Effects of Angiotensin-Converting Enzyme Inhibition on the Development of the Atrial Fibrillation Substrate in Dogs With Ventricular Tachypacing–Induced Congestive Heart Failure
Bibliographic record
Abstract
BACKGROUND: Atrial structural remodeling creates a substrate for atrial fibrillation (AF), but the underlying signal transduction mechanisms are unknown. This study assessed the effects of ACE inhibition on arrhythmogenic atrial remodeling and associated mitogen-activated protein kinase (MAPK) changes in a dog model of congestive heart failure (CHF). METHODS AND RESULTS: Dogs were subjected to various durations of ventricular tachypacing (VTP, 220 to 240 bpm) in the presence or absence of oral enalapril 2 mg. kg(-1). d(-1). VTP for 5 weeks induced CHF, local atrial conduction slowing, and interstitial fibrosis and prolonged atrial burst pacing-induced AF. Atrial angiotensin II concentrations and MAPK expression were increased by tachypacing, with substantial changes in phosphorylated forms of c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38-kinase. Enalapril significantly reduced tachypacing-induced changes in atrial angiotensin II concentrations and ERK expression. Enalapril also attenuated the effects of CHF on atrial conduction (conduction heterogeneity index reduced from 3.1+/-0.4 to 1.9+/-0.2 ms/mm, P<0.05), atrial fibrosis (from 11.9+/-1.1% to 7.5+/-0.4%, P<0.01), and mean AF duration (from 651+/-164 to 218+/-75 seconds, P<0.05). Vasodilator therapy of a separate group of VTP dogs with hydralazine and isosorbide mononitrate did not alter CHF-induced fibrosis or AF promotion. CONCLUSIONS: CHF-induced increases in angiotensin II content and MAPK activation contribute to arrhythmogenic atrial structural remodeling. ACE inhibition interferes with signal transduction leading to the AF substrate in CHF and may represent a useful new component to AF therapy.
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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) | 0.001 | 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 | 0.001 |
| 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".