Variations in Helicobacter pylori Cytotoxin-Associated Genes and Their Influence in Progression to Gastric Cancer: Implications for Prevention

Helicobacter pylori (HP) is a bacterium that colonizes the human stomach and can establish a long-term infection of the gastric mucosa. Persistent Hp infection often induces gastritis and is associated with the development of peptic ulcer disease, atrophic gastritis, and gastric adenocarcinoma. Virulent HP isolates harbor the cag (cytotoxin-associated genes) pathogenicity island (cagPAI), a 40 kb stretch of DNA that encodes components of a type IV secretion system (T4SS). This T4SS forms a pilus for the injection of virulence factors into host target cells, such as the CagA oncoprotein. We analyzed the genetic variability in cagA and other selected genes of the HP cagPAI (cagC, cagE, cagL, cagT, cagV and cag Gamma) using DNA extracted from frozen gastric biopsies or from clinical isolates. Study subjects were 95 cagA+ patients that were histologically diagnosed with chronic gastritis or gastric cancer in Venezuela and Mexico, areas with high prevalence of Hp infection. Sequencing reactions were carried out by both Sanger and next-generation pyrosequencing (454 Roche) methods. We found a total of 381 variants with unambiguous calls observed in at least 10% of the originally tested samples and reference strains. We compared the frequencies of these genetic variants between gastric cancer and chronic gastritis cases. Twenty-six SNPs (11 non-synonymous and 14 synonymous) showed statistically significant differences (P<0.05), and two SNPs, in position 1039 and 1041 of cagE, showed a highly significant association with cancer (p-value = 2.07×10−6), and the variant codon was located in the VirB3 homology domain of Agrobacterium. The results of this study may provide preliminary information to target antibiotic treatment to high-risk individuals, if effects of these variants are confirmed in further investigations

A Functional NQO1 609C>T Polymorphism and Risk of Gastrointestinal Cancers: A Meta-Analysis

Background: The functional polymorphism (rs1800566) in the NQO1 gene, a 609C.T substitution, leading to proline-toserine amino-acid and enzyme activity changes, has been implicated in cancer risk, but individually published studies showed inconclusive results. Methodology/Principal Findings: We performed a meta-analysis of 20 publications with a total of 5,491 cases and 5,917 controls, mainly on gastrointestinal (GI) cancers. We summarized the data on the association between the NQO1 609C.T polymorphism and risk of GI cancers and performed subgroup analyses by ethnicity, cancer site, and study quality. We found that the variant CT heterozygous and CT/TT genotypes of the NQO1 609 C.T polymorphism were associated with a modestly increased risk of GI cancers (CT vs. CC: OR = 1.10, 95 % CI = 1.01 – 1.19, P heterogeneity = 0.27, I 2 = 0.15; CT/TT vs. CC: OR = 1.11, 95%CI = 1.02 – 1.20, Pheterogeneity = 0.14; I 2 = 0.27). Following further stratified analyses, the increased risk was only observed in subgroups of Caucasians, colorectal cancer in Caucasians, and high quality studies. Conclusions: This meta-analysis suggests that the NQO1 609T allele is a low-penetrance risk factor for GI cancers. Although the effect on GI cancers may be modified by ethnicity and cancer sites, small sample seizes of the subgroup analyse

Agent-based modeling and reinforcement learning for optimizing energy systems operation and maintenance: the pathmind solution

The optimization of the Operation and Maintenance (O&amp;M) of energy systems equipped with Prognostics and Health Management (PHM) capabilities can be framed as a sequential decision process, which can be addressed by Reinforcement Learning (RL). However, using RL algorithms requires specific skills, whereas the understanding of the possibly counter-intuitive solutions proposed by RL is not straifhtforward. To sidestep both issues, we use Pathmind, a software tool which enables effectively exploiting the RL capabilities without deep knowledge of machine learning. Pathmind is encoded in the Anylogic environment, which is an Agent-Based simulation software that simplifies the system modeling and allows easily visualizing the effects of the optimized policy. A scaled-down wind farm case study is used to demonstrate the potential of RL in identifying an optimal O&amp;M policy and to show the ease of use of Pathmind and AnyLogic