Muscle satellite cell and atypical myogenic progenitor response following exercise
Bibliographic record
Abstract
Skeletal muscle satellite cells play an essential role in muscle regeneration and exercise adaptation. In recent years atypical myogenic progenitors (non-satellite-cell muscle stem cells) have been identified in skeletal muscle and have been hypothesized to play an important role in the process of muscle regeneration. It remains unknown, however, whether any populations other than satellite cells play a significant role in repair and adaptation following exercise-induced damage. We assessed the response of the satellite cell population and the CD45+:Sca-1+ cell population, previously shown to support muscle regeneration following cardiotoxin-induced injury, after acute eccentrically biased exercise in wild-type mice. We observed evidence of focal muscle damage and repair following the exercise protocol using electron microscopy, hematoxylin-eosin staining, and single-fiber analysis. In addition, we observed an approximately sixfold increase in the number of Myf5-expressing cells by 48 h, which remained elevated until at least 96 h following exercise. We did not, however, observe any significant expansion of the CD45+:Sca-1+ cell population or commitment of resident CD45+:Sca-1+ cells to the myogenic lineage. Furthermore, expression of Wnt gene family members, previously associated with myogenic specification of CD45+:Sca-1+ cells, did not differ following exercise. Therefore, we conclude that muscle satellite cells are the primary responders to exercise-induced stress and that the CD45+:Sca-1+ myogenic progenitors do not contribute to muscle repair/adaptation following exercise.
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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) | 0.002 | 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".