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MicroRNAs (miRNAs/miRs) are small, noncoding RNA molecules that are closely associated with the occurrence and development of tumors. miR-20b is overexpressed in hepatocellular carcinoma cell lines and tissues. However, it is not clear whether miR-20b can promote the proliferation of hepatocellular carcinoma cells. In the present study, the proliferation of H22 mouse hepatocellular carcinoma cells was detected using the Cell Counting Kit-8 assay. MiRanda software was used to predict the binding sites of miR-20b to the 3'-untranslated region (3'-UTR) of phosphatase and tensin homolog (PTEN). The 3'-UTR sequence of the PTEN gene was amplified using the polymerase chain reaction in H22 cells. The recombinant plasmid or empty plasmid was co-transfected with miR-20b mimics or miR-20b scramble into HeLa cells, and luciferase activity was assessed by Dual-Luciferase ® Reporter Assay System 24 h post-transfection. In the present study, miR-20b knockdown significantly inhibited the proliferation of H22 mouse hepatocellular carcinoma cells. In addition, miR-20b inhibition upregulated the expression of PTEN, and it was revealed that miR-20b may directly target the 3'-untranslated region of the PTEN gene. Downregulation of PTEN partially reversed the anti-proliferative effect of miR-20b on H22 cells. In conclusion, miR-20b may promote H22 cell proliferation by targeting PTEN, providing a rationale for further study investigating novel therapeutic strategies for liver cancer.

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Recognition of human actions from digital video is a challenging task due to complex interfering factors in uncontrolled realistic environments. In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities. Utilizing a cognitive-based data reduction method and a hybrid “network upon networks” architecture, we extract human action representations which are robust against spatial and temporal interferences and adaptive to variations in both action speed and duration. We evaluated our method on the UCF101 and other three challenging datasets. Our results demonstrated a superior performance of the proposed algorithm in human action recognition.