Dioxin Induces Genomic Instability in Mouse Embryonic Fibroblasts

Ionizing radiation and certain other exposures have been shown to induce genomic instability (GI), i.e., delayed genetic damage observed many cell generations later in the progeny of the exposed cells. The aim of this study was to investigate induction of GI by a nongenotoxic carcinogen, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Mouse embryonic fibroblasts (C3H10T1/2) were exposed to 1, 10 or 100 nM TCDD for 2 days. Micronuclei (MN) and expression of selected cancer-related genes were assayed both immediately and at a delayed point in time (8 days). For comparison, similar experiments were done with cadmium, a known genotoxic agent. TCDD treatment induced an elevated frequency of MN at 8 days, but not directly after the exposure. TCDD-induced alterations in gene expression were also mostly delayed, with more changes observed at 8 days than at 2 days. Exposure to cadmium produced an opposite pattern of responses, with pronounced effects immediately after exposure but no increase in MN and few gene expression changes at 8 days. Although all responses to TCDD alone were delayed, menadione-induced DNA damage (measured by the Comet assay), was found to be increased directly after a 2-day TCDD exposure, indicating that the stability of the genome was compromised already at this time point. The results suggested a flat dose-response relationship consistent with dose-response data reported for radiation-induced GI. These findings indicate that TCDD, although not directly genotoxic, induces GI, which is associated with impaired DNA damage response

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Functional Characterization of a First Avian Cytochrome P450 of the CYP2D Subfamily (CYP2D49)

The CYP2D family members are instrumental in the metabolism of 20–25% of commonly prescribed drugs. Although many CYP2D isoforms have been well characterized in other animal models, research concerning the chicken CYP2Ds is limited. In this study, a cDNA encoding a novel CYP2D enzyme (CYP2D49) was cloned from the chicken liver for the first time. The CYP2D49 cDNA contained an open reading frame of 502 amino acids that shared 52%–57% identities with other CYP2Ds. The gene structure and neighboring genes of CYP2D49 are conserved and similar to those of human CYP2D6. Additionally, similar to human CYP2D6, CYP2D49 is un-inducible in the liver and expressed predominantly in the liver, kidney and small intestine, with detectable levels in several other tissues. Metabolic assays of the CYP2D49 protein heterologously expressed in E. coli and Hela cells indicated that CYP2D49 metabolized the human CYP2D6 substrate, bufuralol, but not debrisoquine. Moreover, quinidine, a potent inhibitor of human CYP2D6, only inhibited the bufuralol 1′-hydroxylation activity of CYP2D49 to a negligible degree. All these results indicated that CYP2D49 had functional characteristics similar to those of human CYP2D6 but measurably differed in the debrisoquine 4′-hydroxylation and quinidine inhibitory profile. Further structure-function investigations that employed site-directed mutagenesis and circular dichroism spectroscopy identified the importance of Val-126, Glu-222, Asp-306, Phe-486 and Phe-488 in keeping the enzymatic activity of CYP2D49 toward bufuralol as well as the importance of Asp-306, Phe-486 and Phe-488 in maintaining the conformation of CYP2D49 protein. The current study is only the first step in characterizing the metabolic mechanism of CYP2D49; further studies are still required

Increased Frequencies of Th22 Cells as well as Th17 Cells in the Peripheral Blood of Patients with Ankylosing Spondylitis and Rheumatoid Arthritis

