Faculty Opinions recommendation of Carvedilol and its new analogs suppress arrhythmogenic store overload-induced Ca2+ release.
Bibliographic record
Abstract
Carvedilol is one of the most effective beta blockers for preventing ventricular tachyarrhythmias in heart failure, but the mechanisms underlying its favorable antiarrhythmic benefits remain unclear. Spontaneous Ca(2+) waves, also called store overload-induced Ca(2+) release (SOICR), evoke ventricular tachyarrhythmias in individuals with heart failure. Here we show that carvedilol is the only beta blocker tested that effectively suppresses SOICR by directly reducing the open duration of the cardiac ryanodine receptor (RyR2). This unique anti-SOICR activity of carvedilol, combined with its beta-blocking activity, probably contributes to its favorable antiarrhythmic effect. To enable optimal titration of carvedilol's actions as a beta blocker and as a suppressor of SOICR separately, we developed a new SOICR-inhibiting, minimally beta-blocking carvedilol analog, VK-II-86. VK-II-86 prevented stress-induced ventricular tachyarrhythmias in RyR2-mutant mice and did so more effectively when combined with either of the selective beta blockers metoprolol or bisoprolol. Combining SOICR inhibition with optimal beta blockade has the potential to provide antiarrhythmic therapy that can be tailored to individual patients. PMID: 21743453 Funding information This work was supported by: Intramural NIH HHS, United States Canadian Institutes of Health Research, Canada NHLBI NIH HHS, United States Grant ID: R01 HL090905 NHLBI NIH HHS, United States Grant ID: R01 HL075210-08 NHLBI NIH HHS, United States Grant ID: R01 HL075210 NHLBI NIH HHS, United States Grant ID: R01 HL057832-15 NHLBI NIH HHS, United States Grant ID: R01 HL057832 NIAMS NIH HHS, United States Grant ID: R01 AR054098-04 NIAMS NIH HHS, United States Grant ID: R01 AR054098 More Less keyboard_arrow_down
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.365 |
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".