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AIM To establish a cell culture system with long-term replication of hepatitis C virus in vitro.METHODS Human hepatoma cell line 7721 was tested for its susceptibility to HCV by incubating with a serum from a patient with chronic hepatitis C. Cells and supernatant were harvested at various phases during the culturing periods. The presence of HCV RNA, the expression of HCV antigens in cells and/or supernatant were examined by RT-PCR, in situ hybridization and immunohisto-chemistry respectively. RESULTSThe intracellular HCV RNA was first detected on d2 after infection and then could be intermittently detected in both cells and supernatant over a period of at least three months. The expression of HCV NS 3 , CP 10 antigens could be observed in cells. The fresh cells could be infected by supernatant from cultured infected cells and the transmission of viral genome from HCV-infected 7721 cells to PBMCs was also observed.CONCLUSION The hepatoma line 7721 is not only susceptible to HCV but also supports its long-term replication in vitro.

Substantial deep learning methods have been utilized for hyperspectral image (HSI) classification recently. Vision Transformer (ViT) is skilled in modeling the overall structure of images and has been introduced to HSI classification task. However, the fixed patch division operation in ViT may lead to insufficient feature extraction, especially the features of the edges between patches will be ignored. To address this problem, we devise a workflow for HSI classification based on the Nested Transformers (NesT). The NesT employs the block aggregation module to extract edge information between patches, which realizes cross-block communication of nonlocal information and optimizes global information extraction. In this paper, the NesT is used for HSI classification for the first time. The experiments are carried out on four widely used hyperspectral datasets: Indian Pines, Salinas, Tea Farm, and Xiongan New Area (Matiwan Village). The obtained results reveal that the NesT can provide competitive results compared to conventional machine learning and deep learning methods and achieve top accuracy on four datasets, which proves the superiority of the NesT in HSI classification with limited training samples.

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