Dietary Vitamin D Mitigates Coronavirus-Induced Lung Inflammation and Damage in Mice
Bibliographic record
Abstract
The COVID-19 pandemic caused by the SARS-CoV-2 (β-CoV) betacoronavirus has posed a significant threat to global health. Despite the availability of vaccines, the virus continues to spread, and there is a need for alternative strategies to alleviate its impact. Vitamin D, a secosteroid hormone best known for its role in bone health, exhibits immunomodulatory effects in certain viral infections. Here, we have shown that bioactive vitamin D (calcitriol) limits in vitro replication of SARS-CoV-2 and murine coronaviruses MHV-3 and MHV-A59. Comparative studies involving wild-type mice intranasally infected with MHV-3, a model for studying β-CoV respiratory infections, confirmed the protective effect of vitamin D in vivo. Accordingly, mice fed a standard diet rapidly succumbed to MHV-3 infection, whereas those on a vitamin D-rich diet (10,000 IU of Vitamin D3/kg) displayed increased resistance to acute respiratory damage and systemic complications. Consistent with these findings, the vitamin D-supplemented group exhibited lower viral titers in their lungs and reduced levels of TNF, IL-6, IL-1β, and IFN-γ, alongside an enhanced type I interferon response. Altogether, our findings suggest vitamin D supplementation ameliorates β-CoV-triggered respiratory illness and systemic complications in mice, likely via modulation of the host’s immune response to the virus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".