Fiber alignment and coculture with fibroblasts improves the differentiated phenotype of murine embryonic stem cell‐derived cardiomyocytes for cardiac tissue engineering
Bibliographic record
Abstract
Embryonic stem cells (ESCs) are an important source of cardiomyocytes for regenerating injured myocardium. The successful use of ESC-derived cardiomyocytes in cardiac tissue engineering requires an understanding of the important scaffold properties and culture conditions to promote cell attachment, differentiation, organization, and contractile function. The goal of this work was to investigate how scaffold architecture and coculture with fibroblasts influences the differentiated phenotype of murine ESC-derived cardiomyocytes (mESCDCs). Electrospinning was used to process an elastomeric biodegradable polyurethane (PU) into aligned or unaligned fibrous scaffolds. Bioreactor produced mESCDCs were seeded onto the PU scaffolds either on their own or after pre-seeding the scaffolds with mouse embryonic fibroblasts (MEFs). Viable mESCDCs attached to the PU scaffolds and were functionally contractile in all conditions tested. Importantly, the aligned scaffolds led to the anisotropic organization of rod-shaped cells, improved sarcomere organization, and increased mESCDC aspect ratio (length-to-diameter ratio) when compared to cells on the unaligned scaffolds. In addition, pre-seeding the scaffolds with MEFs improved mESCDC sarcomere formation compared to mESCDCs cultured alone. These results suggest that both fiber alignment and pre-treatment of scaffolds with fibroblasts improve the differentiation of mESCDCs and are important parameters for developing engineered myocardial tissue constructs using ESC-derived cardiac cells.
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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".