The generation and activity of serum neuralizing antibody in cynomolgus monkeys and SD rats undergoing long-term toxicity study with antitumor peptides HM-3 were investigated.The rats were administered intravenously with HM-3 at doses of 4.5 mg/kg,13.5 mg/kg,and 40.5 mg/kg for 6 months,and the cynomolgus monkeys were administrated intravenously with HM-3 at doses of 3 mg/kg,9 mg/kg and 27 mg/kg for 6 months,respectively.Anti-HM-3 antibodies and their neutralizing activities in serum samples taken every month after the administrations were determined by ELISA and cell migration assay,respectively.During the long-term administrations,anti-HM-3 antibodies were generated in some SD rats at high and moderate dose groups,and the antibody-neutralizing activities could be detected in a number of these samples (P ＜0.05).However,activity could be detected in very few monkeys (P ＜ 0.05),and the antibody titers were not correlated with the neutralizing activities.Therefore,we conclude that the antigenicity of HM-3 was low,but after long-term administration at high dose,low affinity neutralizing antibody could be generated in small number of samples.

With the development of recombinant DNA technology, genetically altered mouse models for human cancers are critical to the investigation and characterization of oncogene and tumor suppressor gene expression and function and the associated cancer phenotype. Recently, the inhibition of tumor angiogenesis becomes a new “hot spot” in cancer researches. And the genes involved in the regulation of tumor angiogenesis play an increasingly important role in the field of tumor researches. This review is about the progress of the research in transgenic tumor models and focuses on genes regulating tumor angiogenesis associated with transgenic mice model.

MiRNAs are a class of small non-coding RNAs,which regulate gene expression post-transcriptionally by partial complementary base pairing.Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients.In recent years,artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research.Compared to traditional bioinformatic tools,miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy,and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding.Additionally,the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers.In this article,we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms,focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Alcoholic liver disease (ALD) results from continuous and heavy alcohol consumption. The current treatment strategy for ALD is based on alcohol withdrawal coupled with antioxidant drug intervention, which is a long process with poor efficacy and low patient compliance. Alcohol-induced CYP2E1 upregulation has been demonstrated as a key regulator of ALD, but CYP2E1 knockdown in humans was impractical, and pharmacological inhibition of CYP2E1 by a clinically relevant approach for treating ALD was not shown. In this study, we developed a RNAi therapeutics delivered by lipid nanoparticle, and treated mice fed on Lieber-DeCarli ethanol liquid diet weekly for up to 12 weeks. This RNAi-based inhibition of Cyp2e1 expression reduced reactive oxygen species and oxidative stress in mouse livers, and contributed to improved ALD symptoms in mice. The liver fat accumulation, hepatocyte inflammation, and fibrosis were reduced in ALD models. Therefore, this study suggested the feasibility of RNAi targeting to CYP2E1 as a potential therapeutic tool to the development of ALD.