Resveratrol and derivatives for the treatment of atrial fibrillation
Bibliographic record
Abstract
Resveratrol is a bioactive polyphenol, found in grapes, red wine, and peanuts, and has recently garnered much media and scientific attention for its diverse beneficial health effects as a nutritional supplement or nutraceutical. Of particular interest are the well-documented cardioprotective effects of resveratrol that are mediated by diverse mechanisms, including its antioxidant and vascular effects. However, it is now becoming clear that resveratrol may also exhibit direct effects on cardiac function and rhythm through modulation of signaling pathways that regulate cardiac remodeling and ion channel activity that controls cardiac excitability. Resveratrol may therefore possess antiarrhythmic properties that contribute to the cardiovascular benefits of resveratrol. Atrial fibrillation (AF) is the most common cardiac arrhythmia, although current therapies are suboptimal. Our laboratory has been studying resveratrol's effects on cardiac ion channels and remodeling pathways, and we initiated a drug development program aimed at generating novel resveratrol derivatives with improved efficacy against AF when compared to currently available therapeutics. This review therefore focuses on the effects of resveratrol and new derivatives on a variety of cardiac ion channels and molecular pathways that contribute to the development and maintenance of atrial fibrillation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".