Dual effects of the small-conductance Ca <sup>2+</sup> -activated K <sup>+</sup> current on human atrial electrophysiology and Ca <sup>2+</sup> -driven arrhythmogenesis: an <i>in silico</i> study
Bibliographic record
Abstract
Abstract By sensing changes in intracellular Ca 2+ , small-conductance Ca 2+ -activated K + (SK) channels dynamically regulate the dynamics of the cardiac action potential (AP) on a beat-to-beat basis. Given their predominance in atria vs. ventricles, SK channels are considered a promising atrial-selective pharmacological target against atrial fibrillation (AF), the most common cardiac arrhythmia. However, the precise contribution of SK current (I SK ) to atrial arrhythmogenesis is poorly understood, and may potentially involve different mechanisms that depend on species, heart rates, and degree of AF-induced atrial remodeling. Both reduced and enhanced I SK have been linked to AF. Similarly, both SK channel up- and downregulation have been reported in chronic AF (cAF) vs. normal sinus rhythm (nSR) patient samples. Here, we use our multi-scale modeling framework to obtain mechanistic insights into the contribution of I SK in human atrial myocyte electrophysiology. We simulate several protocols to quantify how I SK modulation affects the regulation of AP duration (APD), Ca 2+ transient, refractoriness, and occurrence of alternans and delayed afterdepolarizations (DADs). Our simulations show that I SK activation shortens the APD and atrial effective refractory period, limits Ca 2+ cycling, and slightly increases the propensity for alternans in both nSR and cAF conditions. We also show that increasing I SK counteracts DAD development by reducing the coupling between transmembrane potential and intracellular Ca 2+ . Taken together, our results suggest that increasing I SK in human atrial myocytes could promote reentry, while protecting against triggered activity. Depending on the leading arrhythmogenic mechanism, I SK inhibition may thus be a beneficial or detrimental anti-AF strategy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".