Effects of Chronic Gap Junction Conduction–Enhancing Antiarrhythmic Peptide GAP-134 Administration on Experimental Atrial Fibrillation in Dogs
Bibliographic record
Abstract
BACKGROUND: Abnormal intercellular communication caused by connexin dysfunction may contribute to atrial fibrillation (AF). The present study assessed the effect of the gap junction conduction-enhancing antiarrhythmic peptide GAP-134 on AF inducibility and maintenance in a dog model of atrial cardiomyopathy. METHODS AND RESULTS: Twenty-four dogs subject to simultaneous atrioventricular pacing (220 bpm for 14 days) were randomly assigned to placebo treatment (PACED-CTRL; 12 dogs) or oral GAP-134 (2.9 mg/kg BID; PACED-GAP-134; 12 dogs) starting on day 0. UNPACED-CTRL (4 dogs) and UNPACED-GAP-134 (4 dogs) served as additional control groups. Change in left atrial (LA) systolic area from baseline to 14 days was calculated using transoesophageal echocardiography. At 14 days, animals underwent an open-chest electrophysiological study. PACED-CTRL dogs (versus UNPACED-CTRL) had a shorter estimated LA wavelength (8.0+/-1.4 versus 24.4+/-2.5 cm, P<0.05) and a greater AF vulnerability (mean AF duration, 1588+/-329 versus 25+/-34 seconds, P<0.05). Oral GAP-134 had no effect on AF vulnerability in UNPACED dogs. Compared with PACED-CTRL dogs, PACED-GAP-134 dogs had a longer estimated LA wavelength (10.2+/-2.8 versus 8.0+/-1.4 cm, respectively, P<0.05). Oral GAP-134 did not significantly reduce AF inducibility or maintenance in the entire group of 24 PACED dogs; in a subgroup of dogs (n=11) with less than 100% increase in LA systolic area, oral GAP-134 reduced AF induction from 100% to 40% and mean AF duration from 1737+/-120 to 615+/-280 seconds (P<0.05). CONCLUSIONS: Oral GAP-134 reduces pacing-induced decrease in LA wavelength and appears to attenuate AF vulnerability in dogs with less atrial mechanical remodeling. Gap junction modulation may affect AF in some circumstances.
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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) | 0.001 | 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 | 0.000 | 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".