The response of muscle progenitor cells to cutaneous thermal injury
Bibliographic record
Abstract
BACKGROUND: Severe burn results in a systemic response that leads to significant muscle wasting. It is believed that this rapid loss in muscle mass occurs due to increased protein degradation combined with reduced protein synthesis. Alterations in the microenvironment of muscle progenitor cells may partially account for this pathology. The aim of this study was to ascertain the response of muscle progenitor cells following thermal injury in mice and to enlighten the cellular cascades that contribute to the muscle wasting. METHODS: C57BL/6 mice received a 20% total body surface area (TBSA) thermal injury. Gastrocnemius muscle was harvested at days 2, 7, and 14 following injury for protein and histological analysis. RESULTS: We observed a decrease in myofiber cross-sectional area at 2 days post-burn. This muscle atrophy was compensated for by an increase in myofiber cross-sectional area at 7 and 14 days post-burn. Myeloperoxidase (MPO)-positive cells (neutrophils) increased significantly at 2 days. Moreover, through Western blot analysis of two key mediators of the proteolytic pathway, we show there is an increase in Murf1 and NF-κB 2 days post-burn. MPO-positive cells were also positive for NF-κB, suggesting that neutrophils attain NF-κB activity in the muscle. Unlike inflammatory and proteolytic pathways, the number of Pax7-positive muscle progenitor cells decreased significantly 2 days post-burn. This was followed by a recovery in the number of Pax7-positive cells at 7 and 14 days, suggesting proliferation of muscle progenitors that accompanied regrowth. CONCLUSION: Our data show a biphasic response in the muscles of mice exposed to burn injury, with phenotypic characteristics of muscle atrophy at 2 days while compensation was observed later with a change in Pax7-positive muscle progenitor cells. Targeting muscle progenitors may be of therapeutic benefit in muscle wasting observed after burn 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 | 0.000 |
| 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) | 0.001 | 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".