Resveratrol enhances post-injury muscle regeneration by regulating antioxidant and mitochondrial biogenesis
Bibliographic record
Abstract
Resveratrol (RES), a natural polyphenolic compound, has shown promise in enhancing skeletal muscle regeneration and metabolic function. This study aims to explore the impact of RES on muscle regeneration after injury through the regulation of antioxidant capacity and mitochondrial biogenesis. RES treatment significantly increased the ratio of tibialis anterior muscle mass to body weight, alongside reduced fasting glucose levels. Following cardiotoxin-induced injury, RES treatment improved muscle regeneration, characterized by greater formation of new fibers and better structural repair compared to the control. Notably, gene expression analyses demonstrated that RES-treated mice exhibited elevated levels of myogenic markers, such as paired box 7 (Pax7), myogenic factor 5 (Myf5), myoblast determination protein (MyoD), and Myogenin (MyoG). Concurrently, yes-associated protein (YAP) increased, underscoring its role in regulating satellite cell activity. Transcriptomic analysis identified enriched pathways related to muscle regeneration and mitochondrial biogenesis, with increased expression of mitochondrial transcription factors and higher mitochondrial DNA content in RES-treated mice. Furthermore, RES enhanced antioxidant capacity via the Kelch-like ECH-associated protein 1 (KEAP-1)/nuclear factor erythroid 2-related factor 2 (NRF2)/heme oxygenase-1 (HO-1) signaling pathway, as indicated by elevated activities of total antioxidant capacity, Glutathione peroxidase (GSH-PX), and superoxidase dismutase (SOD). Collectively, these findings suggest that RES not only promotes muscle regeneration but also supports mitochondrial function and antioxidant defenses, positioning it as a food-derived pharmaceutical for targeting muscle repair after injury.
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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 | 0.001 |
| 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".