MEndR: An In Vitro Functional Assay to Predict In Vivo Muscle Stem Cell‐Mediated Repair
Bibliographic record
Abstract
Abstract Functional assessment of stem cell‐mediated endogenous repair relies on animal studies. Here an in vitro assay is described that recapitulates important early steps of the in vivo skeletal muscle endogenous repair (MEndR) process. The assay is integrated with a custom semi‐automated image analysis pipeline to enable high‐content data analysis of donor‐derived muscle fiber content and morphology. Myotube sheets, generated by infiltrating a cellulose scaffold with myoblasts, are engrafted with muscle stem cells (MuSCs), injured to induce a regenerative microenvironment, and muscle repair is assessed. Significantly, the spatiotemporal dynamics of in vitro repair closely matched those observed in vivo, when both stem cells and injury are present. By exploiting the easy imaging geometry of the engineered tissue, cellular mechanisms of action driving the MuSC response to the regenerative template are explored. In vivo outcomes of two known modulators of MuSC‐mediated repair, measured by donor fiber production, MuSC niche repopulation, and response to a secondary injury, are phenocopied in the platform only when both the stem cells and injured 3D template are present. The MEndR platform represents a powerful opportunity to explore MuSC‐mediated repair and potentially compress the discovery pipeline by combining drug screening and validation in one step.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".