Rate-dependent shortening of action potential duration increases ventricular vulnerability in failing rabbit heart
Bibliographic record
Abstract
Congestive heart failure (CHF) predisposes to ventricular fibrillation (VF) in association with electrical remodeling of the ventricle. However, much remains unknown about the rate-dependent electrophysiological properties in a failing heart. Action potential properties in the left ventricular subepicardial muscles during dynamic pacing were examined with optical mapping in pacing-induced CHF (n=18) and control (n=17) rabbit hearts perfused in vitro. Action potential durations (APDs) in CHF were significantly longer than those observed for controls at basic cycle lengths (BCLs)>1,000 ms but significantly shorter at BCLs<400 ms. Spatial APD dispersions were significantly increased in CHF versus control (by 17-81%), and conduction velocity was significantly decreased in CHF (by 6-20%). In both groups, high-frequency stimulation (BCLs<150 ms) always caused spatial APD alternans; spatially concordant alternans and spatially discordant alternans (SDA) were induced at 60% and 40% in control, respectively, whereas 18% and 82% in CHF. SDA in CHF caused wavebreaks followed by reentrant excitations, giving rise to VF. Incidence of ventricular tachycardia/VFs elicited by high-frequency dynamic pacing (BCLs<150 ms) was significantly higher in CHF versus control (93% vs. 20%). In CHF, left ventricular subepicardial muscles show significant APD shortenings at short BCLs favoring reentry formations following wavebreaks in association with SDA. High-frequency excitation itself may increase the vulnerability to VF in CHF.
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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".