POTENTIAL NUTRACEUTICAL EFFECT OF POLYPHENOLS AGAINST CARDIOVASCULAR DISORDER
Bibliographic record
Abstract
Nutraceutical is a food or its part which provides health and medical benefits. It is effective in the management of disorders or prevention of medicine. Globally, the leading cause of death is cardiovascular disease. The important contributors to cardiovascular risk are dietary factors. The effects of other cardiovascular risk factors are dyslipidemia, obesity, hyperlipidemia, diabetes mellitus and hypertension. The protective effects against the development of CVD have been demonstrated for several foods and dietary supplements. The polyphenol compounds act as nutraceuticals which is associated with the disease prevention triggered by oxidative stress. The potential efficacies of polyphenols include lignans, stilbenes, flavonoids and phenolic acids. The stanols, sterols and phytosterols are existing in a variety of plant products comprising numerous cereals, fruits, vegetables, seeds and nuts. The phytochemicals in legumes, cereal, vegetables and fruits are polyphenols. It is also found in beverages manufactured from plant products such as coffee, wine, cocoa and tea. In grapes, the phenolic compound includes flavonols, flavanols, anthocyanins, stilbenes (resveratrol) and phenolic acids. The sterols or stanols decrease LDL-C through up regulation of hepatic LDL receptors, reduction in intestinal cholesterol absorption and reduced production of endogenous cholesterol. In cocoa products, flavanols are concomitant with upgrading of lipid profile. The consumption of Polyphenols includes flavonoid rich vegetables and fruits which help in lowering the blood pressure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".