545 research outputs found

    Applying a novel combination of techniques to develop a predictive model for diabetes complications

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    © 2015 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models

    Caveolin-1 protects B6129 mice against Helicobacter pylori gastritis.

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    Caveolin-1 (Cav1) is a scaffold protein and pathogen receptor in the mucosa of the gastrointestinal tract. Chronic infection of gastric epithelial cells by Helicobacter pylori (H. pylori) is a major risk factor for human gastric cancer (GC) where Cav1 is frequently down-regulated. However, the function of Cav1 in H. pylori infection and pathogenesis of GC remained unknown. We show here that Cav1-deficient mice, infected for 11 months with the CagA-delivery deficient H. pylori strain SS1, developed more severe gastritis and tissue damage, including loss of parietal cells and foveolar hyperplasia, and displayed lower colonisation of the gastric mucosa than wild-type B6129 littermates. Cav1-null mice showed enhanced infiltration of macrophages and B-cells and secretion of chemokines (RANTES) but had reduced levels of CD25+ regulatory T-cells. Cav1-deficient human GC cells (AGS), infected with the CagA-delivery proficient H. pylori strain G27, were more sensitive to CagA-related cytoskeletal stress morphologies ("humming bird") compared to AGS cells stably transfected with Cav1 (AGS/Cav1). Infection of AGS/Cav1 cells triggered the recruitment of p120 RhoGTPase-activating protein/deleted in liver cancer-1 (p120RhoGAP/DLC1) to Cav1 and counteracted CagA-induced cytoskeletal rearrangements. In human GC cell lines (MKN45, N87) and mouse stomach tissue, H. pylori down-regulated endogenous expression of Cav1 independently of CagA. Mechanistically, H. pylori activated sterol-responsive element-binding protein-1 (SREBP1) to repress transcription of the human Cav1 gene from sterol-responsive elements (SREs) in the proximal Cav1 promoter. These data suggested a protective role of Cav1 against H. pylori-induced inflammation and tissue damage. We propose that H. pylori exploits down-regulation of Cav1 to subvert the host's immune response and to promote signalling of its virulence factors in host cells

    Study of the B +→ J / ψ Λ ¯ p decay in proton-proton collisions at √s = 8 TeV

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    A study of the B +→ J / ψ Λ ¯ p decay using proton-proton collision data collected at s = 8 TeV by the CMS experiment at the LHC, corresponding to an integrated luminosity of 19.6 fb−1, is presented. The ratio of branching fractions B(B+→J/ψΛ¯p)/B(B+→J/ψK∗(892)+) is measured to be (1.054 ± 0.057(stat) ± 0.035(syst) ± 0.011(B))%, where the last uncertainty reflects the uncertainties in the world-average branching fractions of Λ ¯ and K*(892) + decays to reconstructed final states. The invariant mass distributions of the J / ψ Λ ¯ , J/ψp, and Λ ¯ p systems produced in the B +→ J / ψ Λ¯ p decay are investigated and found to be inconsistent with the pure phase space hypothesis. The analysis is extended by using a model-independent angular amplitude analysis, which shows that the observed invariant mass distributions are consistent with the contributions from excited kaons decaying to the Λ ¯ p system. [Figure not available: see fulltext.

    Search for new neutral Higgs bosons through the H → ZA→ ℓ+ℓ−b b ¯ process in pp collisions at √s = 13 TeV

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    This paper reports on a search for an extension to the scalar sector of the standard model, where a new CP-even (odd) boson decays to a Z boson and a lighter CP-odd (even) boson, and the latter further decays to a b quark pair. The Z boson is reconstructed via its decays to electron or muon pairs. The analysed data were recorded in proton-proton collisions at a center-of-mass energy s = 13 TeV, collected by the CMS experiment at the LHC during 2016, corresponding to an integrated luminosity of 35.9 fb−1. Data and predictions from the standard model are in agreement within the uncertainties. Upper limits at 95% confidence level are set on the production cross section times branching fraction, with masses of the new bosons up to 1000 GeV. The results are interpreted in the context of the two-Higgs-doublet model. [Figure not available: see fulltext.]
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