Objective To observe the effects of Ketangte 2 on experimental diabetes in mice.Methods Adrenalin and streptozocin(STZ) were used to induce hyperglycemia mice model and diabetic mice model respectively.The mice were divided into five groups randomly:model group,glibenclamide control group,high-,middium-,low-dosage Ketangte 2 groups.The administration lasted consecutive 15 days.The blank control group and model group were treated with the same volume of saline.After treatment,the levels of blood sugar were measured in hyperglycemia mice induced by adrenalin,and the fasting blood-glucose,2-hour blood-glucose,insulin levels in diabetic mice induced by STZ were determined.Meanwhile the histopathological changes of pancreas were observed under light microscope.Results For STZ-induced diabetic mice,the hypoglycemic effect of Ketangte 2 was obvious(P

Objective To establish the HPLC-DAD-ELSD fingerprint of Radix astragali,provide new methods for science quality control of the medicinal materials.Methods Application of HPLC-DAD-ELSD techniques were connected in series.The mobile phase A: 10% acetonitrile,B: 90% acetonitrile,detecting wavelength: 265 nm,flow rate: 1 mL/min,column temperature: 35 ℃,sample size: 20 ?L,gain: 20,tube: 55 ℃,neb: 65%,air pressure: 2.068 5?105 Pa.The mutual mode was established depending on ten Astragalus samples from different growing areas in Gansu.The software "Similarity Evaluation System for Chromatographic Fingerfrint of Chinese Materia Medica" was applied to analyzing.ResultsThe established method is good for the separation of saponins,flavonoids from Radix Astragali,and simultaneous determination of the two different components in one sample injection.The similarity of different batches of medicinal materials is fit for the requirement.Conclusion The method is workable to simultaneously determine saponins and flavonoids fingerprint from Radix Astragali,and to control its quality.

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows:sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). Conclusion We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.