Induced pluripotent stem-cell-derived cardiomyocytes: cardiac applications, opportunities, and challenges
Bibliographic record
Abstract
Chronic diseases are the primary cause of mortality worldwide, accounting for 67% of deaths. One of the major challenges in developing new treatments is the lack of understanding of the exact underlying biological and molecular mechanisms. Chronic cardiovascular diseases are the single most common cause of death worldwide, and sudden deaths due to cardiac arrhythmias account for approximately 50% of all such cases. Traditional genetic screening for genes involved in cardiac disorders is labourious and frequently fails to detect the mutation that explains or causes the disorder. However, when mutations are identified, human induced pluripotent stem cells (hiPSCs) derived from affected patients make it possible to address fundamental research questions directly relevant to human health. As such, hiPSC technology has recently been used to model human diseases and patient-specific hiPSC-derived cardiomyocytes (hiPSC-CMs) thus offer a unique opportunity to investigate potential disease-causing genetic variants in their natural environment. The purpose of this review is to present the current state of knowledge regarding hiPSC-CMs, including their potential, limitations, and challenges and to discuss future prospects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".