Metformin alleviates muscle wasting post-thermal injury by increasing Pax7-positive muscle progenitor cells
Bibliographic record
Abstract
BACKGROUND: Profound skeletal muscle wasting and weakness is common after severe burn and persists for years after injury contributing to morbidity and mortality of burn patients. Currently, no ideal treatment exists to inhibit muscle catabolism. Metformin is an anti-diabetic agent that manages hyperglycemia but has also been shown to have a beneficial effect on stem cells after injury. We hypothesize that metformin administration will increase protein synthesis in the skeletal muscle by increasing the proliferation of muscle progenitor cells, thus mitigating muscle atrophy post-burn injury. METHODS: To determine whether metformin can attenuate muscle catabolism following burn injury, we utilized a 30% total burn surface area (TBSA) full-thickness scald burn in mice and compared burn injuries with and without metformin treatment. We examined the gastrocnemius muscle at 7 and 14 days post-burn injury. RESULTS: At 7 days, burn injury significantly reduced myofiber cross-sectional area (CSA) compared to sham, p < 0.05. Metformin treatment significantly attenuated muscle catabolism and preserved muscle CSA at the sham size. To investigate metformin's effect on satellite cells (muscle progenitors), we examined changes in Pax7, a transcription factor regulating the proliferation of muscle progenitors. Burned animals treated with metformin had a significant increase in Pax7 protein level and the number of Pax7-positive cells at 7 days post-burn, p < 0.05. Moreover, through BrdU proliferation assay, we show that metformin treatment increased the proliferation of satellite cells at 7 days post-burn injury, p < 0.05. CONCLUSION: In summary, metformin's various metabolic effects and its modulation of stem cells make it an attractive alternative to mitigate burn-induced muscle wasting while also managing hyperglycemia.
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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 | 0.001 |
| 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".