Search

Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.

B7-H4 is a member of B7 family which regulates immune responses by delivering costimulatory signals. However, it negatively regulates T cell-mediated immunity and may play an important role in tumor immune evasion. Although several studies have been reported that expression of B7-H4 is elevated in the several types of human cancer with a poor clinical outcome, its clinical significance in the prostate cancer (PCa) has not been well studied. In this study, we investigated the clinical significance of B7-H4 in human PCa and determined if B7-H4 expression is associated with the cancer cell stemness in PCa. Our studies show that expression of B7-H4 is correlated with the pathologic tumor (pT) stage and the clinical stage of PCa. The Kaplan-Meier survival analysis revealed that PCa patients with high expression of B7-H4 exhibits a shorter overall survival (OS) rate. Univariate and multivariate Cox regression analysis indicated that B7-H4 is an independent poor prognostic factor of PCa. In addition, the expression of B7-H4 is correlated with the cancer cell stemness associated genes expression in PCa. Further, our studies show that B7-H4 regulates cancer cell stemness associated genes expression and effects on the cell cycle and PI3K/Akt signaling related genes expression in PCa. These results indicate that B7-H4 expression is associated with cancer cell stemness, and B7-H4 is a potential prognostic biomarker and a therapeutic target of PCa.

Background Many studies have reported the impact of air pollution on cardiovascular disease (CVD), but few of these studies were conducted in severe haze-fog areas. The present study focuses on the impact of different air pollutant concentrations on daily CVD outpatient visits in a severe haze-fog city. Methods Data regarding daily air pollutants and outpatient visits for CVD in 2013 were collected, and the association between six pollutants and CVD outpatient visits was explored using the least squares mean (LSmeans) and logistic regression. Adjustments were made for days of the week, months, air temperature and relative humidity. Results The daily CVD outpatient visits for particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the 90th-quantile group were increased by 30.01, 29.42, 17.68, 14.98, 29.34%, and − 19.87%, respectively, compared to those in the 10 300- and 500-μg/m3, PM2.5 100- and 300-μg/m3 and CO 3-mg/m3 groups were 2.538 (1.070–6.020), 7.781 (1.681–36.024), 3.298 (1.559–6.976), 8.72 (1.523–49.934), and 5.808 (1.016–33.217), respectively, and their corresponding attributable risk percentages (AR%) were 60.6, 87.15, 69.68, 88.53 and 82.78%, respectively. The strongest associations for PM10, PM2.5 and CO were found only in lag 0 and lag 1. The ORs for the increase in CVD outpatient visits per increase in different units of the six pollutants were also analysed. Conclusions All five air pollutants except O3 were positively associated with the increase in daily CVD outpatient visits in lag 0. The high concentrations of PM10, PM2.5 and CO heightened not only the percentage but also the risk of increased daily CVD outpatient visits. PM10, PM2.5 and CO may be the main factors of CVD outpatient visits.