Potential of Resveratrol in Preventing the Development of Heart Failure
Bibliographic record
Abstract
Heart failure is a leading cause of mortality in North America and most other parts of the world. Its development is secondary to diseases such as hypertension, coronary artery disease, valvular heart disease or cardiomyopathies. Current therapies for preventing heart failure include the use of diuretics, inhibitors of the renin-angiotensin-aldosterone system and β-adrenergic receptor blockers. These treatments have been moderately successful; however, the incidence of heart failure is on the rise. In view of the limited success with existing therapies it has become very important to pursue alternative strategies. One such approach could be the use of food-derived compounds that have medical benefits, and can be administered as dietary supplements. In this context, resveratrol, a polyphenol, found predominantly in grapes and berries, and a major component of red wine, has been recently drawing significant attention for its cardioprotective properties. Current research on resveratrol has focused on examining its potential in preventing or regressing defects in cardiac structure and function in experimental models of heart disease. In this paper, we will discuss the potential of resveratrol as a nutraceutical in preventing the development of heart failure in the future. Keywords: Resveratrol, heart failure, nutraceuticals
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".