Seeding Bioreactor-Produced Embryonic Stem Cell-Derived Cardiomyocytes on Different Porous, Degradable, Polyurethane Scaffolds Reveals the Effect of Scaffold Architecture on Cell Morphology
Bibliographic record
Abstract
A successful regenerative therapy to treat damage incurred after an ischemic event in the heart will require an integrated approach including methods for appropriate revascularization of the infarct site, mechanical recovery of damaged tissue, and electrophysiological coupling with native cells. Cardiomyocytes are the ideal cell type for heart regeneration because of their inherent electrical and physiological properties, and cardiomyocytes derived from embryonic stem cells (ESCs) represent an attractive option for tissue-engineering therapies. An important step in developing tissue engineering-based approaches to cardiac cell therapy is understanding how scaffold architecture affects cell behavior. In this work, we generated large numbers of ESC-derived cardiomyocytes in bioreactors and seeded them on porous, 3-dimensional scaffolds prepared using 2 different techniques: electrospinning and thermally induced phase separation (TIPS). The effect of material macro-architecture on the adhesion, viability, and morphology of the seeded cells was determined. On the electrospun scaffolds, cells were elongated in shape, a morphology typical of cultured ESC-derived cardiomyocytes, whereas on scaffolds fabricated using TIPS, the cells retained a rounded morphology. Despite these gross phenotypic and physiological differences, sarcomeric myosin and connexin 43 expression was evident, and contracting cells were observed on both scaffold types, suggesting that morphological changes induced by material macrostructure do not directly correlate to functional differences.
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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) | 0.000 | 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".