Reactive gliosis and neuroinflammation: prime suspects in the pathophysiology of post-acute neuroCOVID-19 syndrome
Bibliographic record
Abstract
Introduction: As the repercussions from the COVID-19 pandemic continue to unfold, an ever-expanding body of evidence suggests that infection also elicits pathophysiological manifestations within the central nervous system (CNS), known as neurological symptoms of post-acute sequelae of COVID infection (NeuroPASC). Although the neurological impairments and repercussions associated with NeuroPASC have been well described in the literature, its etiology remains to be fully characterized. Objectives: This mini-review explores the current literature that elucidates various mechanisms underlining NeuroPASC, its players, and regulators, leading to persistent neuroinflammation of affected individuals. Specifically, we provide some insights into the various roles played by microglial and astroglial cell reactivity in NeuroPASC and how these cell subsets potentially contribute to neurological impairment in response to the direct or indirect mechanisms of CNS injury. Discussion: A better understanding of the mechanisms and biomarkers associated with this maladaptive neuroimmune response will thus provide better diagnostic strategies for NeuroPASC and reveal new potential mechanisms for therapeutic intervention. Altogether, the elucidation of NeuroPASC pathogenesis will improve patient outcomes and mitigate the socioeconomic burden of this 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.001 |
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".