Kallikrein–kinin system as the dominant mechanism to counteract hyperactive renin–angiotensin system
Bibliographic record
Abstract
The renin-angiotensin system (RAS) generates, maintains, and makes worse hypertension and cardiovascular diseases (CVDs) through its biologically active component angiotensin II (Ang II), that causes vasoconstriction, sodium retention, and structural alterations of the heart and the arteries. A few endogenous vasodilators, kinins, natriuretic peptides, and possibly angiotensin (1-7), exert opposite actions and may provide useful therapeutic agents. As endothelial autacoids, the kinins are potent vasodilators, active natriuretics, and protectors of the endothelium. Indeed, the kallikrein-kinin system (KKS) is considered the dominant mechanism for counteracting the detrimental effects of the hyperactive RAS. The 2 systems, RAS and KKS, are controlled by the angiotensin-converting enzyme (ACE) that generates Ang II and inactivates the kinins. Inhibitors of ACE can reduce the impact of Ang II and potentiate the kinins, thus contributing to restore the cardiovascular homeostasis. In the last 20 years, ACE-inhibitors (ACE-Is) have become the drugs of first choice for the treatments of the major CVDs. ACE-Is not only reduce blood pressure, as sartans also do, but by protecting and potentiating the kinins, they can reduce morbidity and mortality and improve the quality of life for patients with CVDs. This paper provides a brief review of the literature on this topic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".