Using hiPSC‐CMs to Examine Mechanisms of Catecholaminergic Polymorphic Ventricular Tachycardia
Bibliographic record
Abstract
Abstract Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a potentially lethal inherited cardiac arrhythmia condition, triggered by physical or acute emotional stress, that predominantly expresses early in life. Gain‐of‐function mutations in the cardiac ryanodine receptor gene (RYR2) account for the majority of CPVT cases, causing substantial disruption of intracellular calcium (Ca2+) homeostasis particularly during the periods of β‐adrenergic receptor stimulation. However, the highly variable penetrance, patient outcomes, and drug responses observed in clinical practice remain unexplained, even for patients with well‐established founder RyR2 mutations. Therefore, investigation of the electrophysiological consequences of CPVT‐causing RyR2 mutations is crucial to better understand the pathophysiology of the disease. The development of strategies for reprogramming human somatic cells to human induced pluripotent stem cells (hiPSCs) has provided a unique opportunity to study inherited arrhythmias, due to the ability of hiPSCs to differentiate down a cardiac lineage. Employment of genome editing enables generation of disease‐specific cell lines from healthy and diseased patient‐derived hiPSCs, which subsequently can be differentiated into cardiomyocytes. This paper describes the means for establishing an hiPSC‐based model of CPVT in order to recapitulate the disease phenotype in vitro and investigate underlying pathophysiological mechanisms. The framework of this approach has the potential to contribute to disease modeling and personalized medicine using hiPSC‐derived cardiomyocytes. © 2021 Wiley Periodicals LLC.
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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) | 0.000 | 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 | 0.001 | 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".