Mechanical Stretch Regimen Enhances the Formation of Bioengineered Autologous Cardiac Muscle Grafts
Bibliographic record
Abstract
BACKGROUND: Surgical repair of congenital and acquired cardiac defects may be enhanced by the use of autologous bioengineered muscle grafts. These tissue-engineered constructs are not optimal in their formation and function. We hypothesized that a mechanical stretch regimen applied to human heart cells that were seeded on a three-dimensional gelatin scaffold (Gelfoam) would improve tissue formation and enhance graft strength. METHODS AND RESULTS: Heart cells from children undergoing repair of Tetralogy of Fallot were isolated and cultured. Heart cells were seeded on gelatin-matrix scaffolds (Gelfoam) and subjected to cyclical mechanical stress (n=7) using the Bio-Stretch Apparatus (80 cycles/minute for 14 days). Control scaffolds (n=7) were maintained under identical conditions but without cyclical stretch. Cell counting, histology, and computerized image analysis determined cell proliferation and their spatial distribution within the tissue-engineered grafts. Collagen matrix formation and organization was determined with polarized light and laser confocal microscopy. Uniaxial tensile testing assessed tissue-engineered graft function. Human heart cells proliferated within the gelatin scaffold. Remarkably, grafts that were subjected to cyclical stretch demonstrated increased cell proliferation and a marked improvement of cell distribution. Collagen matrix formation and organization was enhanced by mechanical stretch. Both maximal tensile strength and resistance to stretch were improved by cyclical mechanical stretch. CONCLUSION: The cyclical mechanical stretch regimen enhanced the formation of a three-dimensional tissue-engineered cardiac graft by improving the proliferation and distribution of seeded human heart cells and by stimulating organized matrix formation resulting in an order of magnitude increase in the mechanical strength of the graft.
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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 | 0.000 | 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) | 0.001 | 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".