biomarker collection.
Flare simulation modeling.
Projected Outcomes:
Cost: $3.8–5.2M (virtual stratification).
Success probability: ↑ from 12% → 62%.
Case Study 2: Ampligen (1990s–2010s)
Cost: $50M+ cumulative (Hemispherx Biopharma).
What Went Wrong:
Inconsistent ME/CFS definitions (Straus, 1991).
Heterogeneous trial populations.
MCAS flares misclassified as toxicity (Adinig et al., 2025).
Viral vs trauma-onset subtypes ignored.
Mitochondrial/redox biomarkers absent (Tomas et al., 2017).
Heavy patient dropout.
CYNAERA Redesign:
Stratification by onset type, cytokine + mitochondrial fragility.
Lactate/ATP panels + mitochondrial stress monitoring (Fluge et al., 2016).
PEM-safe selection algorithm.
Remote cardiac/metabolic flare monitoring.
Dropout prediction + preemptive interventions.
Projected Outcomes:
Cost: $6.5–8M.
Success probability: ↑ from 6% → 54%.
CYNAERA’s Breakthrough: A Terrain-Adaptive Logic Layer for ME/CFS Trial Success
The central problem in ME/CFS therapeutic development has never been the absence of biologic complexity. The problem has been the absence of trial infrastructure capable of interpreting that complexity. Legacy FDA-style trial systems were designed for relatively stable diseases with predictable progression, uniform treatment tolerance, and limited physiologic fluctuation. ME/CFS and related infection-associated chronic conditions (IACCs) do not behave this way. They are dynamic neuroimmune illnesses shaped by exertional instability, autonomic fluctuation, mast-cell activation, mitochondrial dysfunction, inflammatory variability, hormonal modulation, and environmental sensitivity (Institute of Medicine, 2015; Komaroff and Lipkin, 2021; Wirth and Scheibenbogen, 2021).
The CYNAERA framework introduces a terrain-adaptive logic layer specifically designed for fluctuating neuroimmune disease. Rather than treating biologic variability as statistical noise to be minimized, the framework approaches variability as interpretable signal that can improve subgroup resolution, endpoint accuracy, tolerability interpretation, and therapeutic durability modeling. Therapeutic response is therefore treated as state-dependent rather than diagnosis-dependent alone.
The framework integrates multiple interoperable systems including SymCas™ flare modeling, VitalGuard™ environmental instability analysis, Composite Diagnostic Fingerprints™ (CDF™), adaptive dropout forecasting, phenotype-aware stratification, and longitudinal resilience interpretation. Together, these systems create a modular architecture capable of modeling the dynamic physiologic terrain often ignored in conventional ME/CFS trials.
Phenotype Stratification as Standard Infrastructure
Legacy ME/CFS studies frequently grouped biologically incompatible populations into generalized cohorts, diluting signal detection and obscuring subgroup-specific response patterns. CYNAERA instead approaches stratification as foundational infrastructure rather than optional exploratory analysis. AI-supported logic trees and phenotype clustering systems evaluate autonomic instability, PEM severity, immune signaling patterns, mast-cell-sensitive phenotypes, mitochondrial fragility, environmental sensitivity, neurocognitive dysfunction, and inflammatory variability before enrollment and throughout longitudinal monitoring (Hornig et al., 2015; Lipkin et al., 2017; Raj et al., 2020).
This approach allows trials to distinguish between viral-onset, trauma-associated, autonomic-dominant, mast-cell-sensitive, neuroinflammatory, and metabolically unstable subgroups rather than assuming uniform disease behavior across all participants. The result is improved signal clarity, more interpretable endpoint behavior, and reduced subgroup dilution.
Safety Modeling and Flare-Aware Infrastructure
Many severe ME/CFS patients deteriorate not because interventions are universally harmful, but because trial systems fail to account for relapse-sensitive physiology, delayed PEM timing, autonomic fragility, and cumulative exertional stress. CYNAERA’s flare-aware architecture therefore integrates predictive destabilization logic designed to identify periods of elevated physiologic vulnerability before major crashes occur.
