Preventing Atrial Fibrillation in Hypertrophic Cardiomyopathy using Angiotensin-Converting Enzyme (ACE) Inhibitors and Angiotensin Receptor Blockers (ARBs)
Bibliographic record
Abstract
Hypertrophic cardiomyopathy (HCM), a genetic cardiovascular disease, is the leading cause of cardiac death in young people, often due to atrial fibrillation (AF). AF is generally treated using antiarrhythmics and anticoagulants, which have adverse side effects after long-term use, and are therefore unsuitable for young HCM patients. AF is characterized by a rapid and irregular atrial heartbeat, marked by a short action potential duration and atrial effective refractory period in atrial cardiomyocytes. Prior studies have indicated that the renin-angiotensin system is involved in lowering both, so it has been hypothesized that angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), which inhibit the renin-angiotensin system, could prevent AF. Therefore, in this study, we propose a research protocol to examine the viability of ACE inhibitors and ARBs as prophylactic measures against the development of AF in HCM patients. To test this, we suggest extracting atrial cardiomyocytes from HCM patients by performing enzyme dissociation on myocardial tissue. The isolated cardiomyocytes will then be treated in vitro with an ACE inhibitor, an ARB, a combination of both, or a control saline solution, and the patch-clamp technique will be used to determine the frequency and duration of their action potentials. We expect action potential duration and atrial effective refractory period to be longer in treated cells, while neither medication will provide a greater advantage, and, as prior research suggests, the combination will not yield significant benefits. The study will continue by testing the effects of ACE inhibitors and ARBs on the function of atrial myocardial organoids created from differentiated stem cells with an HCM mutation. The results of this study could present a new preventative measure against AF for HCM patients which would be safe for long-term use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".