Modulation of vascular function by perivascular adipose tissue: the role of endothelium and hydrogen peroxide
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Perivascular adipose tissue (PVAT) attenuates vascular contraction, but the mechanisms remain largely unknown. The possible involvement of endothelium (E) and hydrogen peroxide (H2O2) was investigated. EXPERIMENTAL APPROACH: Aortic rings from Wistar rats were prepared with both PVAT and E intact (PVAT+ E+), with either PVAT or E removed (PVAT- E+, or PVAT+ E-), or with both removed (PVAT- E-) for functional studies. Nitric oxide (NO) production was measured. KEY RESULTS: Contraction to phenylephrine and 5-HT respectively was highest in PVAT- E-, lowest in PVAT+ E+, and intermediate in PVAT+ E- or PVAT- E+. In bioassay experiments, transferring bathing solution incubated with a PVAT+ ring (donor) to a PVAT- ring (recipient) induced relaxation in the recipient. This relaxation was abolished by E removal, NO synthase inhibition, scavenging of NO, high extracellular K+, or blockade of calcium-dependent K+ channels (K(Ca)). The solution stimulated NO production in isolated endothelial cells and in PVAT- E+ rings. In E- rings, the contraction to phenylephrine of PVAT+ rings but not PVAT- rings was enhanced by catalase or soluble guanylyl cyclase (sGC) inhibitor, but reduced by superoxide dismutase and tiron. In PVAT- E- rings, H2O2 attenuated phenylephrine-induced contraction. This effect was counteracted by sGC inhibition. NO donor and H2O2 exhibited additive inhibition of the contraction to phenylephrine in PVAT- E- rings. CONCLUSION: PVAT exerts its anti-contractile effects through two distinct mechanisms: (1) by releasing a transferable relaxing factor which induces endothelium-dependent relaxation through NO release and subsequent K(Ca) channel activation, and (2) by an endothelium-independent mechanism involving H2O2 and subsequent activation of sGC.
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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 | 0.000 |
| 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".