<div><h3>Background</h3><p>T-helper (Th) 22 is involved in the pathogenesis of inflammatory diseases. The roles of Th22 cells in the pathophysiological of ankylosing spondylitis (AS) and rheumatoid arthritis (RA) remain unsettled. So we examined the frequencies of Th22 cells, Th17 cells and Th1 cells in peripheral blood (PB) from patients with AS and patients with RA compared with both healthy controls as well as patients with osteoarthritis.</p> <h3>Design and Methods</h3><p>We studied 32 AS patients, 20 RA patients, 10 OA patients and 20 healthy controls. The expression of IL-22, IL-17 and IFN-γ were examined in AS, RA, OA patients and healthy controls by flow cytometry. Plasma IL-22 and IL-17 levels were examined by enzyme-linked immunosorbent assay.</p> <h3>Results</h3><p>Th22 cells, Th17 cells and interleukin-22 were significantly elevated in AS and RA patients compared with OA patients and healthy controls. Moreover, Th22 cells showed positive correlation with Th17 cells as well as interleukin-22 in AS and RA patients. However, positive correlation between IL-22 and Th17 cells was only found in AS patients not in RA patients. In addition, the percentages of both Th22 cells and Th17 cells correlated positively with disease activity only in RA patients not in AS patients.</p> <h3>Conclusions</h3><p>The frequencies of both Th22 cells and Th17 cells were elevated in PB from patients with AS and patients with RA. These findings suggest that Th22 cells and Th17 cells may be implicated in the pathogenesis of AS and RA, and Th22 cells and Th17 cells may be reasonable cellular targets for therapeutic intervention.</p> </div

Using a mHealth tutorial application to change knowledge and attitude of frontline health workers to Ebola virus disease in Nigeria: a before-and-after study

Background: The Ebola epidemic exposed the weak state of health systems in West Africa and their devastating effect on frontline health workers and the health of populations. Fortunately, recent reviews of mobile technology demonstrate that mHealth innovations can help alleviate some health system constraints such as balancing multiple priorities, lack of appropriate tools to provide services and collect data, and limited access to training in health fields such as mother and child health, HIV/AIDS and sexual and reproductive health. However, there is little empirical evidence of mHealth improving health system functions during the Ebola epidemic in West Africa. Methods: We conducted quantitative cross-sectional surveys in 14 health facilities in Ondo State, Nigeria, to assess the effect of using a tablet computer tutorial application for changing the knowledge and attitude of health workers regarding Ebola virus disease. Results: Of 203 participants who completed pre- and post-intervention surveys, 185 people (or 91%) were female, 94 participants (or 46.3%) were community health officers, 26 people (13 %) were nurses/midwives, 8 people (or 4%) were laboratory scientists and 75 people (37%) belonged to a group called others. Regarding knowledge of Ebola: 178 participants (or 87.7%) had foreknowledge of Ebola before the study. Further analysis showed an 11% improvement in average knowledge levels between pre- and post-intervention scores with statistically significant differences (P < 0.05) recorded for questions concerning the transmission of the Ebola virus among humans, common symptoms of Ebola fever and whether Ebola fever was preventable. Additionally, there was reinforcement of positive attitudes of avoiding the following: contact with Ebola patients, eating bush meat and risky burial practices as indicated by increases between pre- and post-intervention scores from 83 to 92%, 57 to 64% and 67 to 79%, respectively. Moreover, more participants (from 95 to 97%) reported a willingness to practice frequent hand washing and disinfecting surfaces and equipment following the intervention, and more health workers were willing (from 94 to 97%) to use personal protective equipment to prevent the transmission of Ebola. Conclusions: The modest improvements in knowledge and reported attitudinal change toward Ebola virus disease suggests mHealth tutorial applications could hold promise for training health workers and building resilient health systems to respond to epidemics in West Africa

Effects of perceived cost, service quality, and customer satisfaction on health insurance service continuance

This paper aims to contribute to the universal discourse on financial services continuance behavior by examining the impact of service cost on customers\u27 service-quality perception and service continuance intention. It presents the results of an empirical study that has explored the impacts of service cost, service quality, and customer satisfaction on health insurance customers\u27 behavioral intention toward continuing or discontinuing with their service providers. Very few studies had examined the impact of service cost on service-quality perception. Our study attempts to fill that gap. A sample of 820 customers was surveyed, and 624 usable responses were analyzed with ANOVA, standard multiple regression, and logistic regression. Our findings indicate that, although highly satisfied health insurance customers will most likely retain their current service providers, customer dissatisfaction does not necessarily lead to discontinuance. Our results also provide some operational implications for health insurance managers, with strategies for reducing attrition and improving customer retention