Empagliflozin prevents oxidative stress in human coronary artery endothelial cells via the NHE/PKC/NOX axis.

BACKGROUND: Empagliflozin (EMPA) ameliorates reactive oxygen species (ROS) generation in human endothelial cells (ECs) exposed to 10 % stretch, but the underlying mechanisms are still unclear. Pathological stretch is supposed to stimulate protein kinase C (PKC) by increasing intracellular calcium (Ca2+), therefore activating nicotinamide adenine dinucleotide phosphate oxidase (NOX) and promoting ROS production in human ECs. We hypothesized that EMPA inhibits stretch-induced NOX activation and ROS generation through preventing PKC activation. METHODS: Human coronary artery endothelial cells (HCAECs) were pre-incubated for 2 h before exposure to cyclic stretch (5 % or 10 %) with either vehicle, EMPA or the PKC inhibitor LY-333531 or PKC siRNA. PKC activity, NOX activity and ROS production were detected after 24 h. Furthermore, the Ca2+ chelator BAPTA-AM, NCX inhibitor ORM-10962 or NCX siRNA, sodium/potassium pump inhibitor ouabain and sodium hydrogen exchanger (NHE) inhibitor cariporide were applied to explore the involvement of the NHE/Na+/NCX/Ca2+ in the ROS inhibitory capacity of EMPA. RESULTS: Compared to 5 % stretch, 10 % significantly increased PKC activity, which was reduced by EMPA and PKC inhibitor LY-333531. EMPA and LY-333531 showed a similar inhibitory capacity on NOX activity and ROS generation induced by 10 % stretch, which was not augmented by combined treatment with both drugs. PKC-ß knockdown inhibits the NOX activation induced by Ca2+ and 10 % stretch. BAPTA, pharmacologic or genetic NCX inhibition and cariporide reduced Ca2+ in static HCAECs and prevented the activation of PKC and NOX in 10%-stretched cells. Ouabain increased ROS generation in cells exposed to 5 % stretch. CONCLUSION: EMPA reduced NOX activity via attenuation of the NHE/Na+/NCX/Ca2+/PKC axis, leading to less ROS generation in HCAECs exposed to 10 % stretch.

Testing exchangeability of multivariate distributions.

Although there have been a number of available tests of bivariate exchangeability, i.e. bivariate symmetry for bivariate distributions, the literature is void of tests whether a multivariate distribution with more than two dimensions is exchangeable or not. In this paper, multivariate permutation tests of exchangeability of multivariate distributions are proposed, which are based on the non-parametric combination methodology, i.e. on combining non-parametric bivariate exchangeability tests. Numerical experiments on real as well as simulated multivariate data with more than two dimensions are presented here. The multivariate permutation test turns out to be typically more powerful than a bivariate exchangeability test performed only over a single pair of variables, and also more suitable compared to tests exploiting the approaches of Benjamini-Yekutieli or Bonferroni.

Canagliflozin inhibits inflammasome activation in diabetic endothelial cells - Revealing a novel calcium-dependent anti-inflammatory effect of canagliflozin on human diabetic endothelial cells.

BACKGROUND: Canagliflozin (CANA) shows anti-inflammatory and anti-oxidative effects on endothelial cells (ECs). In diabetes mellitus (DM), excessive reactive oxygen species (ROS) generation, increased intracellular calcium (Ca2+) and enhanced extracellular signal regulated kinase (ERK) 1/2 phosphorylation are crucial precursors for inflammasome activation. We hypothesized that: (1) CANA prevents the TNF-α triggered ROS generation in ECs from diabetic donors and in turn suppresses the inflammasome activation; and (2) the anti-inflammatory effect of CANA is mediated via intracellular Ca2+ and ERK1/2. METHODS: Human coronary artery endothelial cells from donors with DM (D-HCAECs) were pre-incubated with either CANA or vehicle for 2 h before exposure to 50 ng/ml TNF-α for 2-48 h. NAC was applied to scavenge ROS, BAPTA-AM to chelate intracellular Ca2+, and PD 98059 to inhibit the activation of ERK1/2. Live cell imaging was performed at 6 h to measure ROS and intracellular Ca2+. At 48 h, ELISA and infra-red western blot were applied to detect IL-1ß, NLRP3, pro-caspase-1 and ASC. RESULTS: 10 µM CANA significantly reduced TNF-α related ROS generation, IL-1ß production and NLRP3 expression (P all <0.05), but NAC did not alter the inflammasome activation (P > 0.05). CANA and BAPTA both prevented intracellular Ca2+ increase in cells exposed to TNF-α (P both <0.05). Moreover, BAPTA and PD 98059 significantly reduced the TNF-α triggered IL-1ß production as well as NLRP3 and pro-caspase-1 expression (P all <0.05). CONCLUSION: CANA suppresses inflammasome activation by inhibition of (1) intracellular Ca2+ and (2) ERK1/2 phosphorylation, but not by ROS reduction.

