Effects of angiotensin converting enzyme inhibition on cardiac innervation and ventricular arrhythmias after myocardial infarction
Bibliographic record
Abstract
PURPOSE: To investigate the influence of angiotensin-converting enzyme inhibitor (ACEI) on cardiac innervation and inducible ventricular arrhythmias (VAs) in healed myocardial infarction (MI). METHODS: Left anterior descending coronary artery was ligated to induce MI in 30 rabbits. After oral captopril (10mg/kg/d) for 8 weeks, electrophysiological study was performed to evaluate the incidence of inducible VAs. RT-PCR and immunohistochemistry were used to measure the cardiac innervation. RESULTS: Eight weeks after the operation, the incidence of inducible VAs in the MI-placebo group was higher (58.3%, 7/12) than in the sham operation group (16.7%, 2/12, P < 0.05). However, the incidence of inducible VAs in the MI-captopril group was lower (27.2%, 3/11) than in the MI-placebo group (P < 0.05). Proliferation and growth of nerve fibres in the MI-placebo group were mainly distributed at the periphery of the infarcted and perivascular regions of the myocardium. The density of nerve fibres increased in the MI-placebo group (3889+/-521 microm2/mm2) compared with the sham group (1727+/-304 microm2/mm2, P < 0.01) at the infarct border. In the MI-captopril group, the density of nerve fibres (3507+/-433 microm2/mm2) at the infarct border did not differ from that in the MI-placebo group (P=0.07). MI-induced abnormal nerve fibre distribution was partly restored by captopril treatment. CONCLUSION: In this study, prolonged captopril treatment was effective in preventing VAs in healed MI, partly by attenuating the spatial heterogeneity of cardiac innervation.
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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) | 0.001 | 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".