Inferior response of Asian vs non-Asian hepatitis C genotype 3 infection to combination antiviral therapy
ABSTRACT Racial differences in response to treatment of hepatitis C virus (HCV) have been noted in several trials. In this study, we compared the response rate to treatment of Asian patients infected by genotype 3 HCV with non-Asians treated for the same genotype. Sixteen of 38 (42.1%) Asians achieved a sustained virological response (SVR), compared with 41 of 66 (62.1%) Caucasians (P = 0.063). At baseline prior to treatment, Asians had a higher histological fibrosis stage (P = 0.0014), indicating more advanced disease at presentation. In univariable analysis of baseline factors predicting failure to achieve an SVR, Asian ethnicity, fibrosis stage, higher serum aspartate transaminase, bilirubin and alkaline phosphatase, as well as lower white cell count, haemoglobin and platelet count were statistically significant. None of these factors achieved significance in multivariate analysis, possibly because of the relatively small number of patients studied. We have observed an inferior response to treatment of Asian vs Caucasian patients. The poor response probably reflects the more advanced liver disease at baseline observed for Asian British patients.
Conference Paper: A modified fuzzy clustering based on multisynapse neural network[Show abstract] [Hide abstract]
ABSTRACT: Traditional Hopfield neural networks has ability of optimization computation, image segmentation, etc. However, there exist some problems in this network, i.e. it can only solve linear or quadratic optimal problems. So, Wei and Fahn proposed a new neural architecture, the multisynapse neural network, to solve optimization problems including high-order, logarithmic, sinusoidal forms, etc. As one of its major applications, a fuzzy bidirectional associative clustering network (FBACN) is presented for fuzzy clustering according to the objective functional method. In this paper, first, FBACN is analyzed in detail in theory and some drawbacks is pointed. Then we present a modified FBACN, named as MFBACN, by using expended Lagrange multipliers method. Moreover, we also propose a method of determining Lagrange multipliers. Finally we conduct the experiments with three datasets. The experimental results show that the convergence of MFBACN holds and it is an effective method.Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on; 09/2005
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ABSTRACT: Hepatitis C virus (HCV) is the foremost cause of parenterally transmitted non-A, non-B hepatitis. Effective treatment with Interferon (IFN) based regimens has been shown to reduce morbidity and mortality, improve health-related quality of life, and avoid the huge costs associated with end stage liver disease. HCV-3 has been associated in Europe and the USA to illicit drug abuse in the 70's, while recent epidemiological reports have shown that HCV-3 prevalence is on the rise in both Western Europe and in Middle East. The standard of care for patients with HCV-3 is a 24 week therapy regimen with a combination of Pegylated Interferon (Peg-IFN) and Ribavirin (RBV). Despite the cumulative high rates of sustained virological response (SVR) obtained with this schedule of treatment, it is now clear that a subgroup of patients exists in which lower rates of SVR are achieved. Bridging fibrosis/cirrhosis, high baseline viremia and lack of rapid virological response (RVR) have been identified as predictors of treatment failure in many studies. Recently, "allocation" and randomization trials based on HCV-RNA negativity at week 4 (RVR) have evaluated the chance of abbreviating the treatment schedule to 12-16 weeks, since RVR emerged as a strong predictor of SVR. In this review article we will discuss the current therapeutic strategies in HCV-3 to understand in which subset of patients further treatment customization is possible.
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ABSTRACT: See article in J. Gastroenterol. Hepatol. 2009; 24: 366–371.Journal of Gastroenterology and Hepatology 04/2009; 24(3):330-2. DOI:10.1111/j.1440-1746.2009.05788.x · 3.63 Impact Factor