Mutations change excitability and the probability of re-entry in a computational model of cardiac myocytes in the sleeve of the pulmonary vein
Bibliographic record
Abstract
Abstract Atrial fibrillation (AF) is a common health problem with substantial individual and societal costs. The origin of AF has been debated for more than a century, and the precise, biophysical mechanisms that are responsible for the initiation and maintenance of the chaotic electrochemical waves that define AF, remains unclear. It is well accepted that the outlet of the pulmonary veins is the primary anatomical site of AF initiation, and that electrical isolation of these regions remains the most effective treatment for AF. Furthermore, it is well known that certain ion channel or transporter mutations can significantly increase the likelihood of AF. Here, we present a computational model capable of characterizing functionally important features of the microanatomical and electrophysiological substrate that represents the transition from the pulmonary veins (PV) to the left atrium (LA) of the human heart. This model is based on a finite element representation of every myocyte in a segment of this (PV/LA) region. Thus, it allows for investigation a mix of typical PV and LA myocytes. We use the model to investigate the likelihood of ectopic beats and re-entrant waves in a cylindrical geometry representing the transition from PV to LA. In particular, we investigate and illustrate how six different AF- associated mutations can alter the probability of ectopic beats and re-entry in this region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".