The role of MOTS-c-mediated antioxidant defense in aerobic exercise alleviating diabetic myocardial injury
Bibliographic record
Abstract
Myocardial remodeling and dysfunction are commonly observed in type 2 diabetes mellitus (T2DM). Aerobic exercise can partly alleviate diabetes-induced myocardial dysfunction through its antioxidant actions. MOTS-c is a potential exercise mimic. This study aimed to investigate the effects of MOTS-c on improving diabetic heart function and its mechanism and to identify whether MOTS-c improved antioxidant defenses due to aerobic exercise. Herein, we established a rat model of T2DM induced by high-fat diet combined with a low-dose streptozotocin injection. Interventions were performed using intraperitoneal injections of MOTS-c (i.p. 0.5 mg/kg/day, 7 days/week) or aerobic exercise training (treadmill, 20 m/min, 60 min/day, 5 days/week) for 8 weeks. Myocardial ultrastructure was assessed using transmission electron microscopy (TEM), myocardial lipid peroxidation levels (MDA), superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) levels were assessed using colorimetric methods, and molecular analyses including MOTS-c, Kelch-like ECH-associated protein 1 (Keap1), Nuclear factor E2-related factor 2 (Nrf2), adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK)and phospho-AMPK (p-AMPK) were examined using Western blot. The results showed that MOTS-c, with or without exercise, reduced myocardial ultrastructural damage and improved glucolipid metabolism and cardiac function in T2DM. Furthermore, MOTS-c increased antioxidant markers such as SOD, CAT, and the protein expression of myocardial MOTS-c, Keap1, Nrf2, and p-AMPK. MOTS-c with exercise treatment reduced myocardial MDA and increased p-AMPK significantly comparing to only exercise or MOTS-c alone. Our findings suggest that MOTS-c may be a helpful supplement for overcoming exercise insufficiency and improving myocardial structure and function in diabetes.
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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".