Implication of nigral tachykinin NK<sub>3</sub> receptors in the maintenance of hypertension in spontaneously hypertensive rats: a pharmacologic and autoradiographic study
Bibliographic record
Abstract
1. The role of nigral tachykinin NK(1), NK(2) and NK(3) receptors in central cardiovascular regulation was studied by measuring the effects of selective agonists and antagonists on mean arterial pressure (MAP) and heart rate (HR) after bilateral microinjection into the substantia nigra of spontaneously hypertensive rats (SHR). Quantitative in vitro autoradiography was also performed in the midbrain of SHR and Wistar-Kyoto (WKY) with the NK(3) receptor ligand [(125)I]-HPP-Senktide. 2. Tachycardia was elicited by the NK(1) ([Sar(9),Met(O(2))(11)]SP) and NK(2) ([betaAla(8)]NKA(4-10)) agonists at 25 and 100 pmol while the NK(3) agonist (senktide, 50 and 100 pmol) had no significant effect. The three agonists had no effect on behaviour, and increases in MAP were elicited by the NK(1) agonist only. 3. Whereas antagonists at NK(1) (RP 67580, 500 pmol) and NK(2) (SR 48968, 500 pmol) receptors had no significant effect on MAP and HR, the NK(3) antagonist (R-820, 500 pmol) reduced MAP for over 3 h in SHR. That anti-hypertensive effect did not occur after intracerebroventricular or intravenous injection of R-820. Also, R-820 had no cardiovascular effect in WKY. 4. The affinity (K(D): 0.7 nM) and densities of specific NK(3) receptor binding sites measured in the substantia nigra, ventral tegmental area, hippocampus and amygdala were not significantly different in SHR and WKY. 5. It is concluded that endogenous tachykinins exert a tonic activity on NK(3) receptors in the substantia nigra of SHR to maintain high blood pressure. Hence, nigral tachykinin NK(3) receptors may represent a promising therapeutic target in the treatment of arterial hypertension.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".