A Controlled Investigation of Optimal Internal Medicine Ward Team Structure at a Teaching Hospital

BACKGROUND: The optimal structure of an internal medicine ward team at a teaching hospital is unknown. We hypothesized that increasing the ratio of attendings to housestaff would result in an enhanced perceived educational experience for residents. METHODS: Harbor-UCLA Medical Center (HUMC) is a tertiary care, public hospital in Los Angeles County. Standard ward teams at HUMC, with a housestaff∶attending ratio of 5:1, were split by adding one attending and then dividing the teams into two experimental teams containing ratios of 3:1 and 2:1. Web-based Likert satisfaction surveys were completed by housestaff and attending physicians on the experimental and control teams at the end of their rotations, and objective healthcare outcomes (e.g., length of stay, hospital readmission, mortality) were compared. RESULTS: Nine hundred and ninety patients were admitted to the standard control teams and 184 were admitted to the experimental teams (81 to the one-intern team and 103 to the two-intern team). Patients admitted to the experimental and control teams had similar age and disease severity. Residents and attending physicians consistently indicated that the quality of the educational experience, time spent teaching, time devoted to patient care, and quality of life were superior on the experimental teams. Objective healthcare outcomes did not differ between experimental and control teams. CONCLUSIONS: Altering internal medicine ward team structure to reduce the ratio of housestaff to attending physicians improved the perceived educational experience without altering objective healthcare outcomes

Methods for modelling and analysis of bendable photovoltaic modules on irregularly curved surfaces

Most photovoltaic modules are planar and as a result, research on panel layout for photovoltaic systems typically uses planar panels. However, the increased availability of thin-film photovoltaic modules opens up possibilities for the application of flexible solar panels on irregularly curved surfaces, including the integration of photovoltaic panels on building roofs with double curvature. In order to efficiently arrange photovoltaic panels on such surfaces, geometric CAD tools as well as radiation analysis tools are needed. This paper introduces a method to generate geometry for flexible photovoltaic modules on curved surfaces, as well as a method to arrange multiple of such modules on a surface. By automating the generation of possible photovoltaic panel arrangements and linking the geometric tools to solar analysis software, large numbers of design options can be analysed in a relatively short time. This combination of geometry generation and solar analysis provides data that is important for electrical design of photovoltaic systems. The merits of the methods we introduce are illustrated with a case study, for which hundreds of design configurations have been explored in an automated manner. Based on analysis of the numeric data generated for each of the configurations, the effects of panel dimensions and orientation on solar insolation potential and panel curvature have been established. The quantitative and qualitative conclusions resulting from this analysis have informed the design of the photovoltaic system in the case study project.ISSN:2251-6832ISSN:2008-916

FcγRIIb Inhibits Allergic Lung Inflammation in a Murine Model of Allergic Asthma

Allergic asthma is characterized by airway eosinophilia, increased mucin production and allergen-specific IgE. Fc gamma receptor IIb (FcγRIIb), an inhibitory IgG receptor, has recently emerged as a negative regulator of allergic diseases like anaphylaxis and allergic rhinitis. However, no studies to date have evaluated its role in allergic asthma. Our main objective was to study the role of FcγRIIb in allergic lung inflammation. We used a murine model of allergic airway inflammation. Inflammation was quantified by BAL inflammatory cells and airway mucin production. FcγRIIb expression was measured by qPCR and flow cytometry and the cytokines were quantified by ELISA. Compared to wild type animals, FcγRIIb deficient mice mount a vigorous allergic lung inflammation characterized by increased bronchoalveolar lavage fluid cellularity, eosinophilia and mucin content upon ragweed extract (RWE) challenge. RWE challenge in sensitized mice upregulated FcγRIIb in the lungs. Disruption of IFN-γ gene abrogated this upregulation. Treatment of naïve mice with the Th1-inducing agent CpG DNA increased FcγRIIb expression in the lungs. Furthermore, treatment of sensitized mice with CpG DNA prior to RWE challenge induced greater upregulation of FcγRIIb than RWE challenge alone. These observations indicated that RWE challenge upregulated FcγRIIb in the lungs by IFN-γ- and Th1-dependent mechanisms. RWE challenge upregulated FcγRIIb on pulmonary CD14+/MHC II+ mononuclear cells and CD11c+ cells. FcγRIIb deficient mice also exhibited an exaggerated RWE-specific IgE response upon sensitization when compared to wild type mice. We propose that FcγRIIb physiologically regulates allergic airway inflammation by two mechanisms: 1) allergen challenge mediates upregulation of FcγRIIb on pulmonary CD14+/MHC II+ mononuclear cells and CD11c+ cells by an IFN-γ dependent mechanism; and 2) by attenuating the allergen specific IgE response during sensitization. Thus, stimulating FcγRIIb may be a therapeutic strategy in allergic airway disorders

