Calcium arrhythmogenicity of Purkinje fibers: importance of the animal model
Bibliographic record
Abstract
Ventricular tachycardias (VTs) and fibrillations (VFs) are frequent complications of ischemic myocardial infarction (MI). Because their initiation mechanism remains unknown, these arrhythmias are virtually unpredictable and often degenerate into cardiac arrest and syncope without immediate medical assistance. Electrical mapping and ablation techniques have located the origin of ischemic arrhythmias in the terminal arborizations of the cardiac conduction system, the Purkinje fibers. A classical model of MI in the dog has demonstrated that abnormal calcium (Ca 2+ ) cycling in the Purkinje cells (Pcells) is the source of non-driven depolarizations (DADs) in the conduction tissue and is likely to create the pro-arrhythmic conditions of human ischemic heart. A better understanding of Ca 2+ abnormalities in Pcells post infarction is an evident prerequisite for elucidating the mechanism of ischemic arrhythmias. Nevertheless, a unique Ca 2+ handling system was discovered in Pcells, exhibiting fundamental differences compared with the well-known model of Excitation-Contraction coupling of ventricular cardiomyocytes. This cellular specificity of Purkinje fibers was observed in large mammalian species but not in murine hearts, where Purkinje cells are comparable to ventricular myocytes and designed to respond to 400–600 stimulations/min. The present report reviews the mechanism of Ca 2+ arrhythmogenicity in Pcells of large mammalian hearts and documents the need for animal models that simulate the size and function of human hearts to study ischemic arrhythmias.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".