Mechanical Stress Promotes Maturation of Human Myocardium From Pluripotent Stem Cell-Derived Progenitors
Bibliographic record
Abstract
Recent advances in pluripotent stem cell biology and directed differentiation have identified a population of human cardiovascular progenitors that give rise to cardiomyocytes, smooth muscle, and endothelial cells. Because the heart develops from progenitors in 3D under constant mechanical load, we sought to test the effects of a 3D microenvironment and mechanical stress on differentiation and maturation of human cardiovascular progenitors into myocardial tissue. Progenitors were derived from embryonic stem cells, cast into collagen hydrogels, and left unstressed or subjected to static or cyclic mechanical stress. Compared to 2D culture, the unstressed 3D environment increased cardiomyocyte numbers and decreased smooth muscle numbers. Additionally, 3D culture suppressed smooth muscle α-actin content, suggesting diminished cell maturation. Cyclic stress-conditioning increased expression of several cardiac markers, including β-myosin heavy chain and cardiac troponin T, and the tissue showed enhanced calcium dynamics and force production. There was no effect of mechanical loading on cardiomyocyte or smooth muscle specification. Thus, 3D growth conditions favor cardiac differentiation from cardiovascular progenitors, whereas 2D conditions promote smooth muscle differentiation. Mechanical loading promotes cardiomyocyte structural and functional maturation. Culture in 3-D facilitates understanding how cues such as mechanical stress affect the differentiation and morphogenesis of distinct cardiovascular cell populations into organized, functional human cardiovascular tissue. Stem Cells 2015;33:2148-2157.
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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".