Systems such as SymCas™ model symptom cascade timing, autonomic rebound behavior, cumulative exertional load, and delayed PEM dynamics longitudinally rather than through isolated symptom snapshots. The framework also supports remote monitoring, pacing-aware scheduling, adaptive onboarding, and physiologic burden reduction strategies intended to improve safety while preserving data quality (Bateman et al., 2021; Davenport et al., 2019).
Virtual Simulation and Predictive Trial Architecture
One of CYNAERA’s major innovations is the use of simulation-supported trial refinement before participant enrollment begins. Rather than relying exclusively on fixed pre-launch assumptions, the framework allows researchers to model hundreds of potential trial configurations using virtual phenotype populations, predicted dropout behavior, PEM timing, biomarker variability, and environmental destabilization scenarios.
The CYNAERA Clinical Trials Simulator™ was designed to evaluate:
dropout probability
flare timing
subgroup dilution
endpoint sensitivity
biomarker responsiveness
placebo sensitivity
autonomic destabilization risk
protocol burden thresholds
This approach aligns with broader FDA modernization trends emphasizing AI-supported modeling, decentralized monitoring, and adaptive trial infrastructure within precision medicine and rare disease development (FDA, 2022; Bothwell et al., 2018).
Adaptive Logic and Dynamic Trial Arms
Conventional ME/CFS trials often rely on rigid protocol structures that remain static even when biomarker behavior, tolerability patterns, or flare trajectories clearly suggest the need for adjustment. CYNAERA instead approaches trial architecture dynamically. Adaptive logic systems may modify pacing schedules, monitoring frequency, intervention timing, dose escalation windows, environmental safeguards, or subgroup prioritization according to longitudinal biomarker and symptom behavior. This approach is especially important in neuroimmune illness because treatment tolerance itself may fluctuate depending on autonomic state, sleep quality, hormonal cycling, inflammatory burden, environmental exposure, reinfection timing, and cumulative physiologic stress (Komaroff and Bateman, 2021; Proal and VanElzakker, 2021).
Biomarker Integration Beyond Static Endpoints
Many legacy ME/CFS studies collected insufficient physiologic data to interpret fluctuating neuroimmune disease adequately. CYNAERA emphasizes continuous and longitudinal biomarker interpretation rather than isolated laboratory snapshots. Systems may integrate cytokine variability, HRV patterns, ATP kinetics, histamine signaling, lactate dynamics, orthostatic behavior, sleep instability, cognitive endurance metrics, mitochondrial stress indicators, and small fiber neuropathy-associated symptom patterns into broader terrain analysis (Fluge et al., 2016; Tomas et al., 2017; Rowe et al., 2014). Importantly, the framework does not treat biomarkers as isolated diagnostic artifacts alone. Biomarkers are interpreted within the context of exertional behavior, environmental exposure, relapse timing, autonomic fluctuation, and longitudinal resilience capacity.
What Legacy Trials Missed & How CYNAERA Fixes It
Legacy designs overlooked biomarkers and comorbidities that define ME/CFS. CYNAERA’s proactive approach ensures these are accounted for:
Parameter | Missed in Legacy Trials | CYNAERA Logic | Impact |
MCAS Sensitivity | Misclassified as drug toxicity | Histamine/tryptase profiling + flare logic (Adinig et al., 2025) | Prevents false negatives |
PEM Risk Prediction | Flares mid-trial skewed outcomes | HRV + cytokine flare modeling (Bateman et al., 2021) | Optimizes timing |
Onset Subtype | Mixed cohorts diluted signals | Stratified viral vs trauma arms | Clear efficacy |
Mitochondrial Dysfunction | Not collected | ATP kinetics + lactate stress (Fluge et al., 2016) | Energy pathway insights |
Dropout Forecasting | >30% lost | Predictive dropout buffer cohorts | Protects power |
Adaptive Arm Logic | Fixed/static arms | Dynamic dose/protocol adjustment | Higher remission rates |
FDA Modernization and the Future of ME/CFS Clinical Trials
FDA Modernization Acts 2.0 and 3.0 represent a major shift in how complex disease research may be approached in the coming decade. These modernization efforts increasingly support AI-assisted modeling, decentralized monitoring, adaptive trial infrastructure, digital biomarkers, simulation-supported design, and in silico approaches capable of improving efficiency and biologic precision within therapeutic development (FDA, 2022; Bothwell et al., 2018). While oncology, immunology, and rare disease research have already begun integrating many of these strategies into mainstream clinical development, ME/CFS and related neuroimmune illnesses continue to lag despite demonstrating many of the exact complexities these newer systems were designed to address.
