Review: The skeletal muscle extracellular matrix: Possible roles in the regulation of muscle development and growth
Bibliographic record
Abstract
Velleman, S. G., Shin, J., Li, X. and Song, Y. 2012. Review: The skeletal muscle extracellular matrix: Possible roles in the regulation of muscle development and growth. Can. J. Anim. Sci. 92: 1–10. Skeletal muscle fibers are surrounded by an extrinsic extracellular matrix environment. The extracellular matrix is composed of collagens, proteoglycans, glycoproteins, growth factors, and cytokines. How the extracellular matrix influences skeletal muscle development and growth is an area that is not completely understood at this time. Studies on myogenesis have largely been directed toward the cellular components and overlooked that muscle cells secrete a complex extracellular matrix network. The extracellular matrix modulates muscle development by acting as a substrate for muscle cell migration, growth factor regulation, signal transduction of information from the extracellular matrix to the intrinsic cellular environment, and provides a cellular structural architecture framework necessary for tissue function. This paper reviews extracellular matrix regulation of muscle growth with a focus on secreted proteoglycans, cell surface proteoglycans, growth factors and cytokines, and the dynamic nature of the skeletal muscle extracellular matrix, because of its impact on the regulation of muscle cell proliferation and differentiation during myogenesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".