The Mechanisms of Propofol-Induced Vascular Relaxation and Modulation by Perivascular Adipose Tissue and Endothelium
Bibliographic record
Abstract
BACKGROUND: Propofol causes hypotension due to relaxation of vascular smooth muscle cells through its direct or indirect vasodilator effects. Perivascular adipose tissue (PVAT) and endothelium attenuate vascular contraction, and the function of PVAT is altered in hypertension and diabetes. Whether PVAT affects the action of anesthetics on vascular function is unknown. We studied the mechanisms of propofol-induced relaxation in relation to the involvement of PVAT and endothelium. METHODS: Thoracic aortic rings from Wistar rats were prepared with or without PVAT (PVAT+ and PVAT-), intact endothelium (E+), or both, or with the endothelium removed (E-) for functional studies. RESULTS: In phenylephrine precontracted vessels, propofol-induced relaxation was highest with both PVAT and E+ and lowest in vessels denuded of both PVAT and endothelium. Propofol-induced relaxation occurred via both endothelium-dependent and -independent mechanisms. The relaxation response induced by propofol was significantly reduced by nitric oxide synthase inhibitor (l-NNA), K(+) channel blockers (tetraethylammonium and glibenclamide) in E+ and E- vessels, and by soluble guanylyl cyclase inhibitor 1H-(1,2,4) oxadiazolo (4,3-A) quinazoline-1-one and hydrogen peroxide scavenger (catalase) in E- vessels. The presence of PVAT significantly enhanced the relaxation response induced by propofol. In contrast to phenylephrine precontracted vessels in which the presence of PVAT or endothelium had an effect, in vessels precontracted with KCl, propofol-induced relaxation was similar among the 4 types of vessel preparation. CONCLUSIONS: PVAT enhances the relaxation effect induced by propofol in rat aorta through both endothelium-dependent and endothelium-independent pathways thus highlighting the clinical importance of PVAT.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".