Quercetin as a potential nutraceutic against coronavirus disease 2019 (COVID-19)
Bibliographic record
Abstract
Abstract Introduction: The coronavirus disease 2019 (COVID-19) is a viral disease that affects several human organs and systems. Preventive or prophylactic treatments are specifically useful in emerging infectious diseases such as COVID-19 because they reduce the need for hospitalization and public health spending. Although the SARS-CoV-2 preventive effect of several therapeutic agents (e.g., hydroxychloroquine/chloroquine, remdesivir, lopinavir, and ritonavir) has been extensively evaluated, none of them have demonstrated significant clinical efficacy. Method: We aim to address and discuss the recently published studies on the chemoprophylactic potential of quercetin against SARS-CoV-2. A literature search was carried out on different databases, such as PubMed/MEDLINE, Scielo, Scopus, Web of Science, Cochrane Library, and Clinical Trials.gov. Studies that report the effect of quercetin against SARS-CoV-2 or other types of coronaviruses were included and critically evaluated. Results: Studies have shown that quercetin, an FDA-approved flavonoid used as an antioxidant and anti-inflammatory agent, inhibits the entry of coronavirus (SARS-CoV) into the host cell. Moreover, an in silico study showed that quercetin is a potent inhibitor of the SARS-CoV-2 main protease (Mpro), suggesting that this flavonoid is also active against COVID-19. Conclusions: Because quercetin might prevent and lessen the duration of SARS-CoV-2 infections, it is plausible to assume that the prophylactic use of this flavonoid produces several clinical benefits. However, this preliminary evidence needs to be confirmed by in vitro assays and, posteriorly, in randomized clinical trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".