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1. A Latent Cardiomyocyte Regeneration Potential in Human Heart Disease

2. Predicting post-operative right ventricular failure using video-based deep learning

6. Abstract 15177: Unloading Reveals a Latent Cardiomyocyte Regeneration Potential in the Human Heart

11. Veno-arterial extracorporeal membrane oxygenation versus standard medical management for massive pulmonary embolism.

12. Wnt Activation and Reduced Cell-Cell Contact Synergistically Induce Massive Expansion of Functional Human iPSC-Derived Cardiomyocytes

17. A latent cardiomyocyte regeneration potential in the human heart

18. Abstract 15341: Multi-center Validation of a Novel Echocardiography Artificial Intelligence System to Predict Post-operative Right Ventricular Failure in LVAD Patients

20. Increased Expression of Membrane Type 3-Matrix Metalloproteinase in Human Atherosclerotic Plaque

23. Cholinergic regulation of vascular endothelial function by human ChAT(+) T cells

25. Cholinergic regulation of vascular endothelial function by human ChAT + T cells

26. Dynamics of Cell Generation and Turnover in the Human Heart

28. Supplemental Material - Veno-arterial extra-corporeal membrane oxygenation versus standard medical management for massive pulmonary embolism

35. Evidence for Cardiomyocyte Renewal in Humans

36. The evaluation of an electrocardiographic myocardial ischemia acuteness score to predict the amount of myocardial salvage achieved by early percutaneous coronary intervention: Clinical validation with myocardial perfusion single photon emission computed tomography and cardiac magnetic resonance

41. Risk of Developing Acute Kidney Injury on the VA ECLS Circuit in Patients with Acute Decompensated Heart Failure

42. Cell-based therapies for cardiac repair: a meeting report on scientific observations and European regulatory viewpoints

47. Decreased frequency of transplantation and lower post‐transplant survival free of re‐transplantation in LVAD patients with the new heart transplant allocation system

49. Predicting post-operative right ventricular failure using video-based deep learning

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