Objective Exploring the mechanisms of Sanwu Huangqin Decoction in the treatment of liver tumors based on network pharmacology and molecular docking technologies and experimental observations.Methods TCMSP database was used to screen the effective compounds contained in Sanwu Huangqin decoction,Drug bank,Pharm mapper,Uniport,PubMed and other databases were used to obtain the action targets of Sanwu Huangqin decoction,gene cards and TCGA databases were used to obtain the related targets of hepatocellular carcinoma,and the"drug-disease"target genes were obtained after the intersection with the action targets of Sanwu Huangqin decoction,and the protein interaction network was constructed using string database,David and KEGG databases were used for go and KEGG enrichment analysis,Cytoscape software was used to build the corresponding network,and discovery studios software was used for molecular docking to verify the above network pharmacology related results.The effects of Sanwu Huangqin decoction on the proliferation,the metastasis and the expression of IL-6 of cancer cells were observed.Results 81 effective components and 10 key components of Sanwu Huangqin decoction,249 targets,1420 differential targets between tumor samples and normal samples were obtained,and 59 overlapping targets were obtained.Frequency analysis of protein interaction network:the potential mechanism of Sanwu Huangqin Decoction in treating hepatocellular carcinoma is related to ESR1,Myc,Jun,IL-6,MMP9,EGF,etc.Through GO and KEGG enrichment analysis,IL-17 signaling pathway,TNF signaling pathway,Toll like receptor signaling pathway may play important roles.The results of molecular docking showed that acacetin,wogonin,baicalein,oroxylin a,beta sitosterol,8-isoopenenyl-kaempferol,formononetin,luteolin,quercetin,and stigmastrol bound to IL-6 protein,which further confirmed the above results.Animal experiments showed that Sanwu Huangqin decoction could inhibit the tumor size and the pulmonary metastasis,and the content of IL-6 in cancer cells was decreased,which preliminarily verified the anti-tumor effects and mechanism of Sanwu Huangqin decoction.Conclusion In this study,we found that IL-6 was the key target of Sanwu Huangqin decoction by network pharmacology and molecular docking technology.Sanwu Huangqin decoction could reduce the content of IL-6 in tumor tissues to inhibit liver cancer in vivo.These results provided a theoretical basis for the application of Sanwu Huangqin decoction in the treatment of liver cancer.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the detection trend of vaginal intraepithelial neoplasia(VaIN)of lower genital tract from 2013 to 2015. Methods A retrospective analysis was undertaken of colposcopy-directed biopsy of cervical, vaginal and vulvar intraepithelial neoplasia lesions include cervical intraepithelial neoplasia (CIN), VaIN and vulvar intraepithelial neoplasia (VIN) in Obstetrics and Gynecology Hospital of Fudan University from January 2013 to December 2015. Results (1) Overall data of CIN, VaIN and VIN:a total of 16732 cases were diagnosed of lower genital intraepithelial neoplasia in 3 years, accounting for 23.20% (16732/72128) of total colposcopy-directed biopsy cases. Among them, CIN, VaIN and VIN accounted for 19.48%(14053/72128), 2.67%(1923/72128), 1.05%(756/72128) of total colposcopy-directed biopsy cases of the lower genital tract, 83.99%(14053/16732), 11.49%(1923/16732), 4.52%(756/16732) of total lower genital intraepithelial neoplasia, respectively. (2) Annual data of CIN, VaIN and VIN from 2013 to 2015. The annual proportion of CIN in all intraepithelial neoplasia of lower gential tract was basically stable, consisting of 86.02%(3955/4598),83.25%(4795/5760) and 83.20%(5303/6374), respectively. The annual proportion of VaIN was gradually increasing, consisting of 8.09% (372/4598), 12.45%(717/5760) and 13.08%(834/6374), respectively. The annual proportion of VIN was gradually decreasing, consisting of 5.89% (271/4598), 4.31% (248/5760) and 3.72% (237/6374), respectively. Conclusion The increasing detection of VaIN from 2013 to 2015 might correlate with the increasing attention to inspection of the entire vaginal wall.