Bioactive Natural Constituents from Food Sources—Potential Use in Hypertension Prevention and Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prevention and management of hypertension are the major public health challenges worldwide. Uncontrolled high blood pressure may lead to a shortened life expectancy and a higher morbidity due to a high risk of cardiovascular complications such as coronary heart disease (which leads to heart attack) and stroke, congestive heart failure, heart rhythm irregularities, and kidney failure etc. In recent years, it has been recognized that many dietary constituents may contribute to human cardiovascular health. There has been an increased focus on identifying these natural components of foods, describing their physiological activities and mechanisms of actions. Grain, vegetables, fruits, milk, cheese, meat, chicken, egg, fish, soybean, tea, wine, mushrooms, and lactic acid bacteria are various food sources with potential antihypertensive effects. Their main bioactive constituents include angiotensin I-converting enzyme (ACE) inhibitory peptides, vitamins C and E, flavonoids, flavanols, cathecins, anthocyanins, phenolic acids, polyphenols, tannins, resveratrol, polysaccharides, fiber, saponin, sterols, as well as K, Ca, and P. They may reduce blood pressure by different mechanisms, such as ACE inhibition effect, antioxidant, vasodilatory, opiate-like, Ca(2+) channel blocking, and chymase inhibitory activities. These functional foods may provide new therapeutic applications for hypertension prevention and treatment, and contribute to a healthy cardiovascular population. The present review summarizes the antihypertensive food sources and their bioactive constituents, as well as physiological mechanisms of dietary products, especially focusing on ACE inhibitory activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it