Vitamin D Focused Approach to Nutritional Therapy for the Managementof SARS-CoV-2 Pandemic: A Review
Bibliographic record
Abstract
Background: In several studies, Vitamin D supplementation was found useful against the highly infectious SARS-CoV-2 to reduce the mortality rate and severity of its infection. Viral replication was also found to be affected negatively by vitamin D administration. Objective: The literature was reviewed with an aim to evaluate the efficacy of the therapeutic approach of nutrition involving intervention of Vitamin D towards decreasing the severity of prevailing pandemic of SARS-CoV-2. Methods: A background research of literature was performed using the keywords “SARS-CoV- 2”, “Covid-19”, “nutritional therapy”, “Vitamin D”, “immunity”, “AEC2 receptors” and “RAS” in the Pubmed and Google Scholar. Results: This literature was review suggested that if combined with medical sciences, this nutritional therapy approach can end up as an influential solution to reduce the severity of SARSCoV- 2 infection, which is a prevailing pandemic. A combination of assessment, supplementation of this required micro-nutrient (Vitamin D), and monitoring can be used to aid the immune system of Covid-19 patients. Conclusion: Nutritional therapy with Vitamin D as a major factor can be used to increase the immunity of an individual to fight against the highly infectious SARS-CoV-2. However, the actual mechanism of how Vitamin D supplementation functions to improve the health of an individual is yet unclear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 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) | 0.004 | 0.001 |
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".