Glucagon like peptide-1 (GLP-1) stimulates glucose-dependent insulin secretion. Dipeptidyl peptidase-4 (DPP-4) inhibitors, which block inactivation of GLP-1, are currently in clinical use for type 2 diabetes mellitus. Recently, GLP-1 has also been reported to have neuroprotective effects in cases of cerebral ischemia. We therefore investigated the neuroprotective effects of GLP-1 receptor (GLP-1R) agonist, exendin-4 (ex-4), after cerebral ischemia-reperfusion injury. Transient middle cerebral artery occlusion (tMCAO) was induced in rats by intracerebroventricular (i.c.v.) administration of ex-4 or ex9-39. Oxygen-glucose deprivation was also induced in primary neurons, bEnd.3 cells, and BV-2. Ischemia-reperfusion injury reduced expression of GLP-1R. Additionally, higher oxidative stress in SOD2 KO mice decreased expression of GLP-1R. Downregulation of GLP-1R by ischemic injury was 70% restored by GLP-1R agonist, ex-4, which resulted in significant reduction of infarct volume. Levels of intracellular cyclic AMP, a second messenger of GLP-1R, were also increased by 2.7-fold as a result of high GLP-1R expression. Moreover, our results showed that ex-4 attenuated pro-inflammatory cyclooxygenase-2 (COX-2) and prostaglandin E2 after MCAO. C-Jun NH2 terminal kinase (JNK) signaling, which stimulates activation of COX-2, was 36% inhibited by i.c.v. injection of ex-4 at 24 h. Islet-brain 1 (IB1), a scaffold regulator of JNK, was 1.7-fold increased by ex-4. GLP-1R activation by ex-4 resulted in reduction of COX-2 through increasing IB1 expression, resulting in anti-inflammatory neuroprotection during stroke. Our study suggests that the anti-inflammatory action of GLP-1 could be used as a new strategy for the treatment of neuroinflammation after stroke accompanied by hyperglycemia.

Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Recently, much attention has been directed toward the study of Sc-doped zirconia electrolyte for intermediate-temperature SOFCs ͑IT-SOFCs͒ due to its fairly good ionic conductivity compared with conventional Y-doped zirconia, which can reduce the internal ohmic loss of the cell. For improving unit cell performance, another important point to be considered is the selection of appropriate electrode materials to reduce the polarization loss at the electrode. In this study, we fabricated anode-supported SOFCs with a 1 mol % CeO 2 codoped 10 mol % Sc 2 O 3 -ZrO 2 ͑CeSSZ͒ electrolyte. Various kinds of cathode materials such as La-Sr-Mn-O, La-Sr-Co-O ͑LSCo͒, La-Sr-Co-Fe-O, and Sm-Sr-Co-O ͑SSCo͒ have been applied in order to identify the most suitable cathode material for Sc-doped zirconia. We evaluated the power-generating characteristics of 5 ϫ 5 cm scaled unit cells as well as the cathode polarization effect via a dc current-voltage measurement, a dc current interruption method, and ac impedance spectroscopy. We also thoroughly investigated the interfacial reaction between the electrolyte and the cathode in order to identify appropriate heat-treatment conditions for each candidate cathode material on the CeSSZ electrolyte. According to the investigation, unit cells with a SSCo and LSCo cathode showed superior power density of 1.13 and 1.33 W/cm 2 , respectively, at 700°C and fairly stable cell performance without any serious interfacial reaction.

ObjectivesWe reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.MethodsWe reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.ResultsWe identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.ConclusionsDigital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.