Evaluation of the effects of pioglitazone on perivascular adipose tissue function, properties, and structure in a rat model of type-2 diabetes
Bibliographic record
Abstract
Perivascular adipose tissue (PVAT) plays an important role in many physiological and pathological processes, such as regulation of vascular tone. The aim of this study is to evaluate the effects of pioglitazone on functional, structural, and biochemical properties of PVAT in an experimental model of type-2 diabetes (T2DM). T2DM was induced by high-fat-diet/low-dose-streptozotocin in rats, and pioglitazone (20 mg/kg/p.o.) was administered for 6 weeks. Changes in biochemical parameters, PVAT-mass, vascular-reactivity in thoracic-aorta, as well as PVAT adipocytokine and PPARG-expression levels, and histopathology were evaluated. Pioglitazone administration improved blood glucose and lipid profiles in T2DM. Pioglitazone did not change the anticontractile effect of PVAT on aortic contractile reactivity and besides, had no influence on endothelium-dependent and -independent relaxation responses. Pioglitazone administration increased PVAT-mass and tumor necrotizing factor-α levels, while adiponectin, leptin, and interleukin-6 levels were unchanged. Also, a prominent increase was observed in PPARG-expression in T2DM-Pio group. Moreover, pioglitazone decreased liver steatosis, aortic wall thickening, and myocardial damage, whereas increased adipocyte size and adiposity in PVAT. Overall, pioglitazone treatment changed the mass and in part the inflammatory profile of PVAT but did not modify vasoreactivity in T2DM. This study provides novel findings in relationship with the adipogenic effect of pioglitazone and PVAT function.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".