Microglia Fighting for Neurological and Mental Health: On the Central Nervous System Frontline of COVID-19 Pandemic
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is marked by cardio-respiratory alterations, with increasing reports also indicating neurological and psychiatric symptoms in infected individuals. During COVID-19 pathology, the central nervous system (CNS) is possibly affected by direct severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invasion, exaggerated systemic inflammatory responses, or hypoxia. Psychosocial stress imposed by the pandemic further affects the CNS of COVID-19 patients, but also the non-infected population, potentially contributing to the emergence or exacerbation of various neurological or mental health disorders. Microglia are central players of the CNS homeostasis maintenance and inflammatory response that exert their crucial functions in coordination with other CNS cells. During homeostatic challenges to the brain parenchyma, microglia modify their density, morphology, and molecular signature, resulting in the adjustment of their functions. In this review, we discuss how microglia may be involved in the neuroprotective and neurotoxic responses against CNS insults deriving from COVID-19. We examine how these responses may explain, at least partially, the neurological and psychiatric manifestations reported in COVID-19 patients and the general population. Furthermore, we consider how microglia might contribute to increased CNS vulnerability in certain groups, such as aged individuals and people with pre-existing conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".