Satellite cell activation on fibers: modeling events in vivo — an invited review
Bibliographic record
Abstract
Knowledge of the events underlying satellite cell activation and the counterpart maintenance of quiescence is essential for planning therapies that will promote the growth and regeneration of skeletal muscle in healthy, disease and aging. By modeling those events of satellite cell activation in studies of single muscle fibers or muscles in culture, the roles of mechanical stretching and nitric oxide are becoming understood. Recent studies demonstrated that stretch-induced activation is very rapid and exhibits some features of satellite cell heterogeneity. As well, gene expression studies showed that expression of the c-met receptor gene rises rapidly after stretching muscles in culture compared to those without stretch. This change in gene expression during activation, and the maintenance of quiescence in both normal and dystrophic muscles are dependent on NO, as they are blocked by inhibition of nitric oxide synthase (NOS). Mechanical, contractile activity is the defining feature of muscle function. Therefore, ongoing studies of stretch effects in satellite cell activation and quiescence in quiescent fiber and muscle cultures provides appropriate models by which to explore the regulatory steps in muscle in vivo under many conditions related to disease, repair, rehabilitation, growth and the prevention or treatment of atrophy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".