Mitochondrial DNA haplogroup T is associated with coronary artery disease and diabetic retinopathy: a case control study

<p>Abstract</p> <p>Background</p> <p>There is strong and consistent evidence that oxidative stress is crucially involved in the development of atherosclerotic vascular disease. Overproduction of reactive oxygen species (ROS) in mitochondria is an unifying mechanism that underlies micro- and macrovascular atherosclerotic disease. Given the central role of mitochondria in energy and ROS production, mitochondrial DNA (mtDNA) is an obvious candidate for genetic susceptibility studies on atherosclerotic processes. We therefore examined the association between mtDNA haplogroups and coronary artery disease (CAD) as well as diabetic retinopathy.</p> <p>Methods</p> <p>This study of Middle European Caucasians included patients with angiographically documented CAD (n = 487), subjects with type 2 diabetes mellitus with (n = 149) or without (n = 78) diabetic retinopathy and control subjects without clinical manifestations of atherosclerotic disease (n = 1527). MtDNA haplotyping was performed using multiplex PCR and subsequent multiplex primer extension analysis for determination of the major European haplogroups. Haplogroup frequencies of patients were compared to those of control subjects without clinical manifestations of atherosclerotic disease.</p> <p>Results</p> <p>Haplogroup T was significantly more prevalent among patients with CAD than among control subjects (14.8% vs 8.3%; p = 0.002). In patients with type 2 diabetes, the presence of diabetic retinopathy was also significantly associated with a higher prevalence of haplogroup T (12.1% vs 5.1%; p = 0.046).</p> <p>Conclusion</p> <p>Our data indicate that the mtDNA haplogroup T is associated with CAD and diabetic retinopathy in Middle European Caucasian populations.</p

Convergence in international business ethics? A comparative study of ethical philosophies, thinking style, and ethical decision-making between US and Korean managers

This study investigates the relationship among ethical philosophy, thinking style, and managerial ethical decision-making. Based on the premise that business ethics is a function of culture and time, we attempt to explore two important questions as to whether the national differences in managerial ethical philosophies remain over time and whether the relationship between thinking style and ethical decision-making is consistent across different national contexts. We conducted a survey on Korean managers’ ethical decision-making and thinking style and made a cross-cultural, cross-temporal comparison with the results presented by previous studies that surveyed Korean and US managers with the same questionnaire at different points in time. Our analysis revealed that Korean managers have become more reliant on rule utilitarianism for ethical decision-making over the last two decades, which is dominantly used by US managers, corroborating our convergence hypothesis built on social contracts theory. However, as opposed to previous research, we found that managers with a balanced linear and nonlinear thinking style do not necessarily make more ethical decisions compared to those with a predominantly linear or nonlinear thinking style. This study contributes to international business ethics literature by presenting a theoretical framework that may explain the convergence of ethical philosophies employed by managers in different national contexts over time, and that the relationship between thinking style and managerial ethical decision-making may not be universal, but contingent on contextual factors

Genetic Variability of the mTOR Pathway and Prostate Cancer Risk in the European Prospective Investigation on Cancer (EPIC)

The mTOR (mammalian target of rapamycin) signal transduction pathway integrates various signals, regulating ribosome biogenesis and protein synthesis as a function of available energy and amino acids, and assuring an appropriate coupling of cellular proliferation with increases in cell size. In addition, recent evidence has pointed to an interplay between the mTOR and p53 pathways. We investigated the genetic variability of 67 key genes in the mTOR pathway and in genes of the p53 pathway which interact with mTOR. We tested the association of 1,084 tagging SNPs with prostate cancer risk in a study of 815 prostate cancer cases and 1,266 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). We chose the SNPs (n = 11) with the strongest association with risk (p<0.01) and sought to replicate their association in an additional series of 838 prostate cancer cases and 943 controls from EPIC. In the joint analysis of first and second phase two SNPs of the PRKCI gene showed an association with risk of prostate cancer (ORallele = 0.85, 95% CI 0.78–0.94, p = 1.3×10−3 for rs546950 and ORallele = 0.84, 95% CI 0.76–0.93, p = 5.6×10−4 for rs4955720). We confirmed this in a meta-analysis using as replication set the data from the second phase of our study jointly with the first phase of the Cancer Genetic Markers of Susceptibility (CGEMS) project. In conclusion, we found an association with prostate cancer risk for two SNPs belonging to PRKCI, a gene which is frequently overexpressed in various neoplasms, including prostate cancer

The Transcriptome of Trichuris suis – First Molecular Insights into a Parasite with Curative Properties for Key Immune Diseases of Humans

Iatrogenic infection of humans with Trichuris suis (a parasitic nematode of swine) is being evaluated or promoted as a biological, curative treatment of immune diseases, such as inflammatory bowel disease (IBD) and ulcerative colitis, in humans. Although it is understood that short-term T. suis infection in people with such diseases usually induces a modified Th2-immune response, nothing is known about the molecules in the parasite that induce this response.As a first step toward filling the gaps in our knowledge of the molecular biology of T. suis, we characterised the transcriptome of the adult stage of this nematode employing next-generation sequencing and bioinformatic techniques. A total of ∼65,000,000 reads were generated and assembled into ∼20,000 contiguous sequences ( = contigs); ∼17,000 peptides were predicted and classified based on homology searches, protein motifs and gene ontology and biological pathway mapping.These analyses provided interesting insights into a number of molecular groups, particularly predicted excreted/secreted molecules (n = 1,288), likely to be involved in the parasite-host interactions, and also various molecules (n = 120) linked to chemokine, T-cell receptor and TGF-β signalling as well as leukocyte transendothelial migration and natural killer cell-mediated cytotoxicity, which are likely to be immuno-regulatory or -modulatory in the infected host. This information provides a conceptual framework within which to test the immunobiological basis for the curative effect of T. suis infection in humans against some immune diseases. Importantly, the T. suis transcriptome characterised herein provides a curated resource for detailed studies of the immuno-molecular biology of this parasite, and will underpin future genomic and proteomic explorations

Deep stylometry and lexical and syntactic features based author attribution on PLoS digital repository

In this paper, we address the problem of author attribution through unsupervised clustering using lexical and syntactic features and novel deep learning based Stylometric model. For this purpose, we download all available 158918 publications accessible till 1 July 2015 from PLOS.org - an open access digital repository of full text publications. After pre-processing, out of these, we use 803 single authored publications written by 203 unique authors. For unsupervised modeling, stylometric markers such as lexical and syntactic features are used as a distance matrix by employing k-Means clustering algorithm. For supervised modeling, we present a novel long short-term memory (LSTM) based deep learning model that predicts the testing accuracy of a given publication written by an author. Finally, our unsupervised model shows that 88.17% authors are classified into correct cluster (all papers written by the same author) with at most 0.2 coefficient of Entropy error. While our deep learning based model consistently shows above 95% accuracy across all the given testing samples of publications written by an author with an average loss of 0.21