Objective:The aim of this study is to explore the active ingredients and mechanism of action of danhong injection(DHI)in treating myeloproliferative neoplasms using network pharmacology.Methods:The TCMSP platform and ...Objective:The aim of this study is to explore the active ingredients and mechanism of action of danhong injection(DHI)in treating myeloproliferative neoplasms using network pharmacology.Methods:The TCMSP platform and relevant literature were used to search for the active ingredients and targets of Radix Salviae and Carthami Flos in DHI.Disease targets related to myeloproliferative neoplasms were obtained from the GEO database,GeneCards,and DisGeNET database.The queried component targets were normalized using the UniProt database.Potential targets were identified by constructing protein-protein interactions networks using STRING 11.5 and visualized and analyzed using Cytoscape 3.9.1.GO and KEGG analysis were performed using the Metascape platform,and visualization was done using the built-in plug-in CluoGO or SangerBox platforms with Cytoscape 3.9.1.Results:The active ingredients of DHI for treating myeloproliferative neoplasms mainly consist of flavonoids and o-benzoquinones,including quercetin,luteolin,kaempferol,stigmasterol,tanshinone iia,cryptotanshinone,beta-carotene,2-isopropyl-8-methylphenanthrene-3,4-dione,and neocryptotanshinone ii.The potential targets are JUN,TP53,STAT3,AKT1,MAPK1,RELA,TNF,MAPK14,IL6,and FOS.The relevant signaling pathways involved are mainly TNFαsignaling pathway,PI3K-Akt signaling pathway,apoptosis,IL-17 signaling pathway,cellular senescence,MAPK signaling pathway,p53 signaling pathway,JAK-STAT signaling pathway,and NF-kappa B signaling.Conclusions:DHI acts mainly through flavonoids and o-benzoquinones to treat myeloproliferative neoplasms in a multi-targeted and multi-pathway manner.展开更多
AIM:Through exploring the regulation of gene expression during hepatocarcinogenesis induced by aflatoxin B1 (AFB1),to find out the responsible genes for hepatocellular carcinoma (HCC) and to further understand the und...AIM:Through exploring the regulation of gene expression during hepatocarcinogenesis induced by aflatoxin B1 (AFB1),to find out the responsible genes for hepatocellular carcinoma (HCC) and to further understand the underlying molecular mechanism.METHODS:Tree shrews ( Tupaia belangeri chinensis)were treated with or without AFB1 for about 90 weeks. Liver biopsies were performed regularly during the animal experiment. Eight shares of total RNA were respectively isolated from 2 HCC tissues, 2 HCC-surrounding noncancerous liver tissues, 2 biopsied tissues at the early stage(30^th week) of the experiment from the same animals as above, 1 mixed sample of three liver tissues biopsied at the beginning (0^th week) of the experiment, and another i mixed sample of two liver tissues from the untreated control animals biopsied at the 90^th week of the experiment. The samples were then tested with the method of Atlas^TM cDNA microarray assay. The levels of gene expression in these tissues taken at different time points during hepatocarcinogenesis were compared.RESULTS:The profiles of differently expressed genes were quite different in different ways of comparison.At the same period of hepatocarcinogenesis, the genes in the same function group usually had the same tendency for up-or down-regulation. Among the checked 588 genes that were known to be related to human cancer, 89 genes (15.1%) were recognized as “important genes” because they showed frequent changes in different ways of comparison. The differentially expressed genes during hepatocarcinogenesis could be classified into four categories: genes up-regulated in HCC tissue, genes with similar expressing levels in both HCC and HCC-surrounding liver tissues which were higher than that in the tissues prior to the development of HCC,genes down-regulated in HCC tissue, and genes up-regulated prior to the development of HCC but down-regulated after the development of HCC.CONCLUSION: A considerable number of genes could change their expressing levels both in HCC and in HCC-surrounding non-cancerous liver tissues. A few modular genes were up-regulated only in HCC but not in surrounding liver tissues, while some apoptosis-related genes were down-regulated in HCC and up-regulated in surrounding liver tissues. To compare gene-expressing levels among the liver tissues taken at different time points during hepatocarcinogenesis may be helpful to locate the responsible gene (s) and understand the mechanism for AFB1 induced liver cancer.展开更多
The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critic...The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online sup-port vector regression （OSVR） is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of sup-port vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable con- vergence speed and can solve continuous problems that are infeasible using lookup table.展开更多
基金This work has been supported by grants from the Taishan Scholars Program(TSQN201812015)the Program for Multidisciplinary Research and Innovation Team of Young Scholars at Shandong University(2020QNQT007).
