Blood pressure regulation and micronutrients.
Bibliographic record
Abstract
This review attempts to delineate the underlying mechanisms leading to the development of hypertension as well as the function of vitamins and minerals in the regulation of blood pressure. Physiological processes that regulate cardiac output and systemic vascular resistance impact on the control of blood pressure. Metabolic abnormalities associated with the tetrad of hypertension, dyslipidaemia, glucose intolerance and obesity share insulin resistance, which might be organ or cell specific, as an underlying feature representing different tissue manifestation of a common cellular ionic defect. As Ca is at the centre of ionic regulation of cellular functions, vitamins involved in Ca regulation have a significant role in the control of blood pressure. The endothelium-dependent vasodilator, NO, is susceptible to oxidative damage. Hence, antioxidant vitamins and related factors regulate blood pressure through protection of NO. Robust evidence for the involvement of vitamin B6 (pyridoxine), vitamin C, vitamin D and vitamin E in the regulation of blood pressure have been reported. The well-known roles of Na, K, Ca, Mg and Cl have been explored further. The action of various vitamins on blood pressure regulation cannot always be explained on the basis of their conventionally recognised "vitamin function". The non-traditional functions of vitamins and their derivatives can be exploited as an adjunct to available pharmacological modalities in the treatment of hypertension.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".