The Emergence of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes (hiPSC-CMs) as a Platform to Model Arrhythmogenic Diseases
Bibliographic record
Abstract
There is a need for improved in vitro models of inherited cardiac diseases to better understand basic cellular and molecular mechanisms and advance drug development. Most of these diseases are associated with arrhythmias, as a result of mutations in ion channel or ion channel-modulatory proteins. Thus far, the electrophysiological phenotype of these mutations has been typically studied using transgenic animal models and heterologous expression systems. Although they have played a major role in advancing the understanding of the pathophysiology of arrhythmogenesis, more physiological and predictive preclinical models are necessary to optimize the treatment strategy for individual patients. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have generated much interest as an alternative tool to model arrhythmogenic diseases. They provide a unique opportunity to recapitulate the native-like environment required for mutated proteins to reproduce the human cellular disease phenotype. However, it is also important to recognize the limitations of this technology, specifically their fetal electrophysiological phenotype, which differentiates them from adult human myocytes. In this review, we provide an overview of the major inherited arrhythmogenic cardiac diseases modeled using hiPSC-CMs and for which the cellular disease phenotype has been somewhat characterized.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".