The NOX toolbox: validating the role of NADPH oxidases in physiology and disease

Reactive oxygen species (ROS) are cellular signals but also disease triggers; their relative excess (oxidative stress) or shortage (reductive stress) compared to reducing equivalents are potentially deleterious. This may explain why antioxidants fail to combat diseases that correlate with oxidative stress. Instead, targeting of disease-relevant enzymatic ROS sources that leaves physiological ROS signaling unaffected may be more beneficial. NADPH oxidases are the only known enzyme family with the sole function to produce ROS. Of the catalytic NADPH oxidase subunits (NOX), NOX4 is the most widely distributed isoform. We provide here a critical review of the currently available experimental tools to assess the role of NOX and especially NOX4, i.e. knock-out mice, siRNAs, antibodies, and pharmacological inhibitors. We then focus on the characterization of the small molecule NADPH oxidase inhibitor, VAS2870, in vitro and in vivo, its specificity, selectivity, and possible mechanism of action. Finally, we discuss the validation of NOX4 as a potential therapeutic target for indications including stroke, heart failure, and fibrosis

Malignant Tumors of the Central Nervous System

Malignant tumors of the central nervous system in adults comprise a heterogeneous group of malignancies, the largest subgroups comprising astrocytomas, ependymomas, and oligodendrogliomas. Glioblastomas are the most common tumor type, and they have dismal prognosis. Due to differences in cell type of origin, as well as pathogenesis, it is plausible that their etiology also differs between tumor types. The etiology of malignant CNS tumors is largely unknown and no occupational risk factors have been definitively identified. High doses of ionizing radiation increase the risk, but in occupational settings the dose levels appear too small to result in discernible excesses. Several studies have assessed possible effect of extremely low frequency and radiofrequency electromagnetic fields, but the results are inconsistent. Increased brain tumor risk has been reported in agricultural workers, but no specific exposure has been linked to them. Pesticides have been analyzed in several studies without showing a clear increase in risk.acceptedVersionPeer reviewe

Long Short-Term Memory Recurrent Neural Network for Stroke Prediction

© 2018, Springer International Publishing AG, part of Springer Nature. Electronic Healthcare Records (EHRs) describe the details about a patient’s physical and mental health, diagnosis, lab results, treatments or patient care plan and so forth. Currently, the International Classification of Diseases, 10th Revision or ICD-10 code is used for representing each patient record. The huge amount of information in these records provides insights about the diagnosis and prediction of various diseases. Various data mining techniques are used for the analysis of data deriving from these patient records. Recurrent Neural Network (RNN) is a powerful and widely used technique in machine learning and bioinformatics. This research aims at the investigation of RNN with Long Short-Term Memory (LSTM) hidden units. The empirical research is intended to evaluate the ability of LSTMs to recognize patterns in multi-label classification of cerebrovascular symptoms or stroke. First, we integrated ICD-10 code into health record, as well as other potential risk factors within EHRs into the pattern and model for prediction. Next, we modelled the effectiveness of LSTMs for prediction of stroke based on healthcare records. The results show several strong baselines that include accuracy, recall, and F1 measure score