Objective Impaired endothelial cell autophagy compromises shear-stress induced nitric oxide (NO) generation. We determined the responsible mechanism. Approach and Results Upon autophagy compromise in bovine aortic endothelial cells (ECs) exposed to shear-stress, a decrease in glucose uptake and EC glycolysis attenuated ATP production. We hypothesized that decreased glycolysis-dependent purinergic signaling via P2Y-1 receptors, secondary to impaired autophagy in ECs, prevents shear-induced p-eNOSS1177 and NO generation. Maneuvers that restore glucose transport and glycolysis (e.g., overexpression of GLUT1) or purinergic signaling (e.g., addition of exogenous ADP) rescue shear-induced p-eNOSS1177 and NO production in ECs with impaired autophagy. Conversely, inhibiting glucose-transport via GLUT1 siRNA, blocking purinergic signaling via ectonucleotidase-mediated ATP/ADP degradation (e.g., apyrase), or inhibiting P2Y1 receptors using pharmacological (e.g., MRS2179) or genetic (e.g., P2Y1-R siRNA) procedures, inhibits shear-induced p-eNOSS1177 and NO generation in ECs with intact autophagy. Supporting a central role for PKCδT505 in relaying the autophagy-dependent purinergic-mediated signal to eNOS, we find that: (i) shear-stress induced activating phosphorylation of PKCδT505 is negated by inhibiting autophagy; (ii) shear-induced p-eNOSS1177 and NO generation are restored in autophagy-impaired ECs via pharmacological (e.g., bryostatin) or genetic (e.g., CA-PKCδ) activation of PKCδT505 and (iii) pharmacological (e.g., rottlerin) and genetic (e.g., PKCδ siRNA) PKCδ inhibition prevents shear-induced p-eNOSS1177 and NO generation in ECs with intact autophagy. Key nodes of dysregulation in this pathway upon autophagy compromise were revealed in human arterial endothelial cells. Conclusion Targeted reactivation of purinergic signaling and/or PKCδ has strategic potential to restore compromised NO generation in pathologies associated with suppressed EC autophagy.

Varicella zoster virus (VZV) is a human-restricted α-herpesvirus that exhibits tropism for the skin. The VZV host receptors and downstream signaling pathways responsible for the antiviral innate immune response in the skin are not completely understood. Here, we show that STING mediates an important host defense against VZV infection in dermal cells including human dermal fibroblasts and HaCaT keratinocytes. Inhibition of STING using small interfering-RNA or short hairpin RNA-mediated gene disruption resulted in enhanced viral replication but diminished IRF3 phosphorylation and induction of IFNs and proinflammatory cytokines. Pretreatment with STING agonists resulted in reduced VZV glycoprotein E expression and viral replication. Additionally, using RNA sequencing to analyze dual host and VZV transcriptomes, we identified several host immune genes significantly induced by VZV infection. Furthermore, significant up-regulation of IFN-λ secretion was observed after VZV infection, partly through a STING-dependent pathway; IFN-λ was shown to be crucial for antiviral defense against VZV in human dermal cells. In conclusion, our data provide an important insight into STING-mediated induction of type I and III IFNs and subsequent antiviral signaling pathways that regulate VZV replication in human dermal cells.

PURPOSE: The purpose of this study was to develop and compare 3 predictive models for pressure injury (PI) occurrence in surgical patients. DESIGN: Retrospective case-control study. SUBJECTS AND SETTING: Data on PI risk assessment and preanesthesia evaluation records from 400 patients (80 patients who developed PIs after surgery and 320 patients who did not) in a South Korean acute care setting who underwent surgery between January 2015 and May 2016 were extracted from the electronic health record. METHODS: Three models were developed with items from the Braden Scale (model 1), the Scott Triggers tool (model 2), and the Scott Triggers tool in addition to type of anesthesia, laboratory test results, and comorbid conditions (model 3) using logistic regression to analyze items (factors) in each model. Predictive performance indices, which included sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristics curve, and Akaike information criterion, were compared among the 3 models. RESULTS: Findings showed there were no significant factors in model 1, the estimated surgery time and serum albumin level were significant in model 2, and the estimated surgery time, serum albumin level, and brain disease were significant in model 3. The model performance evaluation revealed that model 2 was the best fitting model, demonstrating the highest sensitivity (84.4%), highest negative predictive value (94.6%), and lowest Akaike information criterion (302.03). CONCLUSIONS: The Scott Triggers tool in model 2, which consists of simple items that are easy to extract from preanesthesia evaluation records, was the best fitting model. We recommend the Scott Triggers tool be used to predict the development of PIs in surgical patients in acute care settings.

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.