The Role of Nutraceuticals in the Prevention and/or Treatment of COVID-19: An Umbrella Review
Bibliographic record
Abstract
Background: To identify the results of published review literature regarding nutraceuticals, including probiotics, melatonin, poly-unsaturated fatty acids (PUFAs), quercetin, N-acetyl cysteine (NAC), and propolis as they relate to the prevention and/ or treatment of COVID-19 (CV) and/or long COVID (long CV) and to outline key areas to consider for clinical application and for further research. Methods: This paper is part of a six-part umbrella review which progresses from a living review. This review incorporates systematic reviews and narrative reviews as they relate to nutraceuticals. A live literature search occurred monthly in PubMed and Google Scholar from May 2022 to May 2023. Assessing the Methodological Quality of Systematic Reviews Version 2 (AMSTAR-2) scoring assessed systematic review quality, while the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines evaluated narrative reviews. Only those studies that were relevant to the nutraceuticals outlined above and that addressed COVID-19 prevention and/or treatment of CV and/or long CV were extracted from each review. Results: Fifteen narrative reviews and 16 systematic reviews were included in this umbrella review. Studies indicate that nutraceuticals may be beneficial in improving the rate of recovery from various COVID-19 symptoms, rate of conversion parameters such as rate or duration of hospital stay and risk of intensive care unit (ICU) admission, and an improvement in various laboratory tests. Conclusion/Summary: The broad antioxidant, anti-inflammatory, antiviral, and immune modulatory characteristics make the nutraceuticals included in this review reasonable choices for further research. Of the nutraceuticals discussed above, probiotics, melatonin, NAC, and quercetin indicate the greatest potential for benefit in the prevention and treatment of COVID-19 and long CV.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".