Enhanced intracellular retention of a hepatitis B virus strain associated with fulminant hepatitis

AbstractA plasmid carrying 1.3-fold HBV genome was constructed from a HBV strain that caused five consecutive cases of fulminant hepatitis (pBFH2), and HepG2 cells were transfected with pBFH2 or its variants. The pBFH2 construct with A1762T/G1764A, G1862T, and G1896A showed the largest amount of core particle-associated intracellular HBV DNA, but no significant increase of extracellular HBV DNA in comparison with the wild construct, suggesting that these mutations might work together for retention of the replicative intermediates in the cells. The retention might relate to the localization of hepatitis B core antigen (HBcAg) in the nucleus of HepG2, which was observed by confocal fluorescence microscopy. HBcAg immunohistochemical examination of liver tissue samples obtained from the consecutive fulminant hepatitis patients showed stronger staining in the nucleus than acute hepatitis patients. In conclusion, the fulminant HBV strain caused retention of the core particles and the core particle-associated HBV DNA in the cells

Common Peak Approach Using Mass Spectrometry Data Sets for Predicting the Effects of Anticancer Drugs on Breast Cancer

We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006). The common peak method is a simple way to select the sensible peaks that are shared with many subjects among all detected peaks by combining a standard spectrum alignment and kernel density estimates. The key idea of our proposed method is to apply the common peak approach to each class label separately. Hence, the proposed method gains more informative peaks for predicting class labels, while minor peaks associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for pattern recognition, based on the set of candidate peaks selected by the proposed method. The analysis gives good performance in the sense of test errors for classifying the class labels for a given feature vector of selected peak values