Effective Automatic Method Selection for Nonlinear Regression Modeling.

Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regression modeling, particularly in recommending either the standard nonlinear least squares estimator or one of such available alternative estimators, which is highly robust with respect to the presence of outliers in the data. The authors hold the opinion that theoretical considerations will never be able to formulate such recommendations for the nonlinear regression context. Instead, metalearning is explored here as an original approach suitable for this task. In this paper, four different approaches for automatic method selection for nonlinear regression are proposed and computations over a training database of 643 real publicly available datasets are performed. Particularly, while the metalearning results may be harmed by the imbalanced number of groups, an effective approach yields much improved results, performing a novel combination of supervised feature selection by random forest and oversampling by synthetic minority oversampling technique (SMOTE). As a by-product, the computations bring arguments in favor of the very recent nonlinear least weighted squares estimator, which turns out to outperform other (and much more renowned) estimators in a quite large percentage of datasets.

Real-time quality control of nuchal translucency measurements using the exponentially weighted moving average chart.

OBJECTIVE: The establishment of ongoing audits for first-trimester nuchal translucency (NT) measurements is of paramount importance. The exponentially weighted moving average (EWMA) chart has been published as an efficient tool for NT quality control with the advantages of being suitable for real-time long-term monitoring. This study aimed to assess the efficacy of real-time NT quality control using EWMA charts. MATERIALS AND METHODS: This was an ongoing prospective study conducted from January 2011 to December 2017 at the Centre for Fetal Medicine Gennet in Prague. The quality of NT measurements was assessed using the NT retrospective distribution parameters and EWMA charts, and the results were presented to the sonographers during collective meetings. RESULTS: Overall, 28,928 NT measurements obtained from six sonographers were eligible for the study. Looking at individual EWMA charts, we observed four main outcomes. First, there was a clear improvement in the performance of sonographers with initially poor performances. Second, the performance of sonographers with an initially satisfactory quality was maintained. Third, there was an observed deterioration of the performance without the audits. Last, the sonographers appreciated an unequivocal and straightforward graphical presentation of EWMA curves. CONCLUSION: EWMA proved to be an efficient and suitable tool for real-time monitoring of NT quality and led to an overall improvement of the sonographers' performance.

Interpoint distance tests for high-dimensional comparison studies.

Modern data collection techniques allow to analyze a very large number of endpoints. In biomedical research, for example, expressions of thousands of genes are commonly measured only on a small number of subjects. In these situations, traditional methods for comparison studies are not applicable. Moreover, the assumption of normal distribution is often questionable for high-dimensional data, and some variables may be at the same time highly correlated with others. Hypothesis tests based on interpoint distances are very appealing for studies involving the comparison of means, because they do not assume data to come from normally distributed populations and comprise tests that are distribution free, unbiased, consistent, and computationally feasible, even if the number of endpoints is much larger than the number of subjects. New tests based on interpoint distances are proposed for multivariate studies involving simultaneous comparison of means and variability, or the whole distribution shapes. The tests are shown to perform well in terms of power, when the endpoints have complex dependence relations, such as in genomic and metabolomic studies. A practical application to a genetic cardiovascular case-control study is discussed.

Likelihood Ratio Testing under Measurement Errors.

We consider the likelihood ratio test of a simple null hypothesis (with density f 0 ) against a simple alternative hypothesis (with density g 0 ) in the situation that observations X i are mismeasured due to the presence of measurement errors. Thus instead of X i for i = 1 , … , n , we observe Z i = X i + δ V i with unobservable parameter δ and unobservable random variable V i . When we ignore the presence of measurement errors and perform the original test, the probability of type I error becomes different from the nominal value, but the test is still the most powerful among all tests on the modified level. Further, we derive the minimax test of some families of misspecified hypotheses and alternatives. The test exploits the concept of pseudo-capacities elaborated by Huber and Strassen (1973) and Buja (1986). A numerical experiment illustrates the principles and performance of the novel test.

[Data analysis: challenges and specifics in neuroscience and psychiatry]. / Analýza dat: výzvy a specifika v neurovedách a psychiatrii.

