https://www.selleckchem.com/products/d-ap5.html Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.MicroRNAs (miRNA) have emerged as important post-transcriptional regulators of metabolic pathways that contribute to cellular and systemic lipoprotein homeostasis. Here, we identify two conserved miRNAs, miR-224, and miR-520d, which target gene networks regulating hepatic expression of the low-density lipoprotein (LDL) receptor (LDLR) and LDL clearance. In silico prediction of miR-224 and miR-520d target gene networks showed that they each repress multiple genes impacting the expression of the LDLR, including the chaperone molecules PCSK9 and IDOL that limit LDLR expression at the cell surface and the rate-limiting enzyme for cholesterol synthesis HMGCR, which is the target of LDL-lowering statin drugs. Using gain- and lo