Modelling Skeletal Muscle Ageing and Repair In Vitro
Bibliographic record
Abstract
Healthy skeletal muscle can regenerate after ischaemic, mechanical, or toxin-induced injury, but ageing impairs that regeneration potential. This has been largely attributed to dysfunctional satellite cells and reduced myogenic capacity. Understanding which signalling pathways are associated with reduced myogenesis and impaired muscle regeneration can provide valuable information about the mechanisms driving muscle ageing and prompt the development of new therapies. To investigate this, we developed a high-throughput in vitro model to assess muscle regeneration in chemically injured C2C12 and human myotube-derived young and aged myoblast cultures. We observed a reduced regeneration capacity of aged cells, as indicated by an attenuated recovery towards preinjury myotube size and myogenic fusion index at the end of the regeneration period, in comparison with younger muscle cells that were fully recovered. RNA-sequencing data showed significant enrichment of KEGG signalling pathways, PI3K-Akt, and downregulation of GO processes associated with muscle development, differentiation, and contraction in aged but not in young muscle cells. Data presented here suggest that repair in response to in vitro injury is impaired in aged vs. young muscle cells. Our study establishes a framework that enables further understanding of the factors underlying impaired muscle regeneration in older age.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".