Recent human ventricular cell action potential models under varied ischaemic conditions
Bibliographic record
Abstract
Investigating arrhythmic mechanisms during ischaemia is essential to improve clinical therapy. However, experimental data in human is scarce. Computational models are an essential tool for bridging this gap. Recent human ventricular cell action potential (AP) models have been built with data from healthy cells, thus their applicability to studies of ischaemia is mostly unknown. We have carried out a simulation study in single cell and tissue under normal and varied ischaemic conditions using 4 recent human models: ten Tusscher et al. 2006 (TP06), Grandi et al. 2010 (GPB), Carro et al. 2011 (CRLP), and O'hara et al. 2011 (ORd). We varied two parameters that play an important role in arrhythmogenesis during ischaemia: extracellular potassium concentration ([K+]o) and peak conductance of the ATP-sensitive inward-rectifying potassium current (IK(ATP)). To assess the applicability of these models to simulate ischaemia, we calculated AP duration (APD) and post-repolarisation refractoriness (PRR), biomarkers of arrhythmic risk. Results show that all models displayed the expected APD shortening due to IK(ATP) activation and hyperkalaemia. Furthermore, all models, apart from the ORd, reproduced an increase in PRR. The GPB did not show propagation of excitation for [K+]o=9mM. This study suggests that the CRLP and TP06 models are the most suitable for performing human-specific simulations of arrhythmogenesis during myocardial ischaemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".