Studies into the mechanism of arsenic-induced neurotoxicity

Arsenic (As) is a notoriously poisonous metalloid with known hazardous effects to human health. The project described in this thesis was aimed at elucidating the probable mechanism of As-induced neurotoxicity in vivo and in vitro. The animal studies in this thesis were designed to answer questions about the effect of As on the peripheral nervous system after sub-acute and chronic intoxication of laboratory rats. Protein composition analysis showed compositional changes in sciatic nerves proteins. Protein expression of neurofilament heavy (NF-H) and neurofilament medium (NF-M) remained unchanged. Neurofilament protein light (NF-L) expression was reduced, while _- and m-calpain protein expression was increased, both in a dose/time pattern. Furthermore, NF-H protein was hypophosphorylated; while NF-L and microtubule-associated protein tau (MAP-tau) proteins were phosphorylated. In the in vitro studies, effects of As species were tested in various cell culture models and the manner of their hyperphosphorylation was further studied for a better understanding of the disruption of neuroskeletal integrity by As. In vitro studies showed that the compositional changes were not caused by the changes on RNA expression levels, rather a post-translational activity. Cells treated with arsenite showed cleavage of p35 to p25 by calpain, which is mediated by an increase of Ca2+ in the cells. Over expression of calpain results in hyperphosphorylation of NF-L and activated calpain is also responsible for NF-L degradation.AZL Onderzoeks- en Ontwikkelingskrediet Apotheek and the J.E. Juriaanse StichtingUBL - phd migration 201

Three-dimensional finite element analysis of a porcelain crowned tooth

The Publisher's final version can be found by following the DOI link. Open access article.open access articleThe restoration of endodontically treated teeth is one of the main challenges in restorative dentistry since the weakened tooth structure is more prone to biomechanical failures due to significant tooth loss. The aim of this paper is to computational analysis of prepared crowned tooth in order to differentiate the possibility of using porcelain material for typical clinical condition and masticatory load by using the three-dimensional finite element method (3D FEM). In order to have an accurate geometry of tooth model, a coordinate measuring machine (CMM) is proposed to scan the tooth. The obtained scanned contours exported to ABAQUS FE package for computational stress analysis. The prosthodontics crown FEM has been created and put on simulated chewing stresses. The model is composed of four different materials, namely; prepared tooth, luting cement, substructure (IPS Empress Core), and Ingot (IPS Empress Layer). The generated FEM run and the stress distributions of the crowned tooth is thoroughly investigated. The developed model is extremely useful for indicating tooth biomechanics and has the tendency to deliver a better understanding to designers in the biomedical engineering field and dentistry

Improving the Performance in Occupational Health and Safety Management in the Electric Sector: An Integrated Methodology Using Fuzzy Multicriteria Approach

