The relationship between hepatitis B virus (HBV) and nonhepatocellular cancers remains inconclusive. This large case-control study aimed to assess the associations between HBV infection status and multiple cancers. Cases (n = 50 392) and controls (n = 11 361) were consecutively recruited from 2008 to 2016 at the First Affiliated Hospital of Nanjing Medical University. Multivariable adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were estimated using logistic regression by adjusting age and gender. A meta-analysis based on published studies was also performed to verify the associations. Of these, 12.1% of cases and 5.5% of controls were hepatitis B surface antigen (HBsAg) seropositive. We observed significant associations between HBsAg seropositivity and esophagus cancer (aOR [95% CI] = 1.32

Background: This study aimed to investigate the prevalence and risk factors for hypertension, diabetes, and dyslipidemia, and to evaluate their additive effects on myocardial infarction (MI) and stroke in Nanjing in East China.Methods: A multistage, stratified random cluster sampling method was used to select representative participants. All eligible participants completed questionnaires, physical measurements, and blood tests.Multivariable and univariable logistic regression analyses were used to identify associated risk factors and evaluate additive effects on cardiovascular events, respectively.Results: Hypertension was the most prevalent chronic disease among 11,036 participants enrolled (18.5%), followed by dyslipidemia (8.3%) and diabetes (6.0%). The prevalence of hypertension was higher in men than in women while no sex-related difference was observed in the prevalence of diabetes and dyslipidemia.Older age and higher body mass index were risk factors for all three diseases. Sex, central obesity, smoking, number of family members, salt intake, and family history of hypertension were associated with hypertension; central obesity, smoking, alcohol assumption, and family history of diabetes correlated with diabetes; and female sex, higher education, and alcohol assumption were risk factors for dyslipidemia. Hypertension complicated with dyslipidemia conferred more risk of MI and stroke than independent effects. Diabetes also contributed to risk based on hypertension or dyslipidemia. Conclusions:The burden of hypertension and diabetes has stopped increasing. However, total cholesterol (TC) concentration in the population has not been well controlled. A more comprehensive approach to managing dyslipidemia, hypertension, and diabetes needs to be developed, especially for individuals with multiple cardiovascular risk factors.

Automated electrocardiogram (ECG) diagnosis could be a useful aid for clinical use. We applied a deep learning method to build a system for automated detection and classification of ECG signals. We first trained a convolutional neural network (CNN) to detect cardiovascular disease in ECG signals using a training data set of 259,789 ECG signals collected from the cardiac function rooms of a tertiary care hospital. The CNN classification was validated using an independent test data set of 18,018 ECG signals.The labels used covered >90% of clinical diagnoses. The system grouped ECGs into 18 classifications-17 different types of abnormalities and normal ECG. The overall accuracy of the model was tested and found to be close to 95%; the accuracy for diagnosis of normal rhythm/atrial fibrillation was 99.15%. The proposed CNN model could help reduce misdiagnosis and missed diagnosis in primary care settings and also improve efficiency and save manpower cost for large general hospitals.

No abstract