Modelling Skeletal Muscle Ageing and Repair In Vitro
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it