Post-COVID syndrome is associated with capillary alterations, macrophage infiltration and distinct transcriptomic signatures in skeletal muscles
Bibliographic record
Abstract
Abstract The SARS-CoV-2 pandemic not only resulted in millions of acute infections worldwide, but also caused innumerable cases of post-infectious syndromes, colloquially referred to as “long COVID”. Due to the heterogeneous nature of symptoms and scarcity of available tissue samples, little is known about the underlying mechanisms. We present an in-depth analysis of skeletal muscle biopsies obtained from eleven patients suffering from enduring fatigue and post-exertional malaise after an infection with SARS-CoV-2. Compared to two independent historical control cohorts, patients with post-COVID exertion intolerance had fewer capillaries, thicker capillary basement membranes and increased numbers of CD169 + macrophages. SARS-CoV-2 RNA could not be detected in the muscle tissues, but transcriptomic analysis revealed distinct gene signatures compared to the two control cohorts, indicating immune dysregulations and altered metabolic pathways. We hypothesize that the initial viral infection may have caused immune-mediated structural changes of the microvasculature, potentially explaining the exercise-dependent fatigue and muscle pain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".