Perivascular Adipose Does Not Affect Endothelium‐Dependent Relaxation or Contractionin Spontaneously Hypertensive and Wistar Kyoto Rat Aorta
Bibliographic record
Abstract
This study examined the effect of the presence or absence of naturally adhering perivascular adipose tissue (PVAT+ or PVAT‐, respectively) on acetylcholine (Ach)‐stimulated endothelium‐dependent vasorelaxation inisolated phenylephrine‐precontractedaortic rings, and onAch‐stimulated endothelium‐dependent vasocontraction in isolated quiescent (non‐precontracted) L‐N G ‐Nitroarginine methyl ester‐preincubated aortic rings from 25‐30 wk old Spontaneously Hypertensive rats (SHR; n=4‐8) and Wistar Kyoto rats (WKY; n=6‐8). Ach from ‐10.0 to ‐5.0 LogM resulted in a dose‐dependent relaxation response in PVAT‐ rings from both WKY (Maximum Amplitude (Max Amp): 85.6±5.8%; EC50: ‐7.72±0.08LogM; Area Under the Curve (AUC): 236±18) and SHR (Max Amp: 87.3±6.1%; EC50: ‐7.90±0.07LogM; AUC: 255±23), with no statistically significant differences found between strains in any dose‐response curve‐fit parameter (p<0.05). In both WKY and SHR, the curve‐fit parameters of the Ach‐stimulated relaxation response in PVAT+ rings were found not to be statistically different from thoseof the PVAT‐ rings.Inquiescent rings, Ach resulted in a dose‐dependent vasocontractile response in PVAT‐ rings from both WKY (Max Amp: 16.5±1.6%; EC50: ‐6.32±0.10LogM; AUC: 28±4) and SHR (Max Amp: 43.7±7.0%; EC50: ‐6.25±0.11LogM; AUC: 59±10), with a greater Max Amp and AUC in SHR (p<0.01). In both WKY and SHR, the curve‐fit parameters of the Ach‐stimulated contractile response in PVAT+ rings were found not to be statistically different from those of the PVAT‐ rings. These preliminary data suggest that, under the conditions examined, the presence of PVAT does not affect endothelium‐dependent relaxation or contraction. Funded by NSERC Canada.
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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".