Vasoactive peptides in cardiovascular (patho)physiology
Bibliographic record
Abstract
Numerous vasoactive agents play an important physiological role in regulating vascular tone, reactivity and structure. In pathological conditions, alterations in the regulation of vasoactive peptides result in endothelial dysfunction, vascular remodeling and vascular inflammation, which are important processes underlying vascular damage in cardiovascular disease. Among the many vasoactive agents implicated in vascular (patho)biology, angiotensin II (Ang II), endothelin (ET), serotonin and natriuretic peptides appear to be particularly important because of their many pleiotropic actions and because they have been identified as potential therapeutic targets in cardiovascular disease. Ang II, ET-1, serotonin and natriuretic peptides mediate effects via specific receptors, which belong to the group of G-protein-coupled receptors. ET, serotonin and Ang II are primarily vasoconstrictors with growth-promoting actions, whereas natriuretic peptides, specifically atrial, brain and C-type natriuretic peptides, are vasodilators with natriuretic effects. Inhibition of vasoconstrictor actions with drugs that block peptide receptors, compounds that inhibit enzymes that generate vasoactive peptides or agents that increase levels of natriuretic peptides are potentially valuable therapeutic tools in the management of cardiovascular diseases. This review focuses on ET, natriuretic peptides and serotonin. The properties and distribution of these vasoactive agents and their receptors, mechanisms of action and implications in cardiovascular (patho)physiology will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".