Y<sub>1</sub>- and α<sub>1</sub>-receptor control of basal hindlimb vascular tone
Bibliographic record
Abstract
The role of endogenous Y(1)-receptor activation on skeletal muscle vasculature under baseline conditions is currently debated and no in vivo studies have been performed to address this issue. Therefore, this study was designed to address the effect of Y(1)-receptor and/or alpha(1)-adrenoceptor antagonism on basal hindlimb vascular conductance in male Sprague-Dawley rats in vivo. Left hindlimb vascular conductance, carotid artery mean arterial pressure, and heart rate were measured during low volume infusion of N(2)-(diphenylacetyl)-N-[(4-hydroxyphenyl)methyl]-d-arginine amide (BIBP3226; 100 microg/kg), prazosin (20 microg/kg), and combined blockade to the left hindlimb. Vascular conductance increased 1.5 +/- 0.5 microl.min(-1).mmHg(-1) with BIBP3226 infusion, 1.7 +/- 0.5 microl.min(-1).mmHg(-1) with prazosin infusion, and 4.8 +/- 1.0 microl.min(-1).mmHg(-1) with combined blockade (P < 0.05). Interestingly, systolic vascular conductance increased in all three conditions, but diastolic vascular conductance only increased in the two conditions where BIBP3226 was present. These data indicate that Y(1)-receptor activation plays an important role in the regulation of vascular conductance in the resting rat hindlimb. Furthermore, this effect was of the same magnitude as the alpha(1)-adrenoceptor contribution. The differential flow profiles following alpha(1) blockade with and without Y(1)-receptor blockade supports local differences in receptor distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".