Bibliographic record
Abstract
Hypertension is a leading cause of morbidity and mortality worldwide. Individuals with hypertension are at increased risk of stroke, heart disease and kidney failure. Although the etiology of essential hypertension has a genetic component, lifestyle factors such as diet play an important role. Reducing dietary salt is effective in lowering blood pressure in salt-sensitive individuals. Insulin resistance and altered glucose metabolism are common features of hypertension in humans and animal models, with or without salt sensitivity. Altered glucose metabolism leads to increased formation of advanced glycation end products. Insulin resistance is also linked to oxidative stress, and alterations in the nitric oxide pathway and renin angiotensin system. A diet rich in protein containing the semiessential amino acid, arginine, and arginine treatment, lowers blood pressure in humans and in animal models. This may be due to the ability of arginine to improve insulin resistance, decrease advanced glycation end products formation, increase nitric oxide, and decrease levels of angiotensin II and oxidative stress, with improved endothelial cell function and decreased peripheral vascular resistance. The Dietary Approaches to Stop Hypertension (DASH) study demonstrated that the DASH diet, rich in vegetables, fruits and low-fat dairy products; low in fat; and including whole grains, poultry, fish and nuts, lowered blood pressures even more than a typical North American diet with similar reduced sodium content. The DASH diet is rich in protein; the blood pressure-lowering effect of the DASH diet may be due to its higher arginine-containing protein, higher antioxidants and low salt content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".