Identification, purification and characterization of a receptor for dengue virus-induced macrophage cytotoxin (CF2) from murine T cells

Dengue type-2 virus infection in mice induces a subpopulation of T lymphocytes to produce a cytokine cytotoxic factor, which induces macrophages (MΦ ) to produce a biologically active cytotoxic cytokine, the MΦ cytotoxin (CF2). Previously we have identified the presence of intermediate-affinity receptors for CF2 on mouse peritoneal MΦ. The present study was undertaken to identify the CF2-receptors (CF2-R) on murine T cells followed by their purification and characterization. Receptor binding assay and Scatchard analysis revealed single, high-affinity (1.0309 nM) receptors for CF2 on T cells (22,000 receptors per cell). The binding of [125I]CF2 on murine T cells was saturable and specific. Furthermore, CF2-R was purified from normal mouse T cell plasma membrane by affinity chromatography followed by reversed-phase high-pressure liquid chromatography. The presence of CF2-R was confirmed by indirect dot-blot assay and its binding with [125I]CF2. The purified CF2-R is a 90-95-kDa protein as characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and immunoblot analysis. The chemical crosslinking of [125I]CF2 and its receptor complex showed a product of 100-110 kDa on different subpopulations of murine T cells. The pretreatment of target cells with anti-CF2-R antisera inhibited the cytotoxic activity of CF2 in a dose-dependent manner and thus confirmed the biological significance of CF2-R. Moreover, the presence of CF2-R was also identified on normal human peripheral blood mononuclear cells and T and B cells by crosslinking with [125I]CF2, thus revealing the possible role of CF2 and CF2-R in the immunopathogenesis of dengue virus disease

Standardized intermediates: Synthesis of model bacterial O-antigens. A regio-/stereoselective synthesis of 1,2 -trans disaccharides as allyl glycosides suitable for chain extension

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Paraurethral leomyoma: a manageable challenge

Leiomyoma is a most common benign tumour of uterus. But it is very rare in vagina urethral and paraurethral. There are approximately 330 case reports are available in literature and the paraurethral site is extremely uncommon. Here, we report a case of 38-year nulliparous woman presented with complain of mass coming out of vagina as well as dyspareunia. Provisional diagnosis of anterior vaginal cyst along with the differential diagnosis of paraurethral and anterior wall vaginal leiomyoma was made. Transvaginal removal of mass was done and diagnosis of paraurethral leiomyoma was confirmed by histopathology

Bridging Encounter Forms and Electronic Medical Record Databases: Annotation, Mapping, and Integration

Abstract-Forms are a major source of input for getting data into the underlying medical databases of electronic health/medical record (EHR/EMR) systems. Standardizing encounter forms and integrating data collected from different forms into a single database would greatly reduce heterogeneity. In this paper, we describe a framework, the fEHR-plus system, that annotates, maps, and integrates user-specified encounter forms into a single database. The development of the framework incorporates machine learning, standard medical terminology, and the principles of database design. We conduct an empirical study with 52 forms collected from 6 medical institutions for evaluating the performance of the fEHR-plus system. The overall results show that the system is promising towards improving interoperability among electronic health record systems

SAMSTARplus: An Automatic Tool for Generating Multi- Dimensional Schemas from an Entity-Relationship Diagram

This paper presents a tool that automatically generates multidimensional schemas for data warehouses from OLTP entity-relationship diagrams (ERDs). Based on user’s input parameters, it generates star schemas, snowflake schemas, or a fact constellation schema by taking advantage of only structural information of input ERDs. Hence, SAMSTARplus can help users reduce efforts for designing data warehouses and aids decision making

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

An empirical study on using hidden markov model for search interface segmentation

This paper describes a hidden Markov model (HMM) based approach to perform search interface segmentation. Automatic processing of an interface is a must to access the invisible contents of deep Web. This entails automatic segmentation, i.e., the task of grouping related components of an interface together. While it is easy for a human to discern the logical relationships among interface components, machine processing of an interface is difficult. In this paper, we propose an approach to segmentation that leverages the probabilistic nature of the interface design process. The design process involves choosing components based on the underlying database query requirements, and organizing them into suitable patterns. We simulate this process by creating an “artificial designer ” in the form of a 2-layered HMM. The learned HMM acquires the implicit design knowledge required for segmentation. We empirically study the effectiveness of the approach across several representative domains of deep Web. In terms of segmentation accuracy, the HMM-based approach outperforms an existing state-of-the-art approach by at least 10% in most cases. Furthermore, our cross-domain investigation shows that a single HMM trained on data having varied and frequent design patterns can accurately segment interfaces from multiple domains

LabeledIn: Cataloging labeled indications for human drugs

AbstractDrug–disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed®. Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions; however, their performance is limited by natural language challenges in disease named entity recognition and indication selection.In response, we report LabeledIn: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive LabeledIn from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the pre-computed candidates and deliver the final gold standard.In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8000 drug labels (500 unique) in DailyMed in which 2950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in LabeledIn consist of 7805 drug–disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength.A systematic comparison of LabeledIn with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, LabeledIn is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies on drug nomenclature and medication errors in EMRs, we adopted a fine-grained drug representation scheme, which enables the automatic identification of drugs with indications specific to certain dose forms or strengths. Future work includes expanding our coverage to more drugs and integration with other resources.The LabeledIn dataset and the annotation guidelines are available at http://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/