No abstract

Increasing evidence has shown that sex differences exist in Adverse Drug Events (ADEs). Identifying those sex differences in ADEs could reduce the experience of ADEs for patients and could be conducive to the development of personalized medicine. In this study, we analyzed a normalized US Food and Drug Administration Adverse Event Reporting System (FAERS). Chi-squared test was conducted to discover which treatment regimens or drugs had sex differences in adverse events. Moreover, reporting odds ratio (ROR) and P value were calculated to quantify the signals of sex differences for specific drug-event combinations. Logistic regression was applied to remove the confounding effect from the baseline sex difference of the events. We detected among 668 drugs of the most frequent 20 treatment regimens in the United States, 307 drugs have sex differences in ADEs. In addition, we identified 736 unique drug-event combinations with significant sex differences. After removing the confounding effect from the baseline sex difference of the events, there are 266 combinations remained. Drug labels or previous studies verified some of them while others warrant further investigation.

End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model-hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.

Gut microbiota plays a dual role in chronic kidney disease (CKD) and is closely linked to production of uremic toxins. Strategies of reducing uremic toxins by targeting gut microbiota are emerging. It is known that Chinese medicine rhubarb enema can reduce uremic toxins and improve renal function. However, it remains unknown which ingredient or mechanism mediates its effect. Here we utilized a rat CKD model of 5/6 nephrectomy to evaluate the effect of emodin, a main ingredient of rhubarb, on gut microbiota and uremic toxins in CKD. Emodin was administered via colonic irrigation at 5ml (1mg/day) for four weeks. We found that emodin via colonic irrigation (ECI) altered levels of two important uremic toxins, urea and indoxyl sulfate (IS), and changed gut microbiota in rats with CKD. ECI remarkably reduced urea and IS and improved renal function. Pyrosequencing and Real-Time qPCR analyses revealed that ECI resumed the microbial balance from an abnormal status in CKD. We also demonstrated that ten genera were positively correlated with Urea while four genera exhibited the negative correlation. Moreover, three genera were positively correlated with IS. Therefore, emodin altered the gut microbiota structure. It reduced the number of harmful bacteria, such as Clostridium spp. that is positively correlated with both urea and IS, but augmented the number of beneficial bacteria, including Lactobacillus spp. that is negatively correlated with urea. Thus, changes in gut microbiota induced by emodin via colonic irrigation are closely associated with reduction in uremic toxins and mitigation of renal injury.