Exercise training prevents the perivascular adipose tissue-induced aortic dysfunction with metabolic syndrome
Bibliographic record
Abstract
The aim of the study was to determine the effects of exercise training on improving the thoracic perivascular adipose tissue (tPVAT) phenotype (inflammation, oxidative stress, and proteasome function) in metabolic syndrome and its subsequent actions on aortic function. METHODS: Lean and obese (model of metabolic syndrome) Zucker rats (n=8/group) underwent 8-weeks of control conditions or treadmill exercise (70% of max speed, 1 h/day, 5 days/week). At the end of the intervention, the tPVAT was removed and conditioned media was made. The cleaned aorta was attached to a force transducer to assess endothelium-dependent and independent dilation in the presence or absence of tPVAT-conditioned media. tPVAT gene expression, inflammatory /oxidative phenotype, and proteasome function were assessed. RESULTS: NO in tPVAT; (3) a reduction in tPVAT oxidant production; and (4) an improved tPVAT proteasome function. Regarding aortic function, endothelium-dependent dilation was greater in exercised lean and obese groups vs. controls (p < 0.05). Lean control tPVAT improved aortic relaxation, whereas obese control tPVAT decreased aortic relaxation. In contrast, the obese Ex-tPVAT increased aortic dilation, whereas the lean Ex-tPVAT did not affect aortic dilation. CONCLUSION: Overall, exercise had the most dramatic impact on the obese tPVAT reflecting a change towards an environment with less oxidant load, less inflammation and improved proteasome function. Such beneficial changes to the tPVAT micro-environment with exercise likely played a significant role in mediating the improvement in aortic function in metabolic syndrome following 8 weeks of exercise.
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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) | 0.001 | 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".