Effectiveness of supplementation with quercetin‐type flavonols for treatment of viral lower respiratory tract infections: Systematic review and meta‐analysis of preclinical studies
Bibliographic record
Abstract
Viral infections of the lower respiratory tract are considered a public health problem. They affect millions of people worldwide, causing thousands of deaths, and are treated with expensive medicines, such as antivirals or palliative measures. In this study, we conducted a systematic review to describe the use of quercetin-type flavonols against lower respiratory tract viruses and discussed the preclinical impact of this approach on different signs and clinical mechanisms of infection. The systematic review was performed in PubMed/MEDLINE, Scopus, Scielo, and Biblioteca Virtual de Saúde (BVS). After the database search, 11 relevant studies were identified as eligible. The analysis of these studies showed evidence of antiviral activity of quercetin-type flavonols with significantly reduced mortality rate (M-H = 0.19, 95% CI: 0.05 to 0.65, p-value = 0.008) of infected animals and a reduction in the average viral load (IV = -1.93, 95% CI: -3.54 to -0.31, p-value = 0.02). Additionally, quercetin and its derivatives reduced the amount of proinflammatory cytokines, chemokines, reactive oxygen species, mucus production, and airway resistance in animals infected with a respiratory virus. Overall, supplementation with quercetin-type flavonols is a promising strategy for treating viral-induced lower respiratory tract infections.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".