To test the hypothesis that the first degree relatives of Chinese women with polycystic ovarian syndrome (PCOS) have higher risk of cardiovascular disease than those without PCOS.The metabolic phenotype and risks of cardiovascular disease were evaluated in 110 family members of 35 women with PCOS and 85 unrelated healthy control subjects without family history of diabetes and PCOS ( four age- and weight-matched subgroups ).The prevalence of impaired glucose tolerance was 51.4％ in mothers and 57.5％ in fathers with their daughters suffering from PCOS.The first degree relatives of PCOS women had significantly higher serum fasting insulin level,homeostasis model assessment for insulin resistance,insulin area under the curve,and lower insulin sensitivity index in all subgroups than the control subjects( P＜0.05 ).The control subjects had significantly elevated high molecular weight-adiponectin levels and decreased high sensitive-C reactive protein levels compared to the first degree relatives of PCOS women in all subgroups.Parents and brothers,but not sisters,of women with PCOS had significantly higher total cholesterol and low density lipoprotein-cholesterol ( P＜ 0.05 ),as well as triglyceride levels ( P＜ 0.05 ),compared with control subjects.The first degree relatives of PCOS women had features of insulin resistance and increased risk of cardiovascular disease.

In subjects aged above 40 years old in Pudong,Shanghai,the annual progression rates from normal glucose regulation to impaired glucose tolerance and to diabetes were 9.5%and 4.4%respectively.and the annual progression rate in subjects with impaired Slucose regulation to diabetes was 20.2%.The conversion rate to diabetes increased along with elevated number of risk factors.

PPARγ2 gene C1431T polymorphism was assayed by PCR-RFLP in 200 polycystic ovary syndrome( PCOS)patients and 150 normal subjects. Serum adiponectin and leptin levels were determined by ELISA method. Polymorphism of the site might be associated with serum leptin and adiponectin concentrations and thiazolidinedione treatment in women with PCOS ( P<0.05 or P<0.01).

Forty women with polycystic ovary syndrome( PCOS) were enrolled. Basal plasma and urine cortisol, pituitary hormones, homeostasis model assessment for insulin resistance, and plasma cortisol in 0. 25 mg dexamethasone inhibition test were determined. In over-weight or obese PCOS patients, cortisol levels before and after inhibition test were increased (P<0. 05 or P<0. 01 ). The results suggest that the feed-back regulation of glucocorticoids to hypothalamas-pituitary is impaired and the change in hypothalamic-pituitary-adrenal axis is associated with insulin resistance.

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.