Vitamin D enhances type I IFN signaling in COVID-19 patients
Bibliographic record
Abstract
The ability of Vitamin D (VitD) to modulate antiviral responses through induction of antimicrobial peptide is well established. However, the effect of VitD on host responses to SARS-CoV-2 is not well investigated. We here report the ability of VitD to enhance host IFN-alpha/beta (a/β) signaling both in vitro and among severe COVID-19 patients treated with VitD. Blood and saliva specimens were obtained from severe COVID-19 patients treated (43 patients), or not (37 patients), with vitD, during their stay in intensive care unit. Patients were followed up to 29 days following admission, and patient survival outcomes were collected. Higher activity levels of RIG-1/MDA-5 and JAK-STAT signaling pathways were observed with significantly higher gene and protein levels of antiviral interferon stimulating genes (ISGs) such as MX-1 and ISG-15; both in vitro, following treatment of PBMCs with vitD, and in whole blood and saliva specimens of VitD treated patients. Moreover, VitD treated patients had lower risk of all-cause mortality by day 29 compared to untreated patients (adjusted hazard ratio, 0.37, 95% confidence interval of 0.14-0.94; P = 0.038). The herein uncovered regulatory role of VitD on type I IFNs suggests the importance of insuring a normal level of VitD for the prevention and probably treatment of SARS-CoV-2 infection. Additional mechanistic studies, however, are needed to fully elucidate the antiviral effects of VitD particularly in the setting of COVID-19 infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".