Biological correlates of the neuropsychiatric symptoms in SARS-CoV-2 infection: an updated review
Bibliographic record
Abstract
Abstract Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) appeared in early 2019 and swiftly became a pandemic causing extensive morbidity and mortality. Many studies have recognized the neuropathological changes in the brain and hypothesized the possible link with cognitive dysfunction, neuropsychiatric symptoms and behavioral disturbances. Potential biological mechanisms may include direct neuronal micro-invasion, disturbances in the neuro-immuno-endocrine system and possibly alteration of neuronal excitability. SARS-CoV-2 facilitates down-regulation of the ACE2 (Angiotensin-2) receptors which could alter inflammatory response through various cellular and neurophysiological systems leading to disturbance in the hypo-thalamopituitary–adrenal (HPA) axis, escalation of the oxidative stress and disruption of the homeostasis of the neurotransmitter system, including serotonin, dopamine and GABA (gamma-aminobutyric acid), eventually resulting in the emergence of neuropsychiatric symptoms. Psychiatric symptoms that emerged are many, some of which may be unique to SARS-CoV-2 infection. These neuropsychiatric symptoms are acute or chronic with possibly distinct etiopathogenesis. This article discusses the possible biological correlates and neurobiology of the psychiatric symptoms of SARS-CoV-2 and their impact on the brain and behavior.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".