Can phytotherapy with polyphenols serve as a powerful approach for the prevention and therapy tool of novel coronavirus disease 2019 (COVID-19)?
Bibliographic record
Abstract
Much more serious than the previous severe acute respiratory syndrome (SARS) coronavirus (CoV) outbreaks, the novel SARS-CoV-2 infection has spread speedily, affecting 213 countries and causing ∼17,300,000 cases and ∼672,000 (∼+1,500/day) deaths globally (as of July 31, 2020). The potentially fatal coronavirus disease (COVID-19), caused by air droplets and airborne as the main transmission modes, clearly induces a spectrum of respiratory clinical manifestations, but it also affects the immune, gastrointestinal, hematological, nervous, and renal systems. The dramatic scale of disorders and complications arises from the inadequacy of current treatments and absence of a vaccine and specific anti-COVID-19 drugs to suppress viral replication, inflammation, and additional pathogenic conditions. This highlights the importance of understanding the SARS-CoV-2 mechanisms of actions and the urgent need of prospecting for new or alternative treatment options. The main objective of the present review is to discuss the challenging issue relative to the clinical utility of plants-derived polyphenols in fighting viral infections. Not only is the strong capacity of polyphenols highlighted in magnifying health benefits, but the underlying mechanisms are also stressed. Finally, emphasis is placed on the potential ability of polyphenols to combat SARS-CoV-2 infection via the regulation of its molecular targets of human cellular binding and replication, as well as through the resulting host inflammation, oxidative stress, and signaling pathways.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".