Adapting Muscle Endogenous Repair (MEndR) Assay for Academic and Industry Adoption
Bibliographic record
Abstract
Skeletal muscle is an essential tissue and in healthy individuals it can self-repair through the activity of muscle stem cells (MuSCs). In age and disease, MuSC endogenous repair proficiency is lost, and therapeutics that target and restore functionality are of interest. However, in vivo validation of drugs with potential to stimulate muscle endogenous repair proves to be a bottleneck in drug discovery and to address this problem we developed a MuSC mediated skeletal muscle endogenous repair (MEndR) phenotypic culture assay that predicts gold-standard in vivo assay outcomes. Roadblocks exist that prevent scale-up, and assay adoption. My thesis addresses two issues: phenotypic data analysis and the use of cells with access and scale-up limitations. My thesis offers a semi-automated pipeline to analyze MuSC-derived myotubes from confocal images with the power to stratify MuSC drug treatment response. I also identify immortalized myoblast cell lines with which to engineer the muscle templates.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".