Muscle-Specific ECM Fibers Made with Anchored Cell Sheet Engineering Support Tissue Regeneration in Rat Models of Volumetric Muscle Loss
Bibliographic record
Abstract
Abstract Volumetric muscle loss (VML) represents a critical unmet need in regenerative medicine, with no established standard of care. This study introduces a novel therapeutic strategy using tissue-specific skeletal muscle extracellular matrix (ECM) fibers fabricated using scaffold-free Anchored Cell Sheet Engineering technology. These engineered fibers replicate the native ECM composition and microarchitecture of skeletal muscle, incorporating essential structural and basement membrane proteins. In a rat VML model, engineered ECM fibers demonstrated a promising regenerative capacity compared to commercial porcine-derived small intestine submucosa (SIS) ECM. Over an 8-week period, the engineered fibers preserved muscle volume and weight, regulated inflammatory and fibrotic responses, and promoted vascularization. In contrast, SIS was rapidly degraded by week 4 and associated with excessive fibrotic response. Force recovery in the muscles treated with engineered ECM fibers was lower at the 8-week time point (77% compared to 91% in the control group), but histological and immunohistochemical analyses revealed newly formed, dispersed muscle fibers exclusively within the repaired muscle tissue treated with engineered ECM fibers. Importantly, only in cases where engineered ECM fibers were used, muscle weight was preserved, resulting in similar normalized force-to-weight recovery across all groups (87% in the test group vs. 88% in the control group). The histological analyses further demonstrated ongoing tissue remodeling, indicative of sustained regeneration, in contrast to the premature fibrotic healing observed in the other groups. A novel quantitative image analysis workflow using a custom Python script, enabled objective assessment of spatial tissue heterogeneity through histology and immunohistochemistry images, setting a new standard for tissue regeneration analysis. These findings establish engineered tissue-specific ECM fibers as a transformative approach for VML treatment and lay the groundwork for translation to clinical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".