Targeted inhibition of activin receptor-like kinase 5 signaling attenuates cardiac dysfunction following myocardial infarction
Bibliographic record
Abstract
Following myocardial infarction (MI), the heart undergoes a pathological process known as remodeling, which in many instances results in cardiac dysfunction and ultimately heart failure and death. Transforming growth factor-beta (TGF-beta) is a key mediator in the pathogenesis of cardiac remodeling following MI. We thus aimed to inhibit TGF-beta signaling using a novel orally active TGF-beta type I receptor [activin receptor-like kinase 5 (ALK5)] inhibitor (GW788388) to attenuate left ventricular remodeling and cardiac dysfunction in a rat model of MI. Sprague-Dawley rats underwent left anterior descending coronary artery ligation to induce experimental MI and then were randomized to receive GW788388 at a dosage of 50 mg.kg(-1).day(-1) or vehicle 1 wk after surgery. After 4 wk of treatment, echocardiography was performed before the rats were euthanized. Animals that received left anterior descending coronary artery ligation demonstrated systolic dysfunction, Smad2 activation, myofibroblasts accumulation, collagen deposition, and myocyte hypertrophy (all P < 0.05). Treatment with GW788388 significantly attenuated systolic dysfunction in the MI animals, together with the attenuation of the activated (phosphorylated) Smad2 (P < 0.01), alpha-smooth muscle actin (P < 0.001), and collagen I (P < 0.05) in the noninfarct zone of MI rats. Cardiomyocyte hypertrophy in MI hearts was also attenuated by ALK5 inhibition (P < 0.05). In brief, treatment with a novel TGF-beta type I receptor inhibitor, GW788388, significantly reduced TGF-beta activity, leading to the attenuation of systolic dysfunction and left ventricular remodeling in an experimental rat model of MI.
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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) | 0.001 | 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".