Vitamin D regulates COVID-19 associated severity by suppressing the NLRP3 inflammasome pathway
Bibliographic record
Abstract
BACKGROUND: The role of vitamin D3 (VitD3) in modulating innate and adaptive immunity has been reported in different disease contexts. Since the start of the coronavirus disease-2019 (COVID-19) pandemic, the role of VitD3 has been highlighted in many correlational and observational studies. However, the exact mechanisms of action are not well identified. One of the mechanisms via which VitD3 modulates innate immunity is by regulating the NLRP3-inflammasome pathway, being a main underlying cause of SARS-CoV-2-induced hyperinflammation. AIMS AND MAIN METHODS: Blood specimens of severe COVID-19 patients with or without VitD3 treatment were collected during their stay in the intensive care unit and patients were followed up for 29 days. qPCR, western blot, and ELISA were done to investigate the mechanism of action of VitD3 on the NLRP3 inflammasome activation. KEY FINDINGS: We here report the ability of VitD3 to downregulate the NLRP3-inflammsome pathway in severe COVID-19 patients. Lower inflammasome pathway activation was observed with significantly lower gene and protein expression of NLRP3, cleaved caspase-1, ASC and IL-1β among severe COVID-19 patients treated with VitD3. The reduction of the inflammasome pathway was associated with a reduction in disease severity markers and enhancement of type I IFN pathway. SIGNIFICANCE: Our data reveals an important anti-inflammatory effect of VitD3 during SARS-CoV-2 infection. Further investigations are warranted to better characterize the ability of VitD3 to control disease pathogenesis and prevent progression to severe states. This will allow for a more efficient use of a low cost and accessible treatment like VitD3.
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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 | 0.001 |
| 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 | 0.001 |
| 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".