The amount of available data relevant for clinical decision support is rising not only rapidly but at the same time much faster than our ability to analyze and interpret them. Thus, the potential of the data to contribute to determining the diagnosis, therapy and prognosis of an individual patient is not appropriately exploited. The hopes to obtain benefit from the data for an individual patient must be accompanied by a reliable and diligent biostatistical analysis which faces serious challenges not always clear to non-statisticians. The aim of this paper is to discuss principles of statistical analysis of big data in research and routine applications in clinical medicine, focusing on particular aspects of psychiatry. The paper brings arguments in favor of the idea that the biostatistical analysis of data in a specialty field requires different approaches and different experience compared to other clinical fields. This is illustrated by a description of common complications of the analysis of psychiatric data. Challenges of the analysis of big data in both psychiatric research and routine practice are explained, which are far from a routine service activity exploiting standard methods of multivariate statistics and/or machine learning. Important research questions, which are important in the current psychiatric research, are presented and discussed from the biostatistical point of view.

A Robust Supervised Variable Selection for Noisy High-Dimensional Data.

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.

System for selecting relevant information for decision support.

We implemented a prototype of a decision support system called SIR which has a form of a web-based classification service for diagnostic decision support. The system has the ability to select the most relevant variables and to learn a classification rule, which is guaranteed to be suitable also for high-dimensional measurements. The classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. The implemented prototype was tested on a sample of patients in a cardiological study and performs an information extraction from a high-dimensional set containing both clinical and gene expression data.

Facial symmetry in robust anthropometrics.

Image analysis methods commonly used in forensic anthropology do not have desirable robustness properties, which can be ensured by robust statistical methods. In this paper, the face localization in images is carried out by detecting symmetric areas in the images. Symmetry is measured between two neighboring rectangular areas in the images using a new robust correlation coefficient, which down-weights regions in the face violating the symmetry. Raw images of faces without usual preliminary transformations are considered. The robust correlation coefficient based on the least weighted squares regression yields very promising results also in the localization of such faces, which are not entirely symmetric. Standard methods of statistical machine learning are applied for comparison. The robust correlation analysis can be applicable to other problems of forensic anthropology.

DNA conformations and their sequence preferences.

The geometry of the phosphodiester backbone was analyzed for 7739 dinucleotides from 447 selected crystal structures of naked and complexed DNA. Ten torsion angles of a near-dinucleotide unit have been studied by combining Fourier averaging and clustering. Besides the known variants of the A-, B- and Z-DNA forms, we have also identified combined A + B backbone-deformed conformers, e.g. with alpha/gamma switches, and a few conformers with a syn orientation of bases occurring e.g. in G-quadruplex structures. A plethora of A- and B-like conformers show a close relationship between the A- and B-form double helices. A comparison of the populations of the conformers occurring in naked and complexed DNA has revealed a significant broadening of the DNA conformational space in the complexes, but the conformers still remain within the limits defined by the A- and B- forms. Possible sequence preferences, important for sequence-dependent recognition, have been assessed for the main A and B conformers by means of statistical goodness-of-fit tests. The structural properties of the backbone in quadruplexes, junctions and histone-core particles are discussed in further detail.

Gorillas of Bwindi-Impenetrable Forest and the Virunga Volcanoes: Taxonomic implications of morphological and ecological differences.

Based on their geographic proximity to the Virunga Volcanoes (≈ 25 km), the Bwindi-Impenetrable Forest gorillas have been referred to the subspecies Gorilla gorilla beringei. Differences in anatomy, habitat, ecology, and behavior, however, suggest Bwindi gorillas are distinct from those in the Virungas. Relative to Virunga gorillas, Bwindi gorillas live at lower elevations, in warmer temperatures, are much more arboreal, have longer day ranges and larger home ranges, and eat much more fruit and pith, and less bamboo and leaves. Morphological differences reflect the differences in ecology, habitat, and behavior. Bwindi gorillas measured have smaller bodies, relatively longer limbs, hands, and feet, shorter trunks, thumbs, big toes, and tooth row lengths, and narrower trunks and orbital breadths than Virunga gorillas. These differences indicate Bwindi gorillas do not belong to G.g. beringei and should not be referred to as "mountain gorillas." How unique the distinguishing features of Bwindi gorillas are, and whether or not they should be assigned to a new taxon, depends on the expression of these features in eastern lowland gorillas (G.g. graueri). © 1996 Wiley-Liss, Inc.