Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens

<p>Abstract</p> <p>Background</p> <p>The Enteropathogen Resource Integration Center (ERIC; <url>http://www.ericbrc.org</url>) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as <it>Escherichia coli </it>and <it>Salmonella </it>spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.</p> <p>Description</p> <p>We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.</p> <p>Conclusion</p> <p>Our Text Mining application is available online on the ERIC website <url>http://www.ericbrc.org/portal/eric/articles</url>. The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.</p

Study of histopathological patterns of endometrium in abnormal uterine bleeding

Background: Abnormal uterine bleeding (AUB) is a common gynecological complaint associated with considerable morbidity and significantly affects the patient′s family, personal and social life. The aim of the study was to analyze the histomorphological patterns of endometrium in patients presenting with AUB and also to determine the incidence of AUB in various age groups. Materials and Methods: This is a prospective study, conducted in the Department of Pathology, in a tertiary care teaching hospital, Mangalore from October 2011 till date. All cases of AUB with a probable endometrial cause were included in the study. Data was entered in Microsoft Excel and managed in Statistical Package for the Social Sciences (SPSS) version 16. Analysis was done in the form of percentages and proportions and represented as tables where necessary. Results: A total of 156 cases were analyzed. Patients′ age ranged from 23-78 years. AUB was most prevalent in the perimenopausal age group. The most common presenting complaint was menorrhagia (47%). Endometrial hyperplasia was the most common histopathological finding and was seen in 25% patients, followed by secretory endometrium in 16.7% patients, and proliferative phase pattern and disordered proliferative endometrium were seen in 12.2% patients each. Malignancy was detected in 6.4% of cases and endometrial carcinoma was the most common lesion (4.5%). Conclusions: Histopathological evaluation of endometrial samples is especially indicated in women over the age of 35 years to rule out malignancy and preneoplasia. Among the patients with no organic pathology, normal physiological patterns with proliferative, secretory, and menstrual changes were observed. The most common endometrial pathology in this series was endometrial hyperplasia

on biodefense-relevant enterobacteria

(ERIC): bioinformatics support for researc

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)