148 research outputs found

    NGAL (Lcn2) monomer is associated with tubulointerstitial damage in chronic kidney disease

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    The type and the extent of tissue damage inform the prognosis of chronic kidney disease (CKD), but kidney biopsy is not a routine test. Urinary tests that correlate with specific histological findings might serve as surrogates for the kidney biopsy. We used immunoblots and ARCHITECT-NGAL assays to define the immunoreactivity of urinary neutrophil gelatinase–associated lipocalin (NGAL) in CKD, and we used mass spectroscopy to identify associated proteins. We analyzed kidney biopsies to determine whether specific pathological characteristics associated with the monomeric NGAL species. Advanced