Bradykinin, an Important Mediator of the Cardiovascular Effects of Metallopeptidase Inhibitors: Experimental and Clinical Evidences
Bibliographic record
Abstract
Bradykinin (BK), a nanopeptide (Arg-Pro-Pro-Gly-Phe-Ser-Pro-Phe-Arg), is the prototype of a family of powerful vasodilatory peptides, the kinins. Although known for a long time only as a pro-inflammatory peptide, BK is now considered an important mediator of the benefits of angiotensin I-converting enzyme inhibitors (ACEi). In fact, over the years, numerous papers have been dedicated to BK, and a number of them dealt with its cardiovascular effects. Different experimental and clinical arguments plead for a role of BK in the cardiovascular effects of ACEi. BK may also be an important mediator of a new class of drugs, vasopeptidase inhibitors. These single molecules simultaneously inhibit the activity of neutral endopeptidase 24.11 and angiotensin-converting enzyme, two kininases, with similar nanomolar inhibitory constants. The protective effect of omapatrilat, the first of this new class of drugs, on BK degradation at the cardiomyocyte and endothelial level, two target sites for metallopeptidase inhibitors, has also been documented and compared to that of ACEi. The purpose of this paper is to review the different experimental and clinical arguments that support a cardioprotective role of this vasodilatory peptide, BK. J Clin Basic Cardiol 2001; 4: 39-46.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".