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Background: Patients with chronic kidney disease stage 5 (CKD5) are predisposed to vascular calcification (VC), but the combined effect of factors associated with VC was sparsely investigated. We applied the relaxed linear separability (RLS) feature selection model to identify features that concomitantly associate with VC in CKD5 patients.Methods: Epigastric arteries collected during surgery from living donor kidney transplant recipients were examined to score the histological extent of medial VC. Sixty-two phenotypic features in 152 patients were entered into RLS model to differentiate between no-minimal VC (n = 93; score 0-1) and moderate-extensive VC (n = 59; score 2-3). The subset of features associated with VC was selected on the basis of cross-validation procedure. The strength of association of the selected features with VC was expressed by the absolute value of 'RLS factor'.Results: Among 62 features, a subset of 17 features provided optimal prediction of VC with 89% of patients correctly classified into their groups. The 17 features included traditional risk factors (diabetes, age, cholesterol, BMI and male sex) and markers of bone metabolism, endothelial function, metabolites, serum antibodies and mitochondrial-derived peptide. Positive RLS factors range from 1.26 to 4.05 indicating features associated with increased risk of VC, and negative RLS factors range from -0.95 to -1.83 indicating features associated with reduced risk of VC.Conclusion: The RLS model identified 17 features including novel biomarkers and traditional risk factors that together concomitantly associated with medial VC. These results may inform further investigations of factors promoting VC in CKD5 patients. [ABSTRACT FROM AUTHOR]

Arteriogenesis, the growth of natural bypass blood vessels, can compensate for the loss of arteries caused by vascular occlusive diseases. Accordingly, it is a major goal to identify the drugs promoting this innate immune system-driven process in patients aiming to save their tissues and life. Here, we studied the impact of the Cobra venom factor (CVF), which is a C3-like complement-activating protein that induces depletion of the complement in the circulation in a murine hind limb model of arteriogenesis. Arteriogenesis was induced in C57BL/6J mice by femoral artery ligation (FAL). The administration of a single dose of CVF (12.5 µg) 24 h prior to FAL significantly enhanced the perfusion recovery 7 days after FAL, as shown by Laser Doppler imaging. Immunofluorescence analyses demonstrated an elevated number of proliferating (BrdU + ) vascular cells, along with an increased luminal diameter of the grown collateral vessels. Flow cytometric analyses of the blood samples isolated 3 h after FAL revealed an elevated number of neutrophils and platelet-neutrophil aggregates. Giemsa stains displayed augmented mast cell recruitment and activation in the perivascular space of the growing collaterals 8 h after FAL. Seven days after FAL, we found more CD68 + /MRC-1 + M2-like polarized pro-arteriogenic macrophages around growing collaterals. These data indicate that a single dose of CVF boosts arteriogenesis by catalyzing the innate immune reactions, relevant for collateral vessel growth.

Rare disease datasets are typically structured such that a small number of patients (cases) are represented by multidimensional feature vectors. In this report, we considered a rare disease, mucopolysaccharidosis (MPS). This disease is divided into 11 types and subtypes, depending on the genetic defect, type of deficient enzyme, and nature of accumulated glycosaminoglycan(s). Among them, 7 types are known as possibly neuronopathic and 4 are non-neuronopathic, and in the case of the former group, prediction of the course of the disease is crucial for patient's treatment and the management. Here, we have used transcriptomic data available for one patient from each MPS type/subtype. The approach to gene grouping considered by us was based on the minimization of the perceptron criterion in the form of convex and piecewise linear function (CPL). This approach allows designing complexes of linear classifiers on the basis of small samples of multivariate vectors. As a result, distinguishing neuronopathic and non-neuronopathic forms of MPS was possible on the basis of bioinformatic analysis of gene expression patterns where each MPS type was represented by only one patient. This approach can be potentially used also for assessing other features of patients suffering from rare diseases, for which large body of data (like transcriptomic data) is available from only one or a few representatives., (© 2022. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)

Background: In complex diseases such as chronic kidney disease (CKD), the risk of clinical complications is determined by interactions between phenotypic and genotypic factors. However, clinical epidemiological studies rarely attempt to analyse the combined effect of large numbers of phenotype and genotype features. We have recently shown that the relaxed linear separability (RLS) model of feature selection can address such complex issues. Here, it is applied to identify risk factors for inflammation in CKD., Methods: The RLS model was applied in 225 CKD stage 5 patients sampled in conjunction with dialysis initiation. Fifty-seven anthropometric or biochemical measurements and 79 genetic polymorphisms were entered into the model. The model was asked to identify phenotypes and genotypes that, when combined, could separate inflamed from non-inflamed patients. Inflammation was defined as a high-sensitivity C-reactive protein concentration above the median (5 mg/L)., Results: Among the 60 genotypic and phenotypic features predicting inflammation, 31 were genetic. Among the 10 strongest predictors of inflammation, 8 were single nucleotide polymorphisms located in the NAMPT, CIITA, BMP2 and PIK3CB genes, whereas fibrinogen and bone mineral density were the only phenotypic biomarkers., Conclusion: These results indicate a larger involvement of hereditary factors in inflammation than might have been expected and suggest that inclusion of genotype features in risk assessment studies is critical. The RLS model demonstrates that inflammation in CKD is determined by an extensive panel of factors and may prove to be a suitable tool that could enable a much-needed multifactorial approach as opposed to the commonly utilized single-factor analysis., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Identification of risk factors in patients with a particular disease can be analyzed in clinical data sets by using feature selection procedures of pattern recognition and data mining methods. The applicability of the relaxed linear separability (RLS) method of feature subset selection was checked for high-dimensional and mixed type (genetic and phenotypic) clinical data of patients with end-stage renal disease. The RLS method allowed for substantial reduction of the dimensionality through omitting redundant features while maintaining the linear separability of data sets of patients with high and low levels of an inflammatory biomarker. The synergy between genetic and phenotypic features in differentiation between these two subgroups was demonstrated.

Variety of prognostic models can be designed on the basis of learning sets by using the principle of linear separability. The degree of linear separability of two learning sets can be evaluated on the basis of the minimal value of the perceptron criterion function, which belongs to a larger family of the convex and piecewise linear (CPL) criterion functions. Parameters constituting the minimal value of a given CPL criterion function can define particular prognostic model. Prognostic models have been designed this way, for example, on the basis of genetic data sets.

The "Hepar" system comprises a clinical database and the shell of procedures that aim at data analysis and the support of diagnosis. The database consists of hepatological patient cases. Each case is described by about 200 medical findings and histopathologically verified diagnosis. The diagnosis supporting rules of "Hepar" are based on visualizing data transformations and on the similarity based techniques. The applied linear visualizing transformations of data sets on the plane aim at separating of the groups of patients associated with different diseases. The resulting diagnostic maps by the visual inspection allow to find such cases in the database that are similar to the previously diagnosed patients. This paper examines combining of data transformations with the nearest neighbors techniques in the support of diagnosis. We report the results on the experimental comparisons of different decision rules including the feature selection procedure.