文摘Objective:The aim of this study is to explore the active ingredients and mechanism of action of danhong injection(DHI)in treating myeloproliferative neoplasms using network pharmacology.Methods:The TCMSP platform and relevant literature were used to search for the active ingredients and targets of Radix Salviae and Carthami Flos in DHI.Disease targets related to myeloproliferative neoplasms were obtained from the GEO database,GeneCards,and DisGeNET database.The queried component targets were normalized using the UniProt database.Potential targets were identified by constructing protein-protein interactions networks using STRING 11.5 and visualized and analyzed using Cytoscape 3.9.1.GO and KEGG analysis were performed using the Metascape platform,and visualization was done using the built-in plug-in CluoGO or SangerBox platforms with Cytoscape 3.9.1.Results:The active ingredients of DHI for treating myeloproliferative neoplasms mainly consist of flavonoids and o-benzoquinones,including quercetin,luteolin,kaempferol,stigmasterol,tanshinone iia,cryptotanshinone,beta-carotene,2-isopropyl-8-methylphenanthrene-3,4-dione,and neocryptotanshinone ii.The potential targets are JUN,TP53,STAT3,AKT1,MAPK1,RELA,TNF,MAPK14,IL6,and FOS.The relevant signaling pathways involved are mainly TNFαsignaling pathway,PI3K-Akt signaling pathway,apoptosis,IL-17 signaling pathway,cellular senescence,MAPK signaling pathway,p53 signaling pathway,JAK-STAT signaling pathway,and NF-kappa B signaling.Conclusions:DHI acts mainly through flavonoids and o-benzoquinones to treat myeloproliferative neoplasms in a multi-targeted and multi-pathway manner.
基金Supported by National Natural Science Foundation of China,NO.39860072 and NO.39869001Natural Science Foundation of Guangxi Zhuang Autonomous Region,NO.9817137
文摘AIM:Through exploring the regulation of gene expression during hepatocarcinogenesis induced by aflatoxin B1 (AFB1),to find out the responsible genes for hepatocellular carcinoma (HCC) and to further understand the underlying molecular mechanism.METHODS:Tree shrews ( Tupaia belangeri chinensis)were treated with or without AFB1 for about 90 weeks. Liver biopsies were performed regularly during the animal experiment. Eight shares of total RNA were respectively isolated from 2 HCC tissues, 2 HCC-surrounding noncancerous liver tissues, 2 biopsied tissues at the early stage(30^th week) of the experiment from the same animals as above, 1 mixed sample of three liver tissues biopsied at the beginning (0^th week) of the experiment, and another i mixed sample of two liver tissues from the untreated control animals biopsied at the 90^th week of the experiment. The samples were then tested with the method of Atlas^TM cDNA microarray assay. The levels of gene expression in these tissues taken at different time points during hepatocarcinogenesis were compared.RESULTS:The profiles of differently expressed genes were quite different in different ways of comparison.At the same period of hepatocarcinogenesis, the genes in the same function group usually had the same tendency for up-or down-regulation. Among the checked 588 genes that were known to be related to human cancer, 89 genes (15.1%) were recognized as “important genes” because they showed frequent changes in different ways of comparison. The differentially expressed genes during hepatocarcinogenesis could be classified into four categories: genes up-regulated in HCC tissue, genes with similar expressing levels in both HCC and HCC-surrounding liver tissues which were higher than that in the tissues prior to the development of HCC,genes down-regulated in HCC tissue, and genes up-regulated prior to the development of HCC but down-regulated after the development of HCC.CONCLUSION: A considerable number of genes could change their expressing levels both in HCC and in HCC-surrounding non-cancerous liver tissues. A few modular genes were up-regulated only in HCC but not in surrounding liver tissues, while some apoptosis-related genes were down-regulated in HCC and up-regulated in surrounding liver tissues. To compare gene-expressing levels among the liver tissues taken at different time points during hepatocarcinogenesis may be helpful to locate the responsible gene (s) and understand the mechanism for AFB1 induced liver cancer.
文摘The goal in reinforcement learning is to learn the value of state-action pair in order to maximize the total reward. For continuous states and actions in the real world, the representation of value functions is critical. Furthermore, the samples in value functions are sequentially obtained. Therefore, an online sup-port vector regression （OSVR） is set up, which is a function approximator to estimate value functions in reinforcement learning. OSVR updates the regression function by analyzing the possible variation of sup-port vector sets after new samples are inserted to the training set. To evaluate the OSVR learning ability, it is applied to the mountain-car task. The simulation results indicate that the OSVR has a preferable con- vergence speed and can solve continuous problems that are infeasible using lookup table.
