Using human induced pluripotent stem cell-derived cardiomyocytes to understand the mechanisms driving cardiomyocyte maturation
Bibliographic record
Abstract
Cardiovascular diseases are the leading cause of mortality and reduced quality of life globally. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a personalized platform to study inherited heart diseases, drug-induced cardiac toxicity, and cardiac regenerative therapy. However, the immaturity of CMs obtained by current strategies is a major hurdle in utilizing hiPSC-CMs at their fullest potential. Here, the major findings and limitations of current maturation methodologies to enhance the utility of hiPSC-CMs in the battle against a major source of morbidity and mortality are reviewed. The most recent knowledge of the potential signaling pathways involved in the transition of fetal to adult CMs are assimilated. In particular, we take a deeper look on role of nutrient sensing signaling pathways and the potential role of cap-independent translation mediated by the modulation of mTOR pathway in the regulation of cardiac gap junctions and other yet to be identified aspects of CM maturation. Moreover, a relatively unexplored perspective on how our knowledge on the effects of preterm birth on cardiovascular development can be actually utilized to enhance the current understanding of CM maturation is examined. Furthermore, the interaction between the evolving neonatal human heart and brown adipose tissue as the major source of neonatal thermogenesis and its endocrine function on CM development is another discussed topic which is worthy of future investigation. Finally, the current knowledge regarding transcriptional mediators of CM maturation is still limited. The recent studies have produced the groundwork to better understand CM maturation in terms of providing some of the key factors involved in maturation and development of metrics for assessment of maturation which proves essential for future studies on in vitro PSC-CMs maturation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".