Abstract. Previous evidence suggests that the accumulation of the hepatitis B virus (HBV) X gene region point mutations may be associated with the development of hepatocellular carcinoma (HCC). However, the pathogenesis of HCC remains to be elucidated. The aim of the present meta-analysis was to investigate the association between the HBV X gene point mutations and the risk of HCC. Studies were collected regarding the association between HBV X gene point mutations and the risk of HCC, which were identified in PubMed, EMBASE and China National Knowledge Infrastructure databases. The results were evaluated by use of odds ratios (ORs) and its 95% confidence intervals (CIs), which were pooled by random or fixed effects. A total of 11 studies involving 2,502 patients were included in this meta-analysis. Statistical summary ORs of HBV X gene point mutations were obtained for T1653 (OR, 3.11; 95% CI, 2.22-4.36), V1753 (OR, 2.55; 95% CI, 1.66-3.92), and T1762/A1764 (OR, 4.49; 95% CI, 2.86-7.07). HBV X gene point mutations T1653, V1753 and T1762/A1764 could increase the risk of HCC significantly, particularly the T1762/A1764 double mutations. These mutations may be predictive for hepatocarcinogenesis. However, these results of the meta-analysis should be treated carefully due to a low level of evidence.
Due to the heterogeneous, opaqueness and dynamic nature of Cloud Computing, existing MapReduce approach is not suitable for perform Parallel Computing on the cloud. In this paper, we propose a modified MapReduce framework which extracts the physical network topology information from the Virtual Machine Monitor (VMM) feature of VMs, in order to exploit dynamic resource allocations, and hence enable effective Parallel Computing within the cloud environment.
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