Fabrication of Mouse Embryonic Stem Cell-Derived Layered Cardiac Cell Sheets Using a Bioreactor Culture System
Bibliographic record
Abstract
Bioengineered functional cardiac tissue is expected to contribute to the repair of injured heart tissue. We previously developed cardiac cell sheets using mouse embryonic stem (mES) cell-derived cardiomyocytes, a system to generate an appropriate number of cardiomyocytes derived from ES cells and the underlying mechanisms remain elusive. In the present study, we established a cultivation system with suitable conditions for expansion and cardiac differentiation of mES cells by embryoid body formation using a three-dimensional bioreactor. Daily conventional medium exchanges failed to prevent lactate accumulation and pH decreases in the medium, which led to insufficient cell expansion and cardiac differentiation. Conversely, a continuous perfusion system maintained the lactate concentration and pH stability as well as increased the cell number by up to 300-fold of the seeding cell number and promoted cardiac differentiation after 10 days of differentiation. After a further 8 days of cultivation together with a purification step, around 1 × 10(8) cardiomyocytes were collected in a 1-L bioreactor culture, and additional treatment with noggin and granulocyte colony stimulating factor increased the number of cardiomyocytes to around 5.5 × 10(8). Co-culture of mES cell-derived cardiomyocytes with an appropriate number of primary cultured fibroblasts on temperature-responsive culture dishes enabled the formation of cardiac cell sheets and created layered-dense cardiac tissue. These findings suggest that this bioreactor system with appropriate medium might be capable of preparing cardiomyocytes for cell sheet-based cardiac tissue.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".