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Background Disability influences physical and mental health of older people, and causes a heavy burden on family and society. The increasing prevalence of empty-nested phenomenon has aroused wide concern for empty and non-empty nesters. Objective To investigate the prevalence and associated factors of comprehensive disability in empty and non-empty nesters in Henan, providing a scientific basis for the prevention and control of disability among these people. Methods By use of multistage stratified sampling, 5 570 older people aged ≥60 years were selected from Henan's 18 provincial cities from January to September 2019 for a questionnaire survey. The comprehensive ability of them was assessed by using the Ability Assessment for Older Adults Scale developed by the Ministry of Civil Affairs of the People's Republic of China. Binary Logistic stepwise regression analysis was used to identify the factors associated with comprehensive disability. Results The participants included 3 731 empty nesters and 1 839 non-empty nesters. The prevalence of intact comprehensive ability, and comprehensive disability in empty nesters was 51.97% (1 939/3 731) , and 48.03% 〔1 792 (1 417 mild, 217 moderate, and 104 severe) /3 731〕, respectively. The prevalence of intact comprehensive ability, and comprehensive disability in non-empty nesters was 44.64% (821/1 839) , and 55.36% 〔1 018 (820 mild, 123 moderate, and 75 severe) /1 839〕, respectively. Empty nesters had lower prevalence of comprehensive disability than did non-empty nesters (χ2=26.448, P

Objective To investigate the features of breast cancer with different sizes of tumor examined using conventional ultrasound and contrast-enhanced ultrasound (CEUS). Methods A total of 107 cases with breast cancer diagnosed by pathology were retrospectively analyzed. There were one group(group≤2.0 cm) with tumor maximum diameter ≤2.0 cm (50.5%, 54 cases) and other group (group >2.0 cm) with that >2.0 cm (49.5%, 53 cases) based on diameter of breast cancer. The features shown with both conventional ultrasound and CEUS examination were compared between two groups. Results Non-parallel orientation was more present in group ≤2.0 cm than in group >2.0 cm by conventional ultrasound [19 cases(35.2%) vs. 6 cases(11.3%), P=0.004]. More cases with Alder grade of blood flow 0-Ⅰ were found in group ≤2.0 cm than those in group >2.0 cm [41 (75.9%) cases vs. 25 (47.2%) cases, P=0.002]. There were four characteristics with differences between two groups in CEUS examination. First was there were 23(42.6%) cases of breast cancer with hypo-enhancement and iso-enhancement in ≤2.0 cm group and 5 (9.4%) cases in group >2.0 cm with statistically significant differences, P2.0 cm [34 (63.0%) cases vs. 20 (37.7%) cases, P=0.009]. Third, the cases with filling defect of contrast in tumor were more in group ≤2.0 cm than in group >2.0 cm [49(90.7%) cases vs. 30(56.6%) cases, P2.0 cm [48(88.9%) cases vs. 37(69.8%) cases, P=0.038]. The differences between two groups were significant statistically. Conclusions The results in this study showed that the difference in the characteristics of conventional ultrasound and CEUS imaging is clear in different size of breast cancer. The size of focus should be considered in the analysis of CEUS.

BackgroundAs a leading cause of ischemic cerebrovascular disease, carotid atherosclerosis (CAS) lowers the productivity of steelworkers. An increasing number of scholars have used machine learning to identify readily available factors to predict the risk of diseases. But there is still a lack of research on risk prediction models for CAS.ObjectiveTo compare the performance of support vector machine (SVM) -, BP neural network (BPNN) - and random forest (RF) -based models in predicting the risk of CAS in steelworkers.Methods4 568 steelworkers who underwent physical examination and health monitoring in Tangshan Hongci Hospital from March to June 2017 were selected for a survey using the Health Assessment Checklist developed by us for understanding their information about demographic characteristics (sex, age, BMI, education level, marital status) , personal behavior and lifestyle (smoking and drinking) , medical history (hypertension, diabetes, family history of CAS) , occupation history (current work in shifts, working under high temperature or in noisy environments) . Levels of serum cholesterol, triglyceride, homocysteine and uric acid were also collected. Variables for building SVM-, BPNN- and RF-based models for predicting the risk of CAS were determined using unconditioned multivariate Logistic regression analysis and literature review.ResultsIn predicting the risk of CAS in participants in the training set, the accuracy, sensitivity and specificity were 83.81%, 80.10%, 87.32%, respectively, for the SVM-based model, 79.27%, 66.19%, 91.62%, respectively, for the BPNN-based model, and 86.60%, 73.62%, and 98.90%, respectively, for the RF-based model. And the AUC for SVM-, BPNN- and RF-based models was 0.84, 0.79 and 0.86, respectively. The SVM-based model had the highest sensitivity, while the RF-based model had the highest accuracy and specificity (P