The association of congenital aortic valve malformation and aortic dissection is analyzed. Over a 30 year period, 186 patients with non-iatrogenic aortic dissection were studied at necropsy. The aortic valve was tricuspid in 170 (91.4%), bicuspid in 14 (7.5%) and unicuspid in 2 (1.1%). Among the 16 patients with aortic dissection and a congenitally malformed valve, the age at death ranged from 17 to 82 years (mean 52) and 13 (81%) were men. The entrance tear of the aortic dissection was located in the ascending aorta in all 16 patients with a malformed valve but in only 68% of those with a tricuspid aortic valve. The aortic valve was stenotic in 6 of the 16 patients with a congenitally malformed valve. Fatal rupture of the false channel occurred after acute ascending aortic dissection in each of the 11 patients (none with healed dissection) who did not have operative therapy for the dissection. Two of the 16 patients with a malformed valve compared with no patient with a tricuspid aortic valve had aortic isthmic coarctation. Histologic sections of aorta from 10 patients disclosed severe degeneration of the elastic fibers of the media in 9 patients. Thus, a congenitally malformed aortic valve appears to be present at least 5 times more frequently in adults with than in those without aortic dissection, and in our patients the entrance tear was always in the ascending aorta, which usually had severe loss of elastic fibers in its media.
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets — amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
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