The Pathobiology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: The Case for Neuroglial Failure
Bibliographic record
Abstract
Although myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) has a specific and distinctive profile of clinical features, the disease remains an enigma because causal explanation of the pathobiological matrix is lacking. Several potential disease mechanisms have been identified, including immune abnormalities, inflammatory activation, mitochondrial alterations, endothelial and muscular disturbances, cardiovascular anomalies, and dysfunction of the peripheral and central nervous systems. Yet, it remains unclear whether and how these pathways may be related and orchestrated. Here we explore the hypothesis that a common denominator of the pathobiological processes in ME/CFS may be central nervous system dysfunction due to impaired or pathologically reactive neuroglia (astrocytes, microglia and oligodendrocytes). We will test this hypothesis by reviewing, in reference to the current literature, the two most salient and widely accepted features of ME/CFS, and by investigating how these might be linked to dysfunctional neuroglia. From this review we conclude that the multifaceted pathobiology of ME/CFS may be attributable in a unifying manner to neuroglial dysfunction. Because the two key features - post exertional malaise and decreased cerebral blood flow - are also recognized in a subset of patients with post-acute sequelae COVID, we suggest that our findings may also be pertinent to this entity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".