The myosin activator omecamtiv mecarbil: a promising new inotropic agent
Bibliographic record
Abstract
Heart failure became a leading cause of mortality in the past few decades with a progressively increasing prevalence. Its current therapy is restricted largely to the suppression of the sympathetic activity and the renin–angiotensin system in combination with diuretics. This restrictive strategy is due to the potential long-term adverse effects of inotropic agents despite their effective influence on cardiac function when employed for short durations. Positive inotropes include inhibitors of the Na+/K+ pump, β-receptor agonists, and phosphodiesterase inhibitors. Theoretically, Ca2+ sensitizers may also increase cardiac contractility without resulting in Ca2+ overload; nevertheless, their mechanism of action is frequently complicated by other pleiotropic effects. Recently, a new positive inotropic agent, the myosin activator omecamtiv mecarbil, has been developed. Omecamtiv mecarbil binds directly to β-myosin heavy chain and enhances cardiac contractility by increasing the number of the active force-generating cross-bridges, presumably without major off-target effects. This review focuses on recent in vivo and in vitro results obtained with omecamtiv mecarbil, and discusses its mechanism of action at a molecular level. Based on clinical data, omecamtiv mecarbil is a promising new tool in the treatment of systolic heart failure.
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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 | 0.001 |
| 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".