Proarrhythmic Effects of Electrolyte Imbalance in Virtual Human Atrial and Ventricular Cardiomyocytes
Bibliographic record
Abstract
Dialysis is prescribed to renal failure patients as a long-term chronic treatment. Whereas dialysis therapeutically normalizes serum electrolytes and removes small toxin molecules, it fails to alleviate fibroblast induced structural fibrosis, and unresponsive uremia. The simultaneous presence of altered electrolytes and fibrosis or uremia is thought to be pro-arrhythmogenic. This study explored potential arrhythmogenesis under pre-dialysis (high electrolyte levels) and post-dialysis (low physiological electrolyte levels) in the presence of fibrosis and uremia in human atrial and ventricular model cardiomyocytes.Two validated human cardiomyocyte models were used in this study that permitted simulation of cardiac atrial and ventricular detailed electrophysiology. Pathological conditions simulating active fibrosis and uremia were implemented in both models. Pre- and post-dialysis conditions were simulated using high and low electrolyte levels respectively. Arrythmogenesis was quantified by computing restitution curves that permitted identification of action potential duration and calcium transient alternans instabilities.In comparison to control conditions, fibrosis abbreviated action potential durations while uremia prolonged the same. Under pre-dialysis conditions, an elevation of serum electrolyte levels caused action potential durations to be abbreviated under both fibrosis and uremia. Alternans instability was observed in the ventricular cardiomyocyte model. Under post-dialysis conditions, lower levels of serum electrolytes promoted an abbreviated action potential duration under fibrosis but caused a large increase of the control and uremic action potential durations. Alternans instabilities were observed in the atrial cardiomyocyte model under post-dialysis conditions at physiological heart rates. The calcium transient restitution showed similar alternans instabilities.Co-existing conditions such as fibrosis and uremia in the presence of unphysiological electrolyte levels promote arrhythmogenesis and may require additional treatment to improve dialysis outcomes.Clinical Relevance. Knowledge of model response to clinically relevant conditions permits use of in silico modeling to better understand and dissect underlying arrhythmia mechanisms.
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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) | 0.000 | 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".