Marine Polyphenols in Cardiovascular Health: Unraveling Structure–Activity Relationships, Mechanisms, and Therapeutic Implications
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are responsible for significant mortality rates globally that have been raised due to the limitation of the available treatments and prevalence of CVDs. The innovative research and identification of potential preventives for CVDs are essential to alleviate global deaths and complications. The marine environment is a rich source of bioactive substances and provides a unique chemical arsenal against numerous ailments due to its unrivaled biodiversity. Marine polyphenolic compounds (MPCs) are unique because of their structural variety and biologically significant activity. Further, MPCs are well-reported for their valuable biological activities, such as anti-inflammatory, cardioprotective, and antioxidant, demonstrating encouraging results in preventing and treating CVDs. Therefore, investigation of the structure-activity relationship (SAR) between MPCs and CVDs provides insights that reveal how the structural components of these compounds affect their effectiveness. Further, comprehending this correlation is essential for advancing medications and nutraceuticals sourced from marine sources, which could transform the strategy for treating and preventing cardiovascular diseases. Therefore, this study provides a comprehensive analysis of existing research by emphasizing the role of MPCs in CVD treatments and evaluating the SAR between MPCs and CVDs with challenges and future directions.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".