Microglial Implications in SARS-CoV-2 Infection and COVID-19: Lessons From Viral RNA Neurotropism and Possible Relevance to Parkinson’s Disease
Bibliographic record
Abstract
Since December 2019, humankind has been experiencing a ravaging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak, the second coronavirus pandemic in a decade after the Middle East respiratory syndrome coronavirus (MERS-CoV) disease in 2012. Infection with SARS-CoV-2 results in Coronavirus disease 2019 (COVID-19), which is responsible for over 3.1 million deaths worldwide. With the emergence of a second and a third wave of infection across the globe, and the rising record of multiple reinfections and relapses, SARS-CoV-2 infection shows no sign of abating. In addition, it is now evident that SARS-CoV-2 infection presents with neurological symptoms that include early hyposmia, ischemic stroke, meningitis, delirium and falls, even after viral clearance. This may suggest chronic or permanent changes to the neurons, glial cells, and/or brain vasculature in response to SARS-CoV-2 infection or COVID-19. Within the central nervous system (CNS), microglia act as the central housekeepers against altered homeostatic states, including during viral neurotropic infections. In this review, we highlight microglial responses to viral neuroinfections, especially those with a similar genetic composition and route of entry as SARS-CoV-2. As the primary sensor of viral infection in the CNS, we describe the pathogenic and neuroinvasive mechanisms of RNA viruses and SARS-CoV-2 vis-à-vis the microglial means of viral recognition. Responses of microglia which may culminate in viral clearance or immunopathology are also covered. Lastly, we further discuss the implication of SARS-CoV-2 CNS invasion on microglial plasticity and associated long-term neurodegeneration. As such, this review provides insight into some of the mechanisms by which microglia could contribute to the pathophysiology of post-COVID-19 neurological sequelae and disorders, including Parkinson's disease, which could be pervasive in the coming years given the growing numbers of infected and re-infected individuals globally.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".