Molecular regulation of myogenic progenitor populations
Bibliographic record
Abstract
Skeletal muscle regeneration and adaptation to exercise require the actions of muscle satellite cells. Muscle satellite cells are thought to play an integral role in the process of exercise adaptation, but have also been shown to possess the capacity to fully regenerate muscle tissue following destructive muscle injury. We now know that molecular regulation of satellite cells involves the coordinated actions of a series of transcriptional networks that leads to myogenic commitment, cell-cycle entry, proliferation, and terminal differentiation. Additionally, Pax7 is a paired-box transcription factor that has been identified as playing a critical role in satellite cell regulation. It remains debatable, however, whether Pax7 is required for the specification of satellite cells and (or) whether it is playing a vital role in self-renewal and maintenance of the satellite cell population. In recent years, the emergence of atypical myogenic progenitor populations has added a new dimension to muscle repair, and significant interest has been focused on identifying populations such as bone-marrow-derived stem cells that have the ability to contribute to muscle. Interestingly, elucidating the molecular regulation of myogenic progenitor populations has involved animal models of muscle regeneration, with questionable relevance for human muscle adaptation to exercise. This paper highlights the current state of knowledge on the molecular regulation of satellite cells, explores the potential contribution of atypical myogenic progenitors, and discusses the information gathered from animal regeneration models in terms of its relevance to the process of exercise adaptation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".