The electric sector is fundamental for the economic and social development of society, impacting on essential aspects such as health, education, employment generation, industrial production, and the provision of various services. In addition to the above, the growing trend in energy consumption worldwide could increase, according to expert estimates, up to 40% by 2030, which in turn increases the efforts of the public and private sector to meet increasing demands and increase access to energy services under requirements of reliability and quality. However, the electricity sector presents challenges and complexities, one of which is the reduction of health and safety risks for workers, service users, and other stakeholders. In many countries, this sector is classified as high risk in occupational safety and health, due to its complexity and the impact of accidents and occupational diseases on the health of workers, in infrastructure, in operating costs and competitiveness of the energy sector. Worldwide, there are rigorous regulations for the electricity sector, from local and national government regulations to international standards to guarantee health and safety conditions. However, it is necessary to develop objective and comprehensive methodologies for evaluating occupational safety and health performance that provides solutions for the electricity sector, not only to comply with standards and regulations also as a continuous improvement tool that supports the decision-making processes given the complexity of the industry and the multiple criteria that are taken into account when evaluating and establishing improvement strategies. In scientific literature, different studies focus on the analysis of accident statistics, the factors that affect accidents and occupational diseases, and the risk assessment of the sector. Despite these considerations, studies that focus directly on the development of hybrid methodologies for the evaluation and improvement of performance in occupational safety and health in the electrical sector, under multiple criteria and uncertainty are mostly limited. Therefore, this document presents an integrated methodology for improving the performance in occupational health and safety in the electric sector through the application of two techniques of Multi-criteria Decision Methods (MCDM) uses in environments under uncertainly. First, the fuzzy Analytic Hierarchy Process (FAHP) is applied to estimate the initial relative weights of criteria and sub-criteria. The fuzzy set theory is incorporated to represent the uncertainty of decision-makers’ preferences. Then, the Decision-making Trial and Evaluation Laboratory (DEMATEL) used for evaluating the interrelations and feedback among criteria and sub-criteria. FAHP and DEMATEL are later combined for calculating the final criteria and sub-criteria weights under vagueness and interdependence. Subsequently, we applied the proposed methodology in a company of the energy sector for diagnosis of performance in OHS to establish improvement proposals, the work path, and implementation costs. Finally, we evaluate the impact of the strategies applied in the improvement of the performance of the company

An integrated approach of multiple correspondences analysis (MCA) and fuzzy AHP method for occupational health and safety performance evaluation in the land cargo transportation

Land cargo transportation is one of the components of the logistics chain with high impact on economic and social development worldwide. However, problems such as top logistics costs, deficiencies in transportation infrastructure and the failure to adopt good operating practices in aspects such as quality, environment, and occupational safety and health affect the ability of companies to comply with the agreements, requirements, and regulations of the clients and other interested parties. One of the most relevant problems for the sector is associated with the high accident rates that make this medium less advantageous compared to other means of transport with impact on operational costs, on logistics indicators, on compliance with legal regulations and customer satisfaction. However, although there are legal standards and management standards in occupational safety and health, evaluating performance can become a difficult and subjective process, due to the complexity of the land cargo transportation and the different interest groups involved. Besides, there is little information in the literature that provides solutions for the industry. Therefore, this document presents an integrated approach between multi-criterion decision making models (MCDM) and the Multiple Correspondences Analysis (MCA) to facilitate the evaluation and improvement of occupational health and safety performance, with a logical process, objective, robust and using both qualitative and quantitative techniques, with real application in the land cargo transportation sector. First, the multivariate method of Multiple Correspondences Analysis (MCA) was used for the evaluation of a sample of companies in the industry, considering the factors and sub-factors identified in the first stage and performing correlational analyzes among the variables. Subsequently, a multicriteria decision-making model was designed to determine the factors and sub-factors that affect occupational health and safety performance through the technique of the Fuzzy Analytic Hierarchy Process (FAHP). Finally, improvement strategies are proposed based on the approaches suggested in this document

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Genome-Wide Association Study Identifies Chromosome 10q24.32 Variants Associated with Arsenic Metabolism and Toxicity Phenotypes in Bangladesh

Arsenic contamination of drinking water is a major public health issue in many countries, increasing risk for a wide array of diseases, including cancer. There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however, the basis of this variation is not well understood. Here, we have performed the first genome-wide association study (GWAS) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health. Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10−8) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations. In a follow-up analysis of 1,085 individuals with arsenic-induced premalignant skin lesions (the classical sign of arsenic toxicity) and 1,794 controls, we show that one of these five variants (rs9527) is also associated with skin lesion risk (P = 0.0005). Using a subset of individuals with prospectively measured arsenic (n = 769), we show that rs9527 interacts with arsenic to influence incident skin lesion risk (P = 0.01). Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10−12) and neighboring gene C10orf32 (P = 10−44), which are involved in C10orf32-AS3MT read-through transcription. This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date, the only GWAS of any arsenic-related trait, and the first study to implicate 10q24.32 variants in both arsenic metabolism and arsenical skin lesion risk. The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species. These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide

