scholarly journals Long-term prediction of hepatocellular carcinoma using serum autotaxin levels after antiviral therapy for hepatitis C

2022 ◽  
pp. 100660
Author(s):  
Wataru Ando ◽  
Fumihiko Kaneko ◽  
Satoshi Shimamoto ◽  
Koji Igarashi ◽  
Katsuya Otori ◽  
...  
2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Tatehiro Kagawa ◽  
Emmet B. Keeffe

Chronic hepatitis C is a major cause of chronic liver disease globally, and the natural history of progression may lead to cirrhosis with liver failure, hepatocellular carcinoma, and premature liver-related death. Emerging data demonstrates that interferon-based therapy, particularly among those achieving a sustained virologic response (SVR), is associated with long-term persistence of SVR, improved fibrosis and inflammation scores, reduced incidence of hepatocellular carcinoma, and prolonged life expectancy. This reduction in the rate of progression has also been demonstrated in patients with chronic hepatitis C and cirrhosis in some but not all studies. The majority of these results are reported with standard interferon therapy, and long-term results of peginterferon plus ribavirin therapy with a higher likelihood of SVR should have a yet greater impact on the population of treated patients. The impact on slowing progression is greatest in patients with an SVR, less in relapsers, and equivocal in nonresponders. Thus, the natural history of chronic hepatitis C after completion of antiviral therapy is favorable with achievement of an SVR, although further data are needed to determine the likely incremental impact of peginterferon plus ribavirin, late long-term effects of therapy, and the benefit of treatment in patients with advanced hepatic fibrosis.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.


2011 ◽  
Vol 9 (3) ◽  
pp. 249-253 ◽  
Author(s):  
Angelo Iacobellis ◽  
Francesco Perri ◽  
Maria Rosa Valvano ◽  
Nazario Caruso ◽  
Grazia Anna Niro ◽  
...  

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