Abstract 407: Modeling Atrial Fibrillation in a Dish Using Atrial iPSC Derived Cardiomyocytes
Bibliographic record
Abstract
Mutations in multiple genes have been linked with familial atrial fibrillation (AF) but the underlying pathophysiologic mechanisms and implications for therapy remain poorly understood. To characterize the electrophysiological phenotype of an AF-linked SCN5A mutation and assess novel mechanism-based therapies, we generated patient-specific atrial induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from a carrier with an SCN5A-E428K mutation and an unaffected family member using a differentiation protocol optimized for atrial differentiation generated atrial iPSC-CMs. The atrial iPSC-CMs carrying SCN5A-E428K displayed increased window and late Na+ current (INa,L), increased beating frequency and irregularity with triggered beats and prolongation of the action potential duration (APD) versus control atrial iPSC-CMs. The multi-electrode array (MEA) recordings of mutant atrial iPSC-CMs showed spontaneous arrhythmogenic activity with beat-to-beat irregularity. We further showed that targeted inhibition of the INa,L with ranolazine normalized the aberrant electrophysiologic phenotype in the SCN5A-E428K atrial iPSC-CMs and mexiletine reversed the sodium channel gating defects, and reduced beating frequency and irregularity. Our study illustrates the potential use of atrial iPSC-CMs for modeling AF in a dish, elucidating the underlying cellular mechanisms, and identifying novel mechanism-based therapies custom-tailored for individual patients.
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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 | 0.001 |
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".