Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, the Coagulation factor V (FV) is a master regulator, coordinating critical steps of this process. Mutations to this factor result in spontaneous bleeding episodes and prolonged hemorrhage after trauma or surgery. Although the role of FV is well characterized, it is unclear how single-point mutations affect its structure. In this study, to understand the effect of mutations, we created a detailed network map of this protein, where each node is a residue, and two residues are connected if they are in close proximity in the three-dimensional structure. Overall, we analyzed 63 point-mutations from patients and identified common patterns underlying FV deficient phenotypes. We used structural and evolutionary patterns as input to machine learning algorithms to anticipate the effects of mutations and anticipated FV-deficiency with fair accuracy. Together, our results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance treatment and diagnosis of coagulation disorders.

Features of calcific leaflet distribution from CT valve images may allow prediction of hemodynamic disease severity in calcific degenerative aortic valve stenosis (DAS). The proposed study describes a signal processing scheme for selecting valve areas from maximum intensity projection valve images in a cohort of 52+43 patient images, diagnosed as having severe and moderate DAS. First the valve center and perimeter are approximated by a manually determined circle of radius r and center (x,y). The circle is then used to define eight masks, based on concentric circles of radius r 2 , r √ 2 2 , r √ 3 2 and r, each with center (x,y). The masks are used to define pixel regions within the valvular area, and statistical and textural descriptors are applied to each. Sensitivity/ specificity testing is performed with these descriptors, applied to the pixels within each mask, which show that disease severity is best predicted by using the smallest, most central mask and statistical features of skewness and kurtosis, providing area under the curve of 0.844 and 0.840 respectively. Our methodology was simple to implement and use, and provided good discriminatory power for disease severity. It also overcomes some difficulties in an earlier method, since our solution is scalable to variation in aortic valve size and tests a range of statistical and textural descriptors.