Modeling transport of interstitial potassium in regional myocardial ischemia: effect on the injury current
Bibliographic record
Abstract
Myocardial ischemia leads to an efflux of potassium ions from affected cells. The resulting depolarization of the resting membrane is one of the main features of ischemic myocardium. It has been shown experimentally that a part of the surplus interstitial potassium is transported out of the ischemic zone, even if no coronary blood flow is present in the affected area. We propose to model this transport mechanism mathematically with a diffusion equation. This model explains the measured spatial profiles of extracellular potential and potassium concentration. In addition, it allows a quantitative prediction of the transmembrane current that flows as a result of ischemia-induced depolarization. This current is thought to play a role in arrhythmogenicity, which is an important cause of mortality in acute myocardial infarction. Our model predicts that this current reaches its maximum exactly on the border of the hypoxic area. An important depolarizing current would be present just within the border, where hypoxia is accompanied by a resting membrane potential that is only slightly elevated, due to coupling with the adjacent normal tissue. Still, in the presence of potassium transport the predicted current density is not large enough to explain ectopic activation on the lateral border of the ischemia. This suggests that activation is more likely to occur at the endocardium, where the potassium gradient is steeper.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".