Chronic inflammation, neuroglial dysfunction, and plasmalogen deficiency as a new pathobiological hypothesis addressing the overlap between post-COVID-19 symptoms and myalgic encephalomyelitis/chronic fatigue syndrome
Bibliographic record
Abstract
After five waves of coronavirus disease 2019 (COVID-19) outbreaks, it has been recognized that a significant portion of the affected individuals developed long-term debilitating symptoms marked by chronic fatigue, cognitive difficulties ("brain fog"), post-exertional malaise, and autonomic dysfunction. The onset, progression, and clinical presentation of this condition, generically named post-COVID-19 syndrome, overlap significantly with another enigmatic condition, referred to as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Several pathobiological mechanisms have been proposed for ME/CFS, including redox imbalance, systemic and central nervous system inflammation, and mitochondrial dysfunction. Chronic inflammation and glial pathological reactivity are common hallmarks of several neurodegenerative and neuropsychiatric disorders and have been consistently associated with reduced central and peripheral levels of plasmalogens, one of the major phospholipid components of cell membranes with several homeostatic functions. Of great interest, recent evidence revealed a significant reduction of plasmalogen contents, biosynthesis, and metabolism in ME/CFS and acute COVID-19, with a strong association to symptom severity and other relevant clinical outcomes. These bioactive lipids have increasingly attracted attention due to their reduced levels representing a common pathophysiological manifestation between several disorders associated with aging and chronic inflammation. However, alterations in plasmalogen levels or their lipidic metabolism have not yet been examined in individuals suffering from post-COVID-19 symptoms. Here, we proposed a pathobiological model for post-COVID-19 and ME/CFS based on their common inflammation and dysfunctional glial reactivity, and highlighted the emerging implications of plasmalogen deficiency in the underlying mechanisms. Along with the promising outcomes of plasmalogen replacement therapy (PRT) for various neurodegenerative/neuropsychiatric disorders, we sought to propose PRT as a simple, effective, and safe strategy for the potential relief of the debilitating symptoms associated with ME/CFS and post-COVID-19 syndrome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".