This gap is increasingly difficult to justify. ME/CFS is characterized by fluctuating autonomic behavior, exertional instability, relapse-sensitive function, inflammatory variability, sensory hypersensitivity, and substantial biologic heterogeneity. Conventional static trial architecture struggles to interpret these patterns accurately because it assumes relatively stable baseline function and uniform treatment response. As a result, trials frequently misclassify flare behavior, underestimate subgroup-specific response, overestimate adverse events, and fail to capture meaningful longitudinal improvement.
CYNAERA was designed specifically for this emerging regulatory and scientific environment. Rather than requiring entirely new research infrastructure, the framework adapts evolving FDA-supported principles including adaptive analytics, phenotype-aware stratification, remote biomarker collection, decentralized monitoring, longitudinal resilience modeling, and simulation-supported trial refinement for use within relapsing neuroimmune illness. Systems including SymCas™, VitalGuard™, Composite Diagnostic Fingerprints™ (CDF™), and the CYNAERA Clinical Trials Simulator™ function as modular interpretive layers capable of improving signal detection, reducing dropout distortion, and identifying biologically meaningful therapeutic response patterns across fluctuating patient populations.
Recent post-viral illness research further reinforces the need for terrain-aware and adaptive trial systems. The Long COVID characteristics study co-authored by Wong, Adinig, Putrino, and colleagues from Yale and Yale–New Haven documented extensive heterogeneity in symptom burden, autonomic dysfunction, cognitive impairment, inflammatory behavior, and longitudinal recovery trajectories across post-viral populations (Wong et al., 2023). Similarly, emerging work examining vaccine injury and Long COVID inflammatory signatures identified immune variability and relapse-sensitive inflammatory behavior frequently overlooked by conventional trial frameworks (Iwasaki, Putrino, Adinig, et al., 2025). Together, these findings support a broader conclusion increasingly recognized across neuroimmune medicine: dynamic diseases require dynamic trial architecture.
Importantly, these changes are no longer theoretical. The tools required to redesign ME/CFS trials already exist. AI-supported stratification, remote monitoring, longitudinal biomarker analysis, decentralized trial participation, adaptive endpoint systems, and predictive simulation modeling are already being implemented across other sectors of medicine. The primary barrier is no longer technological feasibility, but willingness to apply modern systems-level infrastructure to diseases historically marginalized within biomedical research.
Conclusion
ME/CFS trials have repeatedly failed not because patients are untreatable, but because the systems used to study them were fundamentally mismatched to the biology of the disease itself. Conventional trial architecture was built around assumptions of stable progression, uniform disease expression, predictable recovery behavior, and relatively fixed treatment tolerance. ME/CFS and related infection-associated chronic conditions do not behave this way. They are dynamic neuroimmune illnesses shaped by PEM, autonomic instability, inflammatory fluctuation, environmental sensitivity, mitochondrial dysfunction, relapse-sensitive trajectories, and highly individualized physiologic terrain.
The consequence has been decades of expensive studies that frequently produced diluted signal, elevated dropout rates, inconsistent reproducibility, distorted adverse event interpretation, and therapeutic ambiguity. Legacy systems repeatedly failed to account for the very features that define ME/CFS itself. Patients whose symptoms fluctuated dynamically were often treated as statistical noise rather than biologically interpretable populations. The CYNAERA framework proposes a different approach. Rather than suppressing variability, the system organizes variability into interpretable biologic patterns through phenotype-aware enrollment, flare-sensitive endpoint design, longitudinal resilience tracking, predictive simulation modeling, environmental integration, and adaptive trial logic. Systems including SymCas™, VitalGuard™, the CYNAERA Clinical Trials Simulator™, Composite Diagnostic Fingerprints™ (CDF™), and the CYNAERA Remission Standard™ were developed specifically to improve interpretation across relapsing neuroimmune disease where timing, stability, resilience, and terrain profoundly influence therapeutic response.
