A necrotic stimulus is required to maximize matrix-mediated myogenesis in mice
Bibliographic record
Abstract
Biomaterials that are similar to skeletal muscle extracellular matrix have been shown to augment regeneration in ischemic muscle. In this study, treatment with a collagen-based matrix stimulated molecular myogenesis in an mdx murine model of necrosis. Matrix-treated animals ran ≥ 40% further, demonstrating functional regeneration, and expressed increased levels of myogenic transcripts. By contrast, matrix treatment was unable to induce transcriptional or functional changes in an MLC/SOD1(G93A) atrophic mouse model. In vitro, satellite cells were cultured under standard conditions, on matrix, in the presence of myocyte debris (to simulate a necrotic-like environment) or with both matrix and necrotic stimuli. Exposure to both matrix and necrotic stimuli induced the greatest increases in mef2c, myf5, myoD and myogenin transcripts. Furthermore, conditioned medium collected from satellite cells cultured with both stimuli contained elevated levels of factors that modulate satellite cell activation and proliferation, such as FGF-2, HGF and SDF-1. Application of the conditioned medium to C2C12 myoblasts accelerated maturation, as demonstrated by increased mef2c, myf5 and myogenin transcripts and fusion indexes. In summary, the collagen matrix required a necrotic stimulus to enhance the maturation of satellite cells and their secretion of a myogenic cocktail. Considering that matrix treatment supports myogenesis only in in vivo models that exhibit necrosis, this study demonstrates that a necrotic environment is required to maximize matrix-mediated 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".