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Xiao‐Juan Yu Xiao‐Ren Peng Tong‐Huan Li 《Journal of cellular and molecular medicine》2014,18(12):2530-2535
Many studies have examined the association between the FABP2 (rs1799883) Ala54Thr gene polymorphism and type 2 diabetes mellitus risk (T2DM) in various populations, but their results have been inconsistent. To assess this relationship more precisely, A HuGE review and meta‐analysis were performed. The PubMed and CNKI database was searched for case‐control studies published up to April 2014. Data were extracted and pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated. Ultimately, 13 studies, comprising 2020 T2DM cases and 2910 controls were included. Overall, for the Thr carriers (Ala/Thr and Thr/Thr) versus the wild‐type homozygotes (Ala/Ala), the pooled OR was 1.18 (95% CI = 1.04–1.34, P = 0.062 for heterogeneity), for Thr/Thr versus Ala/Ala the pooled OR was 1.17 (95% CI = 1.05–1.41 P = 0.087 for heterogeneity). In the stratified analysis by ethnicity, the significantly risks were found among Asians but not Caucasians. This meta‐analysis suggests that the FABP2 (rs1799883) Ala54Thr polymorphisms are associated with increased susceptibility to T2DM risk among Asians but not Caucasians. 相似文献
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Plant and Soil - To investigate the effects of polystyrene microplastics (PS-beads) on the soil properties, photosynthesis of Flowering Chinese cabbage, the rhizosphere microbial community and... 相似文献
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<正>Aristolochic acids, mutational signature, and hepatocellular carcinoma Aristolochic acids (AA) are the etiologic agents of aristolochic acid nephropathy (AAN) and contribute to the global prevalence of chronic kidney disease and urothelial cancer (Grollman et al., 2007). DNA adducts formed by AA generate a unique AT transversions mutation spectrum at 相似文献
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Semiparametric Regression in Size-Biased Sampling 总被引:1,自引:0,他引:1
Ying Qing Chen 《Biometrics》2010,66(1):149-158
Summary . Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses. 相似文献
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