Regulation of antioxidant enzyme expression in response to exercise in skeletal muscles of dyslipidemic mice
Bibliographic record
Abstract
Dyslipidemia combined with a sedentary lifestyle is a well‐characterized combination of risk factor for cardiovascular diseases (CVD). Although preventive physical exercise delays the onset of CVD, the underlying mechanisms are not clear. We hypothesized that exercise regulates antioxidant enzyme expression that counteracts the deleterious effects of dyslipidemia. To test this hypothesis, 3‐month old (m/o) severely dyslipidemic (LDLR−/−, hApoB+/+) mice (SD) and wild type (WT) controls were exposed or not (sedentarity, SED) for 3 months to voluntary physical training (PT, □4.5 km/day). At 6‐m/o, the endothelial function of the femoral artery was measured using a pressure myograph and the surrounding skeletal muscles were isolated to quantify antioxidant gene expression by qPCR. No differences in endothelial function were noted between groups. In contrast, GPx1 expression decreased by 25% (p<0.05) in SD mice; PT, however, increased (p<0.05) its expression in both WT (+25%) and SD (+54%) mice. Prdx3 mRNA was higher (p<0.05) in SD compared to WT by 44% and 50% in SED and PT mice. Likewise, MnSOD and Txn1 expressions were elevated in SD mice compared to the other groups (p<0.05) and that was prevented by PT. In conclusion, analysis of mRNA expression reveals that the regulation of antioxidant enzymes is sensitive to dyslipidemia and PT, unveiling the dynamic and sensitive nature of the endogenous antioxidants.
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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 | 0.001 | 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) | 0.002 | 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".