Service evaluation to establish the sensitivity, specificity and additional value of broad-range 16S rDNA PCR for the diagnosis of infective endocarditis from resected endocardial material in patients from eight UK and Ireland hospitals

Infective endocarditis (IE) can be diagnosed in the clinical microbiology laboratory by culturing explanted heart valve material. We present a service evaluation that examines the sensitivity and specificity of a broad-range 16S rDNA polymerase chain reaction (PCR) assay for the detection of the causative microbe in culture-proven and culture-negative cases of IE. A clinical case-note review was performed for 151 patients, from eight UK and Ireland hospitals, whose endocardial specimens were referred to the Microbiology Laboratory at Great Ormond Street Hospital (GOSH) for broad-range 16S rDNA PCR over a 12-year period. PCR detects the causative microbe in 35/47 cases of culture-proven IE and provides an aetiological agent in 43/69 cases of culture-negative IE. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the 16S rDNA PCR assay were calculated for this series of selected samples using the clinical diagnosis of IE as the reference standard. The values obtained are as follows: sensitivity = 67 %, specificity = 91 %, PPV = 96 % and NPV = 46 %. A wide range of organisms are detected by PCR, with Streptococcus spp. detected most frequently and a relatively large number of cases of Bartonella spp. and Tropheryma whipplei IE. PCR testing of explanted heart valves is recommended in addition to culture techniques to increase diagnostic yield. The data describing the aetiological agents in a large UK and Ireland series of culture-negative IE will allow future development of the diagnostic algorithm to include real-time PCR assays targeted at specific organisms

GenoMetric Query Language: A novel approach to large-scale genomic data management

Motivation: Improvement of sequencing technologies and data processing pipelines is rapidly providing sequencing data, with associated high-level features, of many individual genomes in multiple biological and clinical conditions. They allow for data-driven genomic, transcriptomic and epigenomic characterizations, but require state-of-the-art ‘big data’ computing strategies, with abstraction levels beyond available tool capabilities. Results: We propose a high-level, declarative GenoMetric Query Language (GMQL) and a toolkit for its use. GMQL operates downstream of raw data preprocessing pipelines and supports queries over thousands of heterogeneous datasets and samples; as such it is key to genomic ‘big data’ analysis. GMQL leverages a simple data model that provides both abstractions of genomic region data and associated experimental, biological and clinical metadata and interoperability between many data formats. Based on Hadoop framework and Apache Pig platform, GMQL ensures high scalability, expressivity, flexibility and simplicity of use, as demonstrated by several biological query examples on ENCODE and TCGA datasets. Availability and implementation: The GMQL toolkit is freely available for non-commercial use at http://www.bioinformatics.deib.polimi.it/GMQL/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

The cytotoxic and migrastatic potentials of Allium Jesdianum hydroalcoholic extract on glioblastoma multiforme cell line model

OBJECTIVE: Glioblastoma multiforme is one of the most malignant types of central nervous system tumors and temozolomide (TMZ) is currently used as a standard treatment for this type of cancer. However, resistance to temozolomide is a problem in the successful treatment. Plants and herbs are potential sources of cancer therapeutics. This study aimed at evaluating the effect of Allium Jesdianum (AJ) hydroalcoholic extract on glioblastoma multiforme cells. MATERIALS AND METHODS: The plant material was purchased and extracted. The cell line was treated with extract for 24, 48, and 72 hr. Cell viability was assessed by trypan blue staining, MTT assay, and lactate dehydrogenase activity measurement. Tumor invasion potential was evaluated by cell migration, invasion, and adhesion tests. Real-time PCR was used to assess the changes in the expression pattern of genes involved in cancer invasion. RESULTS: Extract treatment caused a concentration- and time-dependent decrease in cell survival. Also, a decrease in cell migration, invasion and adhesion potential and the expression of metalloproteinases 2 and 9 in cells was observed after treatment. CONCLUSIONS: Allium Jesdianum showed promising anti-cancer activity in glioblastoma multiforme cells

Mechanism Underlying Defective Interferon Gamma-Induced IDO Expression in Non-obese Diabetic Mouse Fibroblasts

