Simple and Effective Visual Models for Gene Expression Cancer Diagnostics

In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph. The principal innovation proposed in the paper is a method called VizRank, which is able to score and identify the best among possibly millions of candidate projections for visualizations. Compared to recently much applied techniques in the field of cancer genomics that include neural networks, support vector machines and various ensemble-based approaches, VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts. Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of (two to seven) genes and excellent class separation. In addition to providing grounds for gene expression cancer diagnosis, VizRank and its visualizations also identify small sets of relevant genes, uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets

FragViz: visualization of fragmented networks

BACKGROUND Researchers in systems biology use network visualization to summarize the results of their analysis. Such networks often include unconnected components, which popular network alignment algorithms place arbitrarily with respect to the rest of the network. This can lead to misinterpretations due to the proximity of otherwise unrelated elements. RESULTS We propose a new network layout optimization technique called FragViz which can incorporate additional information on relations between unconnected network components. It uses a two-step approach by first arranging the nodes within each of the components and then placing the components so that their proximity in the network corresponds to their relatedness. In the experimental study with the leukemia gene networks we demonstrate that FragViz can obtain network layouts which are more interpretable and hold additional information that could not be exposed using classical network layout optimization algorithms. CONCLUSIONS Network visualization relies on computational techniques for proper placement of objects under consideration. These algorithms need to be fast so that they can be incorporated in responsive interfaces required by the explorative data analysis environments. Our layout optimization technique FragViz meets these requirements and specifically addresses the visualization of fragmented networks, for which standard algorithms do not consider similarities between unconnected components. The experiments confirmed the claims on speed and accuracy of the proposed solution

Congenital heart disease detection in Slovenia: Improvement potential of neonatal pulse oximetry screening

Introduction: Patients with major or critical congenital heart disease (CHD) require surgical treatment or interventional cardiac catheterization during the first year or 28 days of life, respectively. Currently, the detection of CHD in Slovenia relies on the prenatal ultrasound screening and physical examination of the newborn.Aims: 1) To determine the incidence of major/critical CHD in Slovenia; 2) to determine the proportion of infants with late detection of major/critical CHD based on the existing clinical practice; and 3) to estimate the improvement in CHD detection with a nation-wide neonatal pulse oximetry screening programme.Methods: We reviewed the documentation of all patients with major/critical CHD born in Slovenia in years 2007–2012. We determined whether the heart condition was detected: 1) on time – prenatally or prior to discharge from maternity ward; or 2) late – after discharge or at autopsy.Results: Among 128,839 live-born babies, 293 were diagnosed with a major CHD (2.27/1000 live births, 95 % confidence interval (CI): 2.0–2.5/1000) and of those 150 with a critical CHD (1.16/1000 live births, 95 % CI: 1.0–1.4/1000). Late detection occurred in 17.7 % of patients with major and 10.9 % patients with critical CHD. Out of 15 late-detected patients with critical CHD, 14 had an obstructive left heart lesion. In 2 patients CHD was diagnosed after death.Conclusions: Detection of CHD in Slovenia is satisfactory. However, in the observed period, 10.9 % of newborns with a critical CHD were discharged undiagnosed. A nation-wide pulse oximetry screening programme could improve pre-discharge CHD detection. </p

Enterovirus D68 circulation between 2014 and 2022 in Slovenian children

IntroductionEnterovirus D68 (EV-D68) belongs to the Picornaviridae family, genus Enterovirus. It is mostly known as a respiratory virus causing upper and lower respiratory tract infections, but it is also rarely associated with a variety of central nervous system complications, with acute flaccid myelitis being reported most frequently. This study assesses the incidence, seasonality, clinical presentation, and molecular epidemiology of the EV-D68 strain in EV-positive children hospitalized between 2014 and 2022 at the largest pediatric medical center in Slovenia.MethodsEV-D68 was detected using specific qRT-PCR, whereas partial VP1 sequences were obtained with Sanger sequencing, and further analyzed using the software CLC Main Workbench version 7 and MEGA version X.ResultsEV-D68 was detected in 154 out of 1,145 (13.4%) EV-positive children. In the two epidemic years, 2014 and 2016, EV-D68 was most frequently detected in the summer and early autumn, peaking in September. The median age of EV-D68–infected children was 3 years (IQR 1–3 years), with a female: male ratio of 1:1.17. Rhinorrhea was present in 74.0% of children, respiratory distress in 82.5%, and hypoxemia requiring supplemental oxygen in 44.1%. Out of 154 patients, 80.0% were hospitalized, with a median stay of 2 days (IQR 1–3 days). Lower respiratory tract infection was observed in 89.0% of EV-D68–positive patients, with bronchitis and bronchiolitis being most frequently diagnosed. No central nervous system manifestations of EV-D68 infection were observed in the study cohort. Phylogenetic analysis of partial VP1 sequences of EV-D68 revealed close similarity to the EV-D68 variants that were circulating in other European countries in these years.DiscussionSlovenia faced two EV-D68 epidemics in 2014 and 2016; however, after 2016 only nine more cases were detected until the end of the study period. Based on the results of this study, EV-D68 was a frequent cause of lower respiratory tract infection among EV-positive patients. However, none of the patients we studied needed ICU treatment, and none developed acute flaccid paralysis. Our results indicate that EV-D68 is not present constantly, so additional monitoring studies should be conducted in the future to better understand the implications of this EV type in human disease

Urine and Fecal 1^1H-NMR metabolomes differ significantly between pre-term and full-term born physically fit healthy adult males

Preterm birth (before 37 weeks gestation) accounts for ~10% of births worldwide and remains one of the leading causes of death in children under 5 years of age. Preterm born adults have been consistently shown to be at an increased risk for chronic disorders including cardiovascular, endocrine/metabolic, respiratory, renal, neurologic, and psychiatric disorders that result in increased death risk. Oxidative stress was shown to be an important risk factor for hypertension, metabolic syndrome and lung disease (reduced pulmonary function, long-term obstructive pulmonary disease, respiratory infections, and sleep disturbances). The aim of this study was to explore the differences between preterm and full-term male participants’ levels of urine and fecal proton nuclear magnetic resonance ($^1$H-NMR) metabolomes, during rest and exercise in normoxia and hypoxia and to assess general differences in human gut-microbiomes through metagenomics at the level of taxonomy, diversity, functional genes, enzymatic reactions, metabolic pathways and predicted gut metabolites. Significant differences existed between the two groups based on the analysis of $^1$H-NMR urine and fecal metabolomes and their respective metabolic pathways, enabling the elucidation of a complex set of microbiome related metabolic biomarkers, supporting the idea of distinct host-microbiome interactions between the two groups and enabling the efficient classification of sampleshowever, this could not be directed to specific taxonomic characteristics