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Our results suggest that strong TAA-specific CD8(+) T-cell responses suppress the recurrence of HCC. Immunotherapy to induce TAA-specific cytotoxic T lymphocytes by means such as the use of peptide vaccines should be considered for clinical application in patients with HCC after local therapy.

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A unique hepatitis C virus (HCV) strain JFH-1 has been shown to replicate efficiently in cell culture with production of infectious HCV. We previously developed a DNA expression system containing HCV cDNA flanked by two self-cleaving ribozymes to generate HCV particles in cell culture. In this study, we produced HCV particles of various genotypes, including 1a (H77), 1b (CG1b), and 2a (J6 and JFH-1), in the HCVribozyme system. The constructs also contain the secreted alkaline phosphatase gene to control for transfection efficiency and the effects of culture conditions. After transfection into the Huh7-derived cell line Huh7.5.1, continuous HCV replication and secretion were confirmed by the detection of HCV RNA and core antigen in the culture medium. HCV replication levels of strains H77, CG1b, and J6 were comparable, whereas the JFH-1 strain replicates at a substantially higher level than the other strains. To evaluate the infectivity in vitro, the culture medium of JFH-1-transfected cells was inoculated into naive Huh7.5.1 cells. HCV proteins were detected by immunofluorescence 3 days after inoculation. To evaluate the infectivity in vivo, the culture medium from HCV genotype 1b-transfected cells was inoculated into a chimpanzee and caused a typical course of HCV infection. The HCV 1b propagated in vitro and in vivo had sequences identical to those of the HCV genomic cDNA used for cell culture transfection. The development of culture systems for production of various HCV genotypes provides a valuable tool not only to study the replication and pathogenesis of HCV but also to screen for antivirals.Hepatitis C virus (HCV) is a major public health problem and infects about 200 million people worldwide (12, 18). The majority of HCV-infected patients fail to clear the virus, and many develop chronic liver diseases, including cirrhosis and hepatocellular carcinoma. HCV does not replicate efficiently in cultured cells, and robust model systems for HCV infection have been difficult to develop. Recently, we identified a unique HCV genotype 2a strain JFH1 that can replicate and produce viral particles efficiently in cell culture and established an HCV infection model system with cell culture generated JFH-1 virus that is infectious both in vitro and in vivo (5, 7-10, 16, 22, 26).Like other RNA viruses, HCV displays marked genetic heterogeneity and is currently classified into six major genotypes (19). Among these genotypes, genotypes 1 and 2 have worldwide distribution and are known to be associated with different clinical profiles and therapeutic responses (25). These differences in clinical features are likely to be a result of viral characteristics. Study of the molecular mechanisms underlying such differences would provide valuable information regarding the pathogenesis and therapy of hepatitis C in humans. Despite the development of the JFH1 infectious cell culture system, similar systems with other HCV strains have been difficult to establish. Recent studies have shown the production of infectious 1a strain in vitr...

BACKGROUND & AIMS-Hepatitis C virus (HCV) gains entry into susceptible cells by interacting with cell surface receptor(s). Viral entry is an attractive target for antiviral development because of the highly conserved mechanism.

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Prediction of reaction yields by machine learning approach is demonstrated in tungsten-catalyzed epoxidation of alkenes. The various electronic and vibrational parameters of the phosphonic acids are collected by DFT simulation, and chosen by LASSO as the essential parameters for prediction of the reaction yields. With the trained model, we can predict yields of the reaction with unverified phosphonic acids with an error of 26%. Keywords: Prediction of reaction yields | Machine learning | Catalyst informaticsRational design and optimization of a catalyst for a transition-metal-catalyzed reaction require the synthesis and examination of various catalyst molecules, but this traditional "trial-and-error" approach generally costs time and labor. Prediction of the reaction yield with machine learning would facilitate the rational design and optimization, and allow discovery of a highly reactive catalyst rapidly and efficiently. Building up this "catalyst informatics" approach, a practical method to develop the prediction model for catalyst reactivity such as the reaction yields is highly desirable.Statistical data analysis has recently been utilized in catalysis for the correlation and prediction of various reaction outcomes such as selectivity and reactivity.1 For example, Sigman et al. have demonstrated multiple linear regression analysis to select the key parameters for the prediction of the enantioselectivities in various transition-metal-catalyzed asymmetric reactions. 2,3They have collected various molecular parameters such as steric, electronic, and vibrational factors by simulation, and have correlated with ¦¦G ‡ values that represent the difference in transition state energies and are proportional to enantiomeric ratio (e.r.). The method has also been applied to the prediction of site-, 4 diastereo-, 5 and chemoselectivities, 6 which are also represented as ¦¦G ‡ values. While various reaction outcomes could be predicted by this methodology, the prediction of the reaction yields in transition-metal-catalyzed reaction is still very rare, 7,8 and a successful example has been recently demonstrated by Doyle et al., who applied the multiple linear regression analysis to correlate the reaction yields of nickel-catalyzed Suzuki coupling reaction with three molecular parameters of phosphine ligands, cone angles (ª), %volume of sphere of radius r occupied by ligand (%V bur ), and electrostatic potential (V min ). 9In this work, we report a new statistical method for the prediction of the reaction yields of the catalytic reaction with logistic regression analysis and LASSO (Least Absolute Shrinkage and Selection Operator).10 Logistic regression analysis is a fitting model that enables development of a suitable model for the percentage values such as the reaction yields compared to the linear regression analysis, and LASSO is a model optimization method that can quantitatively select the key parameters.11 We adapt this approach to predict the reaction yield of the tungsten-catalyzed epoxidation of alkene with hydrogen ...

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