Killer-cell immunoglobulin-like receptor diversity in an admixed South American population

Natural Killer (NK) cells are innate immune cells that mediate antiviral and antitumor responses. NK cell activation and induction of effector functions are tightly regulated by the integration of activating and inhibitory receptors such as killer immunoglobulin-like receptors (KIR)

Killer-Cell Immunoglobulin-like Receptor Diversity in an Admixed South American Population

Natural Killer (NK) cells are innate immune cells that mediate antiviral and antitumor responses. NK cell activation and induction of effector functions are tightly regulated by the integration of activating and inhibitory receptors such as killer immunoglobulin-like receptors (KIR). KIR genes are characterized by a high degree of diversity due to presence or absence, gene copy number and allelic polymorphism. The aim of this study was to establish the distribution of KIR genes and genotypes, to infer the most common haplotypes in an admixed Colombian population and to compare these KIR gene frequencies with some Central and South American populations and worldwide. A total of 161 individuals from Medellin, Colombia were included in the study. Genomic DNA was used for KIR and HLA genotyping. We analyzed only KIR gene-content (presence or absence) based on PCR-SSO. The KIR genotype, most common haplotypes and combinations of KIR and HLA ligands frequencies were estimated according to the presence or absence of KIR and HLA genes. Dendrograms, principal component (PC) analysis and Heatmap analysis based on genetic distance were constructed to compare KIR gene frequencies among Central and South American, worldwide and Amerindian populations. The 16 KIR genes analyzed were distributed in 37 different genotypes and the 7 most frequent KIR inferred haplotypes. Importantly, we found three new genotypes not previously reported in any other ethnic group. Our genetic distance, PC and Heatmap analysis revealed marked differences in the distribution of KIR gene frequencies in the Medellin population compared to worldwide populations. These differences occurred mainly in the activating KIR isoforms, which are more frequent in our population, particularly KIR3DS1. Finally, we observed unique structural patterns of genotypes, which evidences the potential diversity and variability of this gene family in our population, and the need for exhaustive genetic studies to expand our understanding of the KIR gene complex in Colombian populations

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth.Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+8) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease