The relationship of hepatitis history and pathological diagnosis of primary liver cancer

Antiviral effects of PNA in duck hepatitis B virus infection model

AIM

To study the efficacy of antiviral treatment with PNA for the duck model of HBV (DHBV)-infected ducks. PNA is a 2-amine-9-(2,3-dideoxy-2,3-dihydro-beta-D-arabinofuranosyl)-6-methoxy-9H-purine.

METHODS

The Sichuan Mallard ducklings in the hepatitis B virus model were treated with PNA, a new antiviral agent. DHBV DNA from the blood serum and liver tissues were measured at 0, 5, and 10 d during the treatment and at 3 d withdrawal by real-time PCR. The duck hepatitis B surface antigen (DHBsAg) in the liver cells was observed by Immunohistochemistry (IHC). Pathological changes in the liver tissues were also observed. Control group I was administered with distilled water and control group II was administered with 3-thiacytidine. Treatment group I was administered with PNA at a dose of 40 mg/kg and treatment group II was administered perorally (po) with PNA at a dose of 80 mg/kg. Treatment group III was administered with PNA at a dose of 20 mg/kg and treatment group IV was intravenously administered with PNA at a dose of 40 mg/kg. Each group contained 15 ducklings.

RESULTS

PNA can significantly lower the DHBV replication levels in serum and liver. Compared with control group II, there were no significant differences in inhibiting efficacy in treatment groups I and III (P>0.05) and there were significant differences in inhibiting efficacy in treatment groups II and IV (P<0.05). Interestingly, significant differences were observed at 3 d withdrawal. The DHBV replication levels in each group slightly increased at 3 d withdrawal, but rebounded slightly in the PNA treatment groups than in control group II (P<0.05). The DHBV replication levels in the treatment groups were lower than in control group I. The DHBV replication levels in sera had a positive relationship with that in the liver, but the DHBV replication levels in the liver was lower than that in sera. Pathological changes in the treatment groups were obviously improved and the changes were associated with liver viral DNA levels.

CONCLUSION

The results demonstrate that PNA is a strong inhibitor of DHBV replication in the DHBV-infected duck model.

Marine sponges as pharmacy

Marine sponges have been considered as a gold mine during the past 50 years, with respect to the diversity of their secondary metabolites. The biological effects of new metabolites from sponges have been reported in hundreds of scientific papers, and they are reviewed here. Sponges have the potential to provide future drugs against important diseases, such as cancer, a range of viral diseases, malaria, and inflammations. Although the molecular mode of action of most metabolites is still unclear, for a substantial number of compounds the mechanisms by which they interfere with the pathogenesis of a wide range of diseases have been reported. This knowledge is one of the key factors necessary to transform bioactive compounds into medicines. Sponges produce a plethora of chemical compounds with widely varying carbon skeletons, which have been found to interfere with pathogenesis at many different points. The fact that a particular disease can be fought at different points increases the chance of developing selective drugs for specific targets.

Screening test data analysis for liver disease prediction model using growth curve

This study was done based on screening test data accumulated from 1994 to 2001 for studying of risk factor related with liver disease and prediction model of liver disease. In the existing study related with liver, the main current is studying on liver cancer, not on liver disease, previous step into liver cancer. As a result of estimating prediction model through the risk factors of liver disease and the growth curve on the basis of data, it is shown that most of the risk factors about liver disease are also those about known well as liver cancer. In addition, to investigate liver disease prevalence from the viewpoint of the future, this study presumed risk factor through the various growth curve analysis and examined logistic regression, decision tree and neural network from those estimators. In the case of neural network using growth curve estimator of Xi(5)=alphai+betaiT+epsiloniT, accuracy of liver disease was 72.55% and sensitivity was 78.62%. On the other hand, in the case of liver disease prediction model using recent screening test data estimator, accuracy was 72.09% and sensitivity was 71.72%. Those are lower than liver disease prediction model of growth curve analysis. In the various liver disease prediction models assumed by growth curve and many distinction models, when growth curve estimator was used, sensitivity value was improved.