Objective To explore the prevalence and risk factors of knee osteoarthritis (OA) among the elderly in Tianjin. Methods Totally 2038 elderly in Tianjin were investigated from January 2010 to June 2011 according to protocol of APLA-COPCORD Core Questionnaire to identification of risk factors for knee OA. Results The prevalence rates of knee pain and knee OA were 23.1％ and 21.7％,respectively. Knee OA appeared much frequently in women than men (27.6 ％ vs.16.1 ％,x2 =46.893,P＜0.001 ) in the trend of increase with aging(x2 =764.34,P＜0.001).BMI in knee OA group [(25.4±3.3) kg/m2]was significantly higher than in non-knee OA group [(23.8± 2.9) kg/m2],(t =7.944,P＜0.001).Menopause age in female knee OA group was younger than in female non-knee OA group [(50.2±3.7) years vs.(52.8±4.1) years,t=7.665,P＜0.001].Binary logistic regression revealed that age,sex,BMI and age of menopause were risk factors of knee OA,and eating vegetable might prevent from osteoarthritis,whereas there were no significantly association between knee OA and the other factors such as smoking and drinking.Conclusions Age,female and overweight are identified as risk factors of knee OA.

Objective To establish a quality indicator system of nursing services for patients with urodynamics. Methods Delphi method was used in the study, and the quality indicator system of nursing services for patients was based on two rounds of consultation among 16 experts. Results The quality indicator system of nursing services for patients with urodynamics consisted of 3 first-level indicators, 8 second-level indicators and 29 third-level indicators. Experts had the same opinions on the indicators. Conclusions Nursing behavior for patients with urodynamics could be standardized based on the quality indicator system, and therefore guide nursing work and improve nursing quality. Further theoretical and empirical study is needed to verify the quality indicator system.

Objective:To create radiomics models based on abbreviated multimodal magnetic resonance imaging (MRI) for the diagnosis of breast cancer.Methods:All breast MR imaging data between Jun 2014 and Mar 2019 were retrospectively collected. Patients with pathological results of puncture or surgical resection were involved in this study. One thousand three hundred and six patients (416 benign and 890 breast cancer) were divided into training cohort ( n=702), internal validation cohort ( n=302), and external validation cohort ( n=302). All images were reduced to: the joint model group [including T2 weighted imaging (T2WI), DWI (diffusion-weighted imaging) and first contrast-enhanced sequences], non-enhanced group (T2WI and DWI) and single-phase enhanced group (first contrast-enhanced sequences). Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimension of texture features. Three supervised machine learning algorithms (Bagging decision tree, Gaussian process, support vector machine) were used to predict benign and malignant breast lesions, and the best classifier was selected to construct breast cancer diagnosis model. Models were validated by internal and external validation cohorts. Results:The Gaussian process algorithm was chosen. The area under the curve (AUC) of the joint model and the non-enhanced model for predicting breast cancer were 0.903 and 0.893 for the training cohort, 0.893 and 0.863 for the internal validation cohort, and 0.878 and 0.864 for the external validation cohort.Conclusions:The radiomics model based on abbreviated multimodal MRI can accurately diagnose breast cancer. And the non-enhanced model can accurately diagnose breast cancer without contrast enhancement, which provides feasibility for simplifying the diagnosis process.