The ScaI gene polymorphism of the atrial natriuretic factor and essential arterial hypertension in childhood

In order to investigate the contribution of the atrial natriuretic factor (ANF) gene in pathogenesis of essential arterial hypertension (EAH), we analyzed the ScaI gene polymorphism of the ANF gene in a group of children with EAH. Fifty-eight children, aged 8-19 years, with the diagnosis of EAH were included in the association study and were compared to 57 subjects with normal blood pressure (the control group). Arterial hypertension was defined as systolic/diastolic blood pressure higher than the 95th age-gender-height percentile of the adopted reference values. We failed to demonstrate an association between the ScaI ANF gene polymorphism and EAH in childhood (OR = 2; 95% CI 0.9-4.2; p = 0.07), however, we provided evidence of an interaction between the ScaI ANF gene polymorphism and obesity defined as BMI over the 85th percentile (OR = 13.1; 95% CI 1.6-106; p < 0.001).     

Qualitative study of Mollusca communities in the Serbian Danube stretch (river km 1260–863.4)

First detailed limnological study was performed from April 2003 to June 2008 in a 396.6 km long section of the Serbian Danube (divided in three parts; from 1260 r-km to 863.4 r-km) to examine community composition and spatial distribution of Mollusca with special attention to the expansion of Ponto-Caspian species, propagation of invasive and introduced species and occurrence of endemic species. Mollusca samples were collected at 15 sites in April, June, September and November. We investigated the spatial distributions and calculated the faunistic similarity of localities with respect to the community composition using the Sørensen Quotient of Similarity. Site variation in analyzed qualitative traits was examined using correspondence analysis. Additionally, the obtained Complete Linkage City-block (Manhattan) distances among sites/parts were subjected to UPGMA (unweighted pair-group method using arithmetic averages) cluster analysis. The class Gastropoda was represented by 18 species from two subclasses (Prosobranchia — six families and Pulmonata — three families).The class Bivalvia was represented by 15 species from four families of order Eulamellibranchiata. Mollusca were represented by 33 species belonging to 17 genera and 13 families. Out of five recorded Ponto-Caspian species in the studied Danube stretch, Lithoglyphus naticoides (Pfeiffer, 1828) and Dreissena polymorpha (Pallas, 1771) are assumed to be invasive species with the highest occurrence frequency (F = 1, each). Four new species in invertebrate fauna for the Danube, denoted as the introduced species — Neozoa, were identified: Theodoxus fluviatilis (L., 1758), Corbicula fluminalis (Müller, 1774), C. fluminea (Müller, 1774), and Sinanodonta woodiana (Lea, 1834). The only endemic species of Gastropoda found in the Danube was Viviparus acerosus Bourguignat, 1862.     

A data-driven acute inflammation therapy

Acute inflammation is a severe medical condition defined as an inflammatory response of the body to an infection. Its rapid progression requires quick and accurate decisions from clinicians. Inadequate and delayed decisions makes acute inflammation the 10th leading cause of death overall in United States with the estimated cost of treatment about $17 billion annually. However, despite the need, there are limited number of methods that could assist clinicians to determine optimal therapies for acute inflammation. We developed a data-driven method for suggesting optimal therapy by using machine learning model that is learned on historical patients' behaviors. To reduce both the risk of failure and the expense for clinical trials, our method is evaluated on a virtual patients generated by a mathematical model that emulates inflammatory response. In conducted experiments, acute inflammation was handled with two complimentary pro- and anti-inflammatory medications which adequate timing and doses are crucial for the successful outcome. Our experiments show that the dosage regimen assigned with our data-driven method significantly improves the percentage of healthy patients when compared to results by other methods used in clinical practice and found in literature. Our method saved 88% of patients that would otherwise die within a week, while the best method found in literature saved only 73% of patients. At the same time, our method used lower doses of medications than alternatives. In addition, our method achieved better results than alternatives when only incomplete or noisy measurements were available over time as well as it was less affected by therapy delay. The presented results provide strong evidence that models from the artificial intelligence community have a potential for development of personalized treatment strategies for acute inflammation.     

Reinventing Biostatistics Education for Basic Scientists

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.     

Early classification of multivariate temporal observations by extraction of interpretable shapelets

ABSTRACT: BACKGROUND: Early classification of time series is beneficial for biomedical informatics problems suchincluding, but not limited to, disease change detection. Early classification can be oftremendous help by identifying the onset of a disease before it has time to fully take hold. Inaddition, extracting patterns from the original time series helps domain experts to gaininsights into the classification results. This problem has been studied recently using timeseries segments called shapelets. In this paper, we present a method, which we callMultivariate Shapelets Detection (MSD), that allows for early and patient-specificclassification of multivariate time series. The method extracts time series patterns, calledmultivariate shapelets, from all dimensions of the time series that distinctly manifest thetarget class locally. The time series were classified by searching for the earliest closestpatterns. RESULTS: The proposed early classification method for multivariate time series has been evaluated oneight gene expression datasets from viral infection and drug response studies in humans. Inour experiments, the MSD method outperformed the baseline methods, achieving highlyaccurate classification by using as little as 40%-64% of the time series. The obtained resultsprovide evidence that using conventional classification methods on short time series is notas accurate as using the proposed methods specialized for early classification. CONCLUSION: For the early classification task, we proposed a method called Multivariate ShapeletsDetection (MSD), which extracts patterns from all dimensions of the time series. Weshowed that the MSD method can classify the time series early by using as little as40%-64% of the time series' length.     

Comparative Analysis of SOX3 Protein Orthologs: Expansion of Homopolymeric Amino Acid Tracts During Vertebrate Evolution

To understand more fully the structure and evolution of the SOX3 protein, we comparatively analyzed its orthologs in vertebrates. Since complex disorders are associated with human SOX3 polyalanine expansions, our investigation focused on both compositional and evolutionary analysis of various homopolymeric amino acid tracts observed in SOX3 orthologs. Our analysis revealed that the observed homopolymeric alanine, glycine, and proline tracts are mammal-specific, except for one polyglycine tract present in birds. Since it is likely that the SOX3 protein acquired additional roles in brain development in Eutheria, we might speculate that development of novel brain functions during the course of evolution was affected, at least in part, by such structural–functional changes in the SOX3 protein.