Indoleamine 2,3-dioxygenase (IDO) can locally suppress T cell-mediated immune responses. It has been shown that defective self-tolerance in early prediabetic female non-obese diabetic (NOD) mice can be attributed to the impaired interferon-gamma (IFN-γ)- induced IDO expression in dendritic cells of these animals. As IFN-γ can induce IDO in both dendritic cells and fibroblasts, we asked the question of whether there exists a similar defect in IFN-γ-induced IDO expression in NOD mice dermal fibroblasts. To this end, we examined the effect of IFN-γ on expression of IDO and its enzymatic activity in NOD dermal fibroblasts. The results showed that fibroblasts from either prediabetic (8 wks of age) female or male, and diabetic female or male (12 and 24 wks of age respectively) NOD mice failed to express IDO in response to IFN-γ treatment. To find underlying mechanisms, we scrutinized the IFN- γ signaling pathway and investigated expression of other IFN-γ-modulated factors including major histocompatibility complex class I (MHC-I) and type I collagen (COL-I). The findings revealed a defect of signal transducer and activator of transcription 1 (STAT1) phosphorylation in NOD cells relative to that of controls. Furthermore, we found an increase in MHC-I and suppression of COL-I expression in fibroblasts from both NOD and control mice following IFN-γ treatment; indicating that the impaired response to IFN-γ in NOD fibroblasts is specific to IDO gene. Finally, we showed that an IFN-γ-independent IDO expression pathway i.e. lipopolysaccharide (LPS)-mediated-c-Jun kinase is operative in NOD mice fibroblast. In conclusion, the findings of this study for the first time indicate that IFN-γ fails to induce IDO expression in NOD dermal fibroblasts; this may partially be due to defective STAT1 phosphorylation in IFN-γ-induced-IDO signaling pathway

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies

Characterization of aluminum, aluminum oxide and titanium dioxide nanomaterials using a combination of methods for particle surface and size analysis

International audienceThe application of appropriate analytical techniques is essential for nanomaterial (NM) characterization. In this study, we compared different analytical techniques for NM analysis. Regarding possible adverse health effects, ionic and particulate NM effects have to be taken into account. As NMs behave quite differently in physiological media, special attention was paid to techniques which are able to determine the biosolubility and complexation behavior of NMs. Representative NMs of similar size were selected: aluminum (Al 0) and aluminum oxide (Al 2 O 3), to compare the behavior of metal and metal oxides. In addition, titanium dioxide (TiO 2) was investigated. Characterization techniques such as dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) were evaluated with respect to their suitability for fast characterization of nanoparticle dispersions regarding a particle's hydrodynamic diameter and size distribution. By application of inductively coupled plasma mass spectrometry in the single particle mode (SP-ICP-MS), individual nanoparticles were quantified and characterized regarding their size. SP-ICP-MS measurements were correlated with the information gained using other characterization techniques, i.e. transmission electron microscopy (TEM) and small angle X-ray scattering (SAXS). The particle surface as an important descriptor of NMs was analyzed by X-ray diffraction (XRD). NM impurities and their co-localization with biomolecules were determined by ion beam microscopy (IBM) and confocal Raman microscopy (CRM). We conclude advantages and disadvantages of the different techniques applied and suggest options for their complementation. Thus, this paper may serve as a practical guide to particle characterization techniques

Promoting menstrual health among persian adolescent girls from low socioeconomic backgrounds: a quasi-experimental study

<p>Abstract</p> <p>Background</p> <p>Research in the past decade has revealed average to poor menstrual health among many Iranian girls. The present study investigated the effectiveness of a health promotion project on improving menstrual health in adolescent girls in Iran.</p> <p>Methods</p> <p>A quasi-experimental study was conducted to evaluate the effectiveness of the health intervention program. A total of 698 students (study participants and controls) in several schools in Mazandaran province, Iran were included. The project comprised 10 two-hour educational sessions. Educational topics included the significance of adolescence, physical and emotional changes during adolescence, pubertal and menstruation health and premenstrual syndrome. A self-administered questionnaire measuring demographic characteristics, behaviors during menstruation, menstrual patterns, sources of information about menstruation and personal health data was administered. The questionnaire was administered to all participating students after the experimental group received the training.</p> <p>Results</p> <p>Among the most significant results was the impact of educational sessions on bathing and genital hygiene. A total of 61.6% in the experimental group compared with 49.3% in the control group engaged in usual bathing during menstruation (p = 0.002). Individual health status was significantly statistically correlated with menstrual health. Attitude towards menstruation was also significantly related to menstrual health.</p> <p>Conclusions</p> <p>The present study confirms that educational interventions, such as the health promotion project in this study, can be quite effective in promoting menstrual health.</p

Explorative visual analytics on interval-based genomic data and their metadata

Background: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. Results: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. Conclusions: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSEunder GPLv3 open-source license