The Relationship Between Vitamin D and Infections Including COVID-19: Any Hopes?
Bibliographic record
Abstract
Vitamin D is proposed to have a potential role in the pathogenicity, clinical presentation, prognosis, complications, and treatment of several diseases. In addition to its well-known role in calcium metabolism, vitamin D regulates both innate and adaptive immunity, and subsequently modulates the antiviral and antibacterial inflammatory immune responses. In view of the emerging coronavirus disease 2019 (COVID-19) pandemic, searching for potential therapeutic and protective strategies is of urgent interest, and vitamin D is one of the promising agents in this field. In this review, we present data from literature that supports the promising role of vitamin D in treatment and/or prevention of several infections including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review summarizes vitamin D metabolism and its role in inflammation, thrombosis and immune regulation. It also reviews, in short, the role of vitamin D and the impact of its deficiency in several infections namely tuberculosis, influenza, human immunodeficiency virus (HIV), and SARS-CoV-2. Considering the roles of vitamin D on immune modulation, controlling of thrombosis, and attacking several microorganisms, the current review will elaborate on the association between these salient roles of vitamin D and the pathogenicity of various infectious agents including COVID-19. Consequently, the comprehensive finding of the current review shows a possible significant impact of vitamin D supplement as a hope in preventing, treating, and/or improving the progression of certain infections, specifically during the worldwide attempts to fight against the COVID-19 pandemic and minimize the severity of health complications encountered accordingly. In addition, avoiding a status of vitamin D deficiency to obtain its positive effects on the immune system and its protective mechanism during infections will be a general benefit overall.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".