The 341C/T polymorphism in the GSTP1 gene is associated with increased risk of oesophageal cancer

<p>Abstract</p> <p>Background</p> <p>The Glutathione S-transferases (GSTs) comprise a group of enzymes that are critical in the detoxification of carcinogens. In this study the effects of polymorphisms in these genes on the risk of developing oesophageal squamous cell carcinoma (OSCC) were evaluated in a hospital-based case-control study in two South African population groups. Genetic polymorphisms in GSTs were investigated in 245 patients and 288 controls samples by PCR-RFLP analysis.</p> <p>Results</p> <p>The <it>GSTP1 341T </it>variant was associated with significantly increased risk of developing OSCC as observed from the odds ratios for the <it>GSTP1 341C/T </it>and GSTP1 341T/T genotypes (OR = 4.98; 95%CI 3.05-8.11 and OR = 10.9; 95%CI 2.43-49.1, respectively) when compared to the homozygous GSTP1 341C/C genotype. The risk for OSCC in the combined GSTP1 341C/T and T/T genotypes was higher in tobacco smokers (OR = 7.51, 95% CI 3.82-14.7), alcohol consumers (OR = 15.3, 95% CI 1.81-12.9) and those using wood or charcoal for cooking and heating (OR = 12.1, 95% CI 3.26-49) when compared to those who did not smoke tobacco, or did not consume alcohol or user other forms of fuel for cooking and heating. Despite the close proximity of the two GSTP1 SNPs (313A>G and 341C>T), they were not in linkage disequilibrium in these two population groups (D':1.0, LOD: 0.52, r²: 0.225). The GSTP1 313A/G polymorphism on the other hand, did not display any association with OSSC. The homozygous <it>GSTT1*0 </it>genotype was associated with increased risk of OSCC (OR = 1.71, 95%CI 1.18-2.46) while the homozygous <it>GSTM1*0 </it>genotype was associated with significantly decreased risk of OSCC in the Mixed Ancestry subjects (OR= 0.39, 95%CI 0.25-0.62).</p> <p>Conclusions</p> <p>This study shows that the risk of developing OSCC in the South African population can be partly explained by genetic polymorphisms in GST coding genes and their interaction with environmental factors such as tobacco smoke and alcohol consumption.</p

Diversity of Woodland Communities and Plant Species along an Altitudinal Gradient in the Guancen Mountains, China

Study on plant diversity is the base of woodland conservation. The Guancen Mountains are the northern end of Luliang mountain range in North China. Fifty-three quadrats of 10 m × 20 m of woodland communities were randomly established along an altitudinal gradient. Data for species composition and environmental variables were measured and recorded in each quadrat. To investigate the variation of woodland communities, a Two-Way Indicator Species Analysis (TWINSPAN) and a Canonical Correspondence Analysis (CCA) were conducted, while species diversity indices were used to analyse the relationships between species diversity and environmental variables in this study. The results showed that there were eight communities of woodland vegetation; each of them had their own characteristics in composition, structure, and environment. The variation of woodland communities was significantly related to elevation and also related to slope, slope aspect, and litter thickness. The cumulative percentage variance of species-environment relation for the first three CCA axes was 93.5%. Elevation was revealed as the factor which most influenced community distribution and species diversity. Species diversity was negatively correlated with elevation, slope aspect, and litter thickness, but positively with slope. Species richness and heterogeneity increased first and then decreased but evenness decreased significantly with increasing elevation. Species diversity was correlated with slope, slope aspect, and litter thickness

Fast GPU-Based Two-Way Continuous Collision Handling

Step-and-project is a popular way to simulate non-penetrated deformable bodies in physically-based animation. First integrating the system in time regardless of contacts and post resolving potential intersections practically strike a good balance between plausibility and efficiency. However, existing methods could be defective and unsafe when the time step is large, taking risks of failures or demands of repetitive collision testing and resolving that severely degrade performance. In this paper, we propose a novel two-way method for fast and reliable continuous collision handling. Our method launches the optimization at both ends of the intermediate time-integrated state and the previous intersection-free state, progressively generating a piecewise-linear path and finally reaching a feasible solution for the next time step. Technically, our method interleaves between a forward step and a backward step at a low cost, until the result is conditionally converged. Due to a set of unified volume-based contact constraints, our method can flexibly and reliably handle a variety of codimensional deformable bodies, including volumetric bodies, cloth, hair and sand. The experiments show that our method is safe, robust, physically faithful and numerically efficient, especially suitable for large deformations or large time steps

EGTSyn: Edge-based Graph Transformer for Anti-Cancer Drug Combination Synergy Prediction

Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.Comment: 15 pages,4 figures,6 table