Satellite Cells and Skeletal Muscle Regeneration
Bibliographic record
Abstract
Skeletal muscles are essential for vital functions such as movement, postural support, breathing, and thermogenesis. Muscle tissue is largely composed of long, postmitotic multinucleated fibers. The life-long maintenance of muscle tissue is mediated by satellite cells, lying in close proximity to the muscle fibers. Muscle satellite cells are a heterogeneous population with a small subset of muscle stem cells, termed satellite stem cells. Under homeostatic conditions all satellite cells are poised for activation by stimuli such as physical trauma or growth signals. After activation, satellite stem cells undergo symmetric divisions to expand their number or asymmetric divisions to give rise to cohorts of committed satellite cells and thus progenitors. Myogenic progenitors proliferate, and eventually differentiate through fusion with each other or to damaged fibers to reconstitute fiber integrity and function. In the recent years, research has begun to unravel the intrinsic and extrinsic mechanisms controlling satellite cell behavior. Nonetheless, an understanding of the complex cellular and molecular interactions of satellite cells with their dynamic microenvironment remains a major challenge, especially in pathological conditions. The goal of this review is to comprehensively summarize the current knowledge on satellite cell characteristics, functions, and behavior in muscle regeneration and in pathological conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".