A Model of Cell Population Dynamics in Skeletal Muscle Regeneration
Bibliographic record
Abstract
Muscle stem cells (MuSCs), also commonly called satellite cells (SCs), work to repair damaged muscle after injury and are key targets for treating muscle diseases such as Duchenne's Muscular Dystrophy (DMD). However, clinical application of stem cell therapy has run into challenges as it is hampered by the complexity of manipulating or augmenting stem cell systems in vivo as well as their diverse differentiation pathways. This is partially due to the cell heterogeneity, dynamic gene regulatory mechanisms that drive cells to make fate decisions, and the dynamic interplay between intrinsic mechanisms and extrinsic factors constituting the stem cell niche. Previous models have attempted to simulate the response of healthy or damaged tissue to continual injury-induced damage. These studies have often relied on the analysis of multiple experiments conducted on injured skeletal muscles. However, accurately capturing the influx of each cell within a single muscle poses a significant challenge as variable sources may present potential biases due to inter-experiment variability, differences in sample collection, injury protocols, and technical variations. By leveraging single cell RNA sequencing (scRNA-seq) data, we obtain comprehensive and unbiased information on cell type proportions within the same muscle, avoiding biases associated with pooling data from multiple sources.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".