Classification of Kinin Receptors
Bibliographic record
Abstract
This minireview is divided into three parts: the first part refers to the characterization and classification of kinin receptors using agonists and antagonists in isolated tissues (classical pharmacology). Two kinin receptors have been considered on the basis of their distinct pharmacology, namely the B1 receptor of the rabbit aorta (rank order of potency of agonists: LysdesArg9BK > desArg9BK > or = LysBK > BK; apparent affinities of antagonists Lys[Leu8]desArg9BK (pIC50 8.4) > [Leu8]desArg9BK (pIC50 7.4) >>> HOE 140, a B2 receptor antagonist, pIC50<5.0), and the B2 receptor of the rabbit jugular vein (potency of agonists: LysBK = BK >>> LysdesArg9BK = desArg9BK and HOE 140 (pIC50 9.0) >>> Lys[Leu8]desArg9BK, pIC50<5.0). The second part describes species-related B1 receptor subtypes, demonstrated by different pharmacological profiles of agonists and antagonists: human, rabbit and pig subtypes (LysdesArg9BK >> desArg9BK and Lys[Leu8]desArg9BK > [Leu8]desArg9BK) and dog, rat, mouse and hamster B1 receptors (desArg9BK = LysdesArg9BK and [Leus]desArg9BK = Lys[Leu8]desArg9BK). Affinities of agonists and antagonists in some species (man, rabbit, pig) are significantly increased (at least 10-fold) by the presence of a Lys at their N-terminus. The last part describes species-related B2 receptor subtypes supported by results obtained with non-peptide receptor agonists (FR 190997) and antagonists (FR 173657). While BK acts as a full agonist in man, rabbit and pig, FR 190997 behaves as a full agonist on human, as partial agonist on rabbit, and as pure antagonist on pig B2 receptors. Various hypotheses are considered to interpret these findings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".