A comprehensive review on the antiviral activities of chalcones
Bibliographic record
Abstract
Some viral outbreaks have plagued the world since antiquity, including the most recent COVID-19 pandemic. The continuous spread and emergence of new viral diseases have urged the discovery of novel treatment options that can overcome the limitations of currently marketed antiviral drugs. Chalcones are natural open chain flavonoids that are found in various plants and can be synthesised in labs. Several studies have shown that these small organic molecules exert a number of pharmacological activities, including antiviral, anti-inflammatory, antimicrobial and anticancer. The purpose of this review is to provide a summary of the antiviral activities of chalcones and their derivatives on a set of human viral infections and their potential for targeting the most recent COVID-19 disease. Accordingly, we herein review chalcones activities on the following human viruses: Middle East respiratory syndrome coronavirus, severe acute respiratory syndrome coronavirus, human immunodeficiency, influenza, human rhinovirus, herpes simplex, dengue, human cytomegalovirus, hepatitis B and C, Rift Valley fever and Venezuelan equine encephalitis. We hope that this review will pave the way for the design and development of potentially potent and broad-spectrum chalcone based antiviral drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".