Abstract 1087: Effects of Oral Gap Junction Conduction-Enhancing Antiarrhythmic Peptide GAP-134 on Experimental Atrial Fibrillation in Dogs
Bibliographic record
Abstract
Background: Abnormal intercellular communication caused by connexin dysfunction may promote atrial fibrillation (AF). Objective: To assess the effect of the gap junction conduction-enhancing antiarrhythmic peptide GAP-134 on AF inducibility and maintenance in a new dog model of atrial cardiomyopathy. Methods and Results: Twenty four dogs underwent simultaneous atrioventricular pacing (2 weeks at 220 bpm, atrioventricular delay 0 ms), and were randomly assigned to placebo treatment (PACED-PLACEBO; 12 dogs) or oral GAP-134 (PACED-GAP 134; 12 dogs) (starting at day 0). Percent change in left atrial systolic area (Δ% LASA) from baseline to 2 weeks was calculated using trans-esophageal echocardiography. At 2 weeks, animals underwent an open chest electrophysiological study; conduction velocity (CV) when pacing at 150ms cycle length (CL), effective refractory periods (ERP) and AF vulnerability were measured. The mean plasma concentration of GAP-134 was 557 ± 239 nmol/L. GAP-134 increased CV (395.1 ± 63.2 vs 307.8 ± 54.6 mm/s, p<0.01), and shortened ERP at 200ms CL (104.0 ± 8.6 vs 112.8 ± 11.5 ms, P<0.05). GAP-134 significantly reduced AF inducibility [% burst attempts inducing AF] and maintenance [mean AF duration, number of episodes >10min] in dogs with less than 100% ΔLASA (n=5). In dogs with more structural remodeling (ΔLASA ≥100%, n=7), CV increased but AF inducibility was unaffected. Conclusions: Oral GAP-134 prevents CV slowing in a dog model of atrial cardiomyopathy, but attenuates AF inducibility and maintenance only in dogs with less mechanical remodeling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".