When the clock ticks wrong with COVID‐19
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the coronavirus family that causes the novel coronavirus disease first diagnosed in 2019 (COVID-19). Although many studies have been carried out in recent months to determine why the disease clinical presentations and outcomes can vary significantly from asymptomatic to severe or lethal, the underlying mechanisms are not fully understood. It is likely that unique individual characteristics can strongly influence the broad disease variability; thus, tailored diagnostic and therapeutic approaches are needed to improve clinical outcomes. The circadian clock is a critical regulatory mechanism orchestrating major physiological and pathological processes. It is generally accepted that more than half of the cell-specific genes in any given organ are under circadian control. Although it is known that a specific role of the circadian clock is to coordinate the immune system's steady-state function and response to infectious threats, the links between the circadian clock and SARS-CoV-2 infection are only now emerging. How inter-individual variability of the circadian profile and its dysregulation may play a role in the differences noted in the COVID-19-related disease presentations, and outcome remains largely underinvestigated. This review summarizes the current evidence on the potential links between circadian clock dysregulation and SARS-CoV-2 infection susceptibility, disease presentation and progression, and clinical outcomes. Further research in this area may contribute towards novel circadian-centred prognostic, diagnostic and therapeutic approaches for COVID-19 in the era of precision health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".