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Ovarian cancer is the leading cause of death from gynaecological malignancies worldwide. Here we perform a three-stage genome-wide association study (GWAS) in Han Chinese women to identify risk genetic variants for epithelial ovarian cancer (EOC). We scan 900,015 single-nucleotide polymorphisms (SNPs) in 1,057 EOC cases and 1,191 controls in stage I, and replicate 41 SNPs (P meta o10 À 4 ) in 960 EOC cases and 1,799 controls (stage II), and an additional 492 EOC cases and 1,004 controls (stage III). Finally, we identify two EOC susceptibility loci at 9q22.33 (rs1413299 in COL15A1, P meta ¼ 1.88 Â 10 À 8 ) and 10p11.21 (rs1192691 near ANKRD30A, P meta ¼ 2.62 Â 10 À 8 ), and two consistently replicated loci at 12q14.2 (rs11175194 in SRGAP1, P meta ¼ 1.14 Â 10 À 7 ) and 9q34.2 (rs633862 near ABO and SURF6, P meta ¼ 8.57 Â 10 À 7 ) (Po0.05 in all three stages). These results may advance our understanding of genetic susceptibility to EOC.

Background Mobile health (mHealth)—a method of assisting long-term care in patients with chronic cardiovascular diseases (CVDs)—is gaining popularity in China, mainly owing to the large number of patients and limited clinical resources. Patients of different ages have varying needs for CVD management. However, evidence regarding how age influences Chinese CVD patients’ use and perceptions of mHealth is limited. Objective This study aimed to explore age-related differences among Chinese patients with CVD regarding their use and perceptions of mHealth and to determine the factors that influence this population’s willingness to use mHealth technologies. Methods We conducted a cross-sectional study of patients with chronic CVDs in a tertiary hospital in Beijing using a new questionnaire designed by the investigators. Participants were sourced using nonproportional quota-sampling methods, being recruited consecutively in each sampling category (age 18-49, 50-64, 65-74, and ≥75 years, with at least 25 men and 25 women in each age group). The survey consisted of 5 parts, including sociodemographic profile and medical history; current disease management situation; self-evaluation of disease management; current usage of mobile and internet technology (IT); and willingness to use an mHealth solution to perform disease self-management. Responses were compared among the 4 age groups as well as between patients who were willing to use mHealth solutions and those who were not. Multivariate logistic regression model was used to identify predictors of willingness to use mHealth for self-management. Results Overall, 231 patients (124 men) completed the questionnaire; of these, 53 were aged 18-49 years, 66 were aged 50-64 years, 54 were aged 65-74 years, and 58 were aged ≥75 years. Patients in the older cohorts visited hospitals more often than did those in the younger cohorts ( P <.001), and they also showed lower technology skills regarding the use of mobile or internet devices ( P <.001) and searched for health-related information on the internet less often ( P <.001). In addition, 68.0% (157/231) of the patients showed interest in using mHealth solution to manage their disease; of these, 40.8% (64/157) were aged ≥65 years. Patients who were more willing to use mHealth solution to manage their diseases were younger ( P <.001), more educated ( P <.001), still working ( P =.001), possessed higher skill regarding mobile or internet device use ( P <.001), and more frequently searched for health information on the internet ( P <.001). Finally, multivariate logistic regression showed that IT skill was the single indicator ( P =.003) of willingness to use mHealth, not age. Concl...

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BACKGROUND Detection of atrial fibrillation (AF) occurrence over a long duration has been a challenge in the screening and follow-up of AF patients. Wearable devices may be an ideal solution.OBJECTIVE The purpose of this study was to measure the sensitivity, specificity, and accuracy of a recently developed smart wristband device that is equipped with both photoplethysmographic (PPG) and single-channel electrocardiogram (ECG) systems and an AF-identifying, artificial intelligence (AI) algorithm, used in the short term.METHODS Use of the Amazfit Health Band 1S, which records both PPG and single-channel ECG data, was assessed in 401 patients (251 normal individuals and 150 ECG-diagnosed AF patients).RESULTS ECG and PPG readings could not be judged in 15 and 18 subjects, respectively. Subjects who were unable to be judged were defined as either false negative or false positive. The sensitivity, specificity, and accuracy of wristband PPG readings were 88.00%, 96.41%, and 93.27%, respectively, and those of wristband ECG readings were 87.33%, 99.20%, and 94.76%, respectively. When the original wristband ECG records were judged by physicians, the sensitivity, specificity, and accuracy were 96.67%, 98.01%, and 97.51%, respectively.CONCLUSION This promising new combination of PPG, ECG, and AI algorithm has the potential to facilitate AF detection.