Preliminary Pages
Bibliographic record
Abstract
Peptides act as vasoconstrictors (for instance angiotensins, vasopressin) or vasodilators (the kinins, the neurokinins), both through direct activation of specific receptors in the vascular smooth muscles or indirectly through the release of other endogenous inhibitors of the vascular tone. Kinins and neurokinins as well as their multiple receptors have been analyzed in the present study to assess the possible contributions of peptides to vasodilatation. Kinin receptors, B1 and B2, have been characterized, using new selective agonists and antagonists. B1 and B2 receptors appear to present in endothelium (B2) and in smooth muscles (B2, B1) of a variety of isolated vessels of the dog and the rabbit, where they subserve both stimulatory and inhibitory effects. Vasodilator inhibitory mechanisms depend on the release of the endothelium-relaxing factor and/or of prostanoids from the endothelium or the smooth muscles, especially in the dog renal vessels, where both B1and B2 receptors appear to be involved in causing vasodilatation. B2 receptors have also been shown to activate cardiovascular reflexes through a direct action on sensory fibers or on reflexogenic areas of the epicardium. Three types of receptors for neurokinins, namely NK-1, NK-2 and NK-3, have been identified by the use of naturally occurring peptides and of some analogues that act as selective agonists of a single receptor type. NK-1 receptors (particularly sensitive to substance P) have been shown to be present in endothelia where they promote the release of the endothelium relaxing factor, while NK-2 receptors (sensitive to neurokinin A) are found in the pulmonary artery of the rabbit and act directly to contract the smooth muscle. NK-3 receptors (sensitive to neurokinin B) may be present in the veins where they act as venoconstrictors, increasing also the vascular permeability. Thus, peptides and their receptors participate through various mechanisms in the regulation of vascular tone and peripheral blood flow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.841 | 0.729 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".