Under this framework, trial optimization becomes both a scientific and economic strategy. Better stratification may reduce false negatives. Flare-aware logic may reduce dropout distortion and adverse event inflation. Adaptive monitoring may improve safety and endpoint accuracy. Simulation-supported architecture may reduce unnecessary costs while improving the likelihood that effective therapies are recognized rather than discarded prematurely. The broader implication is clear: future success in ME/CFS therapeutic development will likely depend not only on discovering better interventions, but on building better systems capable of interpreting complex neuroimmune disease accurately. The infrastructure to begin that transition already exists. The remaining question is whether research systems are willing to stop forcing ME/CFS into outdated models and instead build trials that reflect the biologic reality patients have been describing for decades.
CYNAERA Framework Papers and Core Research Libraries
This paper draws on a defined subset of CYNAERA Institute white papers that establish the methodological and analytical foundations of CYNAERA’s frameworks. These publications provide deeper context on prevalence reconstruction, remission, combination therapies and biomarker approaches. Our Long COVID Library, ME/CFS Library, Lyme Library, Autoimmune Library and CRISPR Remission Library are also in depth resources.
The Science of Remission of IACC's
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.
Patent-Pending Systems
Bioadaptive Systems Therapeutics™ (BST) and affiliated CYNAERA frameworks are protected under U.S. Provisional Patent Application No. 63/909,951. CYNAERA is built as modular intelligence infrastructure designed for licensing, integration, and strategic deployment across health, research, public sector, and enterprise environments.
Licensing and Integration
CYNAERA supports licensing of individual modules, bundled systems, and broader architecture layers. Current applications include research modernization, trial stabilization, diagnostic innovation, environmental forecasting, and population level modeling for complex chronic conditions. Basic licensing is available through CYNAERA Market, with additional pathways for pilot programs, institutional partnerships, and enterprise integration.
About the Author
Cynthia Adinig is the founder of CYNAERA, a modular intelligence infrastructure company that transforms fragmented real world data into predictive insight across healthcare, climate, and public sector risk environments. Her work sits at the intersection of AI infrastructure, federal policy, and complex health system modeling, with a focus on helping institutions detect hidden costs, anticipate service demand, and strengthen planning in high uncertainty environments.
Cynthia has contributed to federal health and data modernization efforts spanning HHS, NIH, CDC, FDA, AHRQ, and NASEM, and has worked with congressional offices including Senator Tim Kaine, Senator Ed Markey, Representative Don Beyer, and Representative Jack Bergman on legislative initiatives related to chronic illness surveillance, healthcare access, and data infrastructure. In 2025, she was appointed to advise the U.S. Department of Health and Human Services and has testified before Congress on healthcare data gaps and system level risk.
She is a PCORI Merit Reviewer, currently advises Selin Lab at UMass Chan, and has co-authored research with Harlan Krumholz, MD, Akiko Iwasaki, PhD, and David Putrino, PhD, including through Yale’s LISTEN Study. She also advised Amy Proal, PhD’s research group at Mount Sinai through its CoRE advisory board and has worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. Her CRISPR Remission™ abstract was presented at CRISPRMED26 and she has authored a Milken Institute essay on artificial intelligence and healthcare.
Cynthia has been covered by outlets including TIME, Bloomberg, Fortune, and USA Today for her policy, advocacy, and public health work. Her perspective on complex chronic conditions is also informed by lived experience, which sharpened her commitment to reforming how chronic illness is understood, studied, and treated. She also advocates for domestic violence prevention and patient safety, bringing a trauma informed lens to her research, systems design, and policy work. Based in Northern Virginia, she brings more than a decade of experience in strategy, narrative design, and systems thinking to the development of cross sector intelligence infrastructure designed to reduce uncertainty, improve resilience, and support institutional decision making at scale.
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