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.

Objective:To investigate the feasibility of developing a radiomics model based on MRI and clinical features to predict the PD-L1 level in breast cancer.Methods:A total of 139 consecutive patients with breast cancer confirmed by pathology were enrolled retrospectively, including 79 PD-L1 negative patients and 60 PD-L1 positive patients. All patients were randomly assigned to a training dataset( n=97) and a validation dataset( n=42). Radiomics features were extracted from dynamic contrast-enhanced MRI. Radiomics feature selection was generated through the analysis of variance(ANOVA), least absolute shrinkage and selection operator(LASSO). Radiomics model and comprehensive model were developed for predicting the level of PD-L1. The receiver operating characteristic curve(ROC) was used to evaluate the predictive capacity of the models. Results:The radiomics model exhibited good performance in the training and validation datasets, with an area under the curve(AUC) of 0.847(95% confidence interval CI: 0.770-0.924) and 0.826(95% CI: 0.699-0.954), respectively. Compared with the radiomics model, the clinical feature combined prediction model showed better results, with AUC of 0.919(95% CI: 0.868-0.970) and 0.882(95% CI: 0.782-0.982), respectively, but without statistically significant difference( Z=1.32, P=0.19), respectively, but without statistically significant difference. Conclusions:The radiomi.Conclusions:The radiomics model has a certain value in preoperative prediction of PD-L1 expression level in breast cancer, which may be used as a supplement and improvement to the pathological gold standard to provide support for clinical decision-making.

Objective:To analyze the etiology of chronic spontaneous urticaria (CSU) in children and associated factors affecting the disease severity.Methods:A single-center cross-sectional study was conducted. Children aged ≤ 17 years with CSU were prospectively enrolled at the Department of Dermatology, Children′s Hospital of Chongqing Medical University from November 2021 to November 2022. Clinical data were collected, serum total IgE and allergen-specific IgE (sIgE) were detected, and basophil activation test (BAT) and autologous serum skin test (ASST) were performed. According to the ASST and BAT results, the children were divided into the chronic autoimmune urticaria (CAU) group (positive for both ASST and BAT), non-CAU group (negative for both ASST and BAT), and partial CAU group (positive for either ASST or BAT). Differences in the etiology and clinical characteristics were analyzed between the CAU group and the non-CAU group. Based on the weekly urticaria activity score (UAS7), the children with CSU were divided into the mild group (UAS7 < 16 points) and moderate to severe group (UAS7 ≥ 16 points). Factors associated with the severity of CSU in children were analyzed using logistic regression. Non-normally distributed quantitative data were expressed as M ( Q1, Q3), and the non-parametric rank sum test (Kruskal-Wallis test) was used to compare quantitative data among multiple groups. Results:This study enrolled a total of 93 children with CSU, including 50 males (53.8%) and 43 females (46.2%), with the age being 5.9 (2.9, 9.2) years, and the disease duration being 4 (2, 8) months; 32 patients (34.4%) were complicated by angioedema, 28 (30.1%) had a family history of chronic urticaria, 49 (52.7%) had a family history of atopic diseases, 14 (15.1%) had a family history of autoimmune diseases, and 26 (28.0%) had at least one atopic comorbidity. Etiologic analysis showed that 32 cases (32/69, 46.4%) were positive for ASST and 28 (28/70, 40.0%) were positive for BAT. Both ASST and BAT were performed in 57 cases, and they were divided into the CAU group (18 cases), non-CAU group (24 cases), and partial CAU group (15 cases) according to the test results. There were no significant differences in the age, disease duration, gender ratio, proportion of patients with atopic comorbidity, or proportion of patients having a family history of atopic diseases among the 3 groups (all P > 0.05), while the proportion of patients with moderate to severe CSU (UAS7 ≥ 16 points) was higher in the CAU group (16/18) than in the non-CAU group (11/24, P < 0.05). Triggering factors were identified in 19 cases (20.4%), including 18 (19.3%) cases of food allergy and 1 case (1.0%) of antibiotic allergy. The serum total IgE level was elevated in 22 cases (22/89, 24.7%), and 40 (40/81, 49.4%) showed elevated levels of at least 1 sIgE. The UAS7 of the children with CSU was 16 (15, 21) points, and there were 31 (33.3%) children with mild CSU and 62 (66.7%) with moderate to severe CSU. Univariate logistic regression analysis showed that BAT positivity was associated with disease severity ( OR = 7.566, 95% CI: 2.238 - 25.572, P < 0.05). After adjustment for age and gender, multivariate logistic regression analysis showed that BAT positivity was associated with moderate to severe CSU ( OR = 6.725, 95% CI: 1.361 - 33.227, P < 0.05) . Conclusions:Autoimmunity may be the main cause of CSU in children, followed by allergic factors. ASST could be used as a primary screening test for the diagnosis of CAU in children, and BAT may help identify CAU and predict disease severity.