Perivascular adipose tissue promotes vasoconstriction: the role of superoxide anion
Bibliographic record
Abstract
OBJECTIVES: Recent studies have demonstrated that perivascular adipose tissue (PVAT) releases vascular relaxation factor(s). In this study, we examined if PVAT releases other vasoactive factors in response to perivascular nerve activation by electrical field stimulation (EFS). METHODS AND RESULTS: In Wistar-Kyoto rats, rings of superior mesenteric artery (MA) with intact PVAT (PVAT (+)) showed a greater contractile response to EFS than rings with PVAT removed (PVAT (-)). Superoxide dismutase (SOD) reduced the contractile response to EFS more in PVAT (+) MA than in PVAT (-) MA. Inhibitors of NAD(P)H oxidase and cyclooxygenase exerted a greater inhibition on EFS-induced contraction in PVAT (+) MA than in PVAT (-) MA. Inhibitors of tyrosine kinase (tyrphostin A25) and MAPK/ERK (U 0126) attenuated EFS-induced contraction in PVAT (+) MA in a concentration-related manner, while inactive forms of these inhibitors (tyrphostin A1 and U 0124) did not inhibit the response. Exogenous superoxide augmented the contractile response to EFS and to phenylephrine in PVAT (-) MA, and this augmentation was blunted by inhibition of tyrosine kinase and MAPK/ERK. EFS increased superoxide generation in isolated PVAT and PVAT (+)/(-) MA, which was attenuated by NAD(P)H oxidase inhibition. RT-PCR showed the mRNA expression of p(67phox) subunit of NAD(P)H oxidase and immunohistochemical staining confirmed its localization in the adipocytes of PVAT. CONCLUSION: These results show that PVAT enhances the arterial contractile response to perivascular nerve stimulation through the production of superoxide mediated by NAD(P)H oxidase, and that this enhancement involves activation of tyrosine kinase and MAPK/ERK pathway.
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 | 0.000 |
| 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".