Design and Synthesis of (3-Phenylisoxazol-5-yl)methanimine Derivatives as Hepatitis B Virus Inhibitors.

Series of (3-phenylisoxazol-5-yl)methanimine derivatives were synthesized, and evaluated for anti-hepatitis B virus (HBV) activity in vitro. Half of them more effectively inhibited HBsAg than 3TC, and more favor to inhibit secretion of HBeAg than to HBsAg. Part of the compounds with significant inhibition on HBeAg were also effectively inhibit replication of HBV DNA. Compound (E)-3-(4-fluorophenyl)-5-((2-phenylhydrazineylidene)methyl)isoxazole inhibited excellently HBeAg with IC50 in 0.65 µM (3TC(Lamivudine) in 189.90 µM), inhibited HBV DNA in 20.52 µM (3TC in 26.23 µM). Structures of compounds were determined by NMR and HRMS methods, and chlorination on phenyl ring of phenylisoxazol-5-yl was confirmed by X-ray diffraction analysis, and the structure-activity relationships (SARs) of the derivatives was discussed. This work provided a new class of potent non-nucleoside anti-HBV agents.

Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms.

Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.