Vitamin A and vitamin E in SARS-CoV-2 Infection: a systematic review
Bibliographic record
Abstract
Abstract It is known that nutrition plays an important role in maintaining the immune system. In addition, viral infections can culminate in the depletion of vitamins, such as vitamins A and E, and inadequate levels of nutrients can be harmful to health, it is important to verify its relationship with COVID-19, a disease caused by the SARS-CoV-2, the new coronavirus first identified in Wuhan (China), and has spread rapidly worldwide, affecting the immune system and causing exacerbated inflammation. The aim of the study is to investigate whether COVID-19 affects the levels of these vitamins, and how they can help fighting the infection, it is relevant since they can be included as therapy against the disease. A systematic search was performed according to the PRISMA statement, using the PubMed, Web of Science, and EMBASE databases. We selected articles that presented the controlled descriptors present in the title or abstract without language exclusion. Abstracts, conferences, editorials, book chapters, review articles, letters, short communications, supporting information, or articles unavailable for download were excluded. The quality of studies was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). From a total of 4,572 articles, 10 met all inclusion criteria. Three of them were related with vitamin A, two with vitamin E and five relating both vitamins to COVID-19. Of these, 70% were studies that evaluated the concentrations of vitamins, and 30% that evaluated the impact of supplementation of these micronutrients. Adult and elderly patients had decreased vitamin A and E concentrations, being below or close to the reference values for deficiency, especially in critical patients. Supplementation of these vitamins was associated with better prognosis of patients. However, the therapeutic capacity of these vitamins requires additional biological validation through more robust studies, such as randomized clinical trials and assessment of food intake of these vitamins. In addition, studies that assess serum levels of these vitamins before infection are also required.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".