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The level of CHB virus (HBV) core antibody (HBcAb) is different in four stages of chronic HBV infection and may be used for differential diagnosis of the natural history of chronic HBV infection. To address this question, we examined multiple blood biomarkers and assessed the efficacy to diagnose different stages of chronic HBV infection. The quantitative detection of HBcAb, hepatitis B surface antigen (HBsAg), HBV DNA, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and platelet count (PLT) were determined in the serum of 73 cases of low-replicative phase (LR), 46 cases of immune-tolerant phase (IT), 44 cases of immune clearance phase (IC), and 57 cases of HBeAg-negative hepatitis (ENH). Differentiating performance of these serum protein levels was analyzed by receiver operating characteristic (ROC) curve analysis. Our results showed that the levels of HBcAb, ALT, and AST levels were significantly higher in IC and ENH than those in LR and IT (both P ≤ 0.001). The levels of HBV DNA and HBsAg were higher in IC and IT than those in LR and ENH (both P ≤ 0.001). Logistic regression models showed that HBcAb, HBsAg, HBV DNA, ALT, and AST were the independent variables, respectively, and when combined, they provided high diagnostic accuracy for the staging of CHB. To sum up, HBcAb quantification is a new index, which can reflect whether the liver is in the immune activation state of HBV infection, and is related to the inflammatory state of the host liver. The combined detection of HBcAb quantification and other indicators has showed promising efficiency for staging of IC and ENH and can assist the diagnosis and treatment of CHB. [ABSTRACT FROM AUTHOR]

This paper has made proposition of a nested array and an estimation algorithm for direction-of-arrival (DOA) of two-dimensional (2D) coherently distributed (CD) sources. According to the difference coarray concept, double parallel hole-free virtual uniform linear arrays are generated by virtue of vectorization operation on cross-correlation matrices of subarrays. Sensor coordinates of virtual arrays are derived. Rational invariance relationships of virtual arrays are derived. According to the rotational invariance relationships, matrices satisfying rotation invariance are constructed by extracting and regrouping the receive vectors of the virtual arrays, and then an estimation of signal parameters via rotational invariance techniques- (ESPRIT-) like framework on matrix reconstruction is deduced. Optimal configuration of the nested array as well as computational complexity are analyzed. Without pair matching, the proposed method can resolve more sources than the sensor number. Simulation outcomes indicate that the proposed method tends to have a better performance as compared to the traditional uniform arrays that have similar number of sensors. [ABSTRACT FROM AUTHOR]