Dose-response relationship between arsenic exposure and the serum enzymes for liver function tests in the individuals exposed to arsenic: a cross sectional study in Bangladesh

<p>Abstract</p> <p>Background</p> <p>Chronic arsenic exposure has been shown to cause liver damage. However, serum hepatic enzyme activity as recognized on liver function tests (LFTs) showing a dose-response relationship with arsenic exposure has not yet been clearly documented. The aim of our study was to investigate the dose-response relationship between arsenic exposure and major serum enzyme marker activity associated with LFTs in the population living in arsenic-endemic areas in Bangladesh.</p> <p>Methods</p> <p>A total of 200 residents living in arsenic-endemic areas in Bangladesh were selected as study subjects. Arsenic concentrations in the drinking water, hair and nails were measured by Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). The study subjects were stratified into quartile groups as follows, based on concentrations of arsenic in the drinking water, as well as in subjects' hair and nails: lowest, low, medium and high. The serum hepatic enzyme activities of alkaline phosphatase (ALP), aspartate transaminase (AST) and alanine transaminase (ALT) were then assayed.</p> <p>Results</p> <p>Arsenic concentrations in the subjects' hair and nails were positively correlated with arsenic levels in the drinking water. As regards the exposure-response relationship with arsenic in the drinking water, the respective activities of ALP, AST and ALT were found to be significantly increased in the high-exposure groups compared to the lowest-exposure groups before and after adjustments were made for different covariates. With internal exposure markers (arsenic in hair and nails), the ALP, AST and ALT activity profiles assumed a similar shape of dose-response relationship, with very few differences seen in the higher groups compared to the lowest group, most likely due to the temporalities of exposure metrics.</p> <p>Conclusions</p> <p>The present study demonstrated that arsenic concentrations in the drinking water were strongly correlated with arsenic concentrations in the subjects' hair and nails. Further, this study revealed a novel exposure- and dose- response relationship between arsenic exposure metrics and serum hepatic enzyme activity. Elevated serum hepatic enzyme activities in the higher exposure gradients provided new insights into arsenic-induced liver toxicity that might be helpful for the early prognosis of arsenic-induced liver diseases.</p

Women's Education Level, Maternal Health Facilities, Abortion Legislation and Maternal Deaths: A Natural Experiment in Chile from 1957 to 2007

The aim of this study was to assess the main factors related to maternal mortality reduction in large time series available in Chile in context of the United Nations' Millennium Development Goals (MDGs).Time series of maternal mortality ratio (MMR) from official data (National Institute of Statistics, 1957-2007) along with parallel time series of education years, income per capita, fertility rate (TFR), birth order, clean water, sanitary sewer, and delivery by skilled attendants were analysed using autoregressive models (ARIMA). Historical changes on the mortality trend including the effect of different educational and maternal health policies implemented in 1965, and legislation that prohibited abortion in 1989 were assessed utilizing segmented regression techniques.During the 50-year study period, the MMR decreased from 293.7 to 18.2/100,000 live births, a decrease of 93.8%. Women's education level modulated the effects of TFR, birth order, delivery by skilled attendants, clean water, and sanitary sewer access. In the fully adjusted model, for every additional year of maternal education there was a corresponding decrease in the MMR of 29.3/100,000 live births. A rapid phase of decline between 1965 and 1981 (-13.29/100,000 live births each year) and a slow phase between 1981 and 2007 (-1.59/100,000 live births each year) were identified. After abortion was prohibited, the MMR decreased from 41.3 to 12.7 per 100,000 live births (-69.2%). The slope of the MMR did not appear to be altered by the change in abortion law.Increasing education level appears to favourably impact the downward trend in the MMR, modulating other key factors such as access and utilization of maternal health facilities, changes in women's reproductive behaviour and improvements of the sanitary system. Consequently, different MDGs can act synergistically to improve maternal health. The reduction in the MMR is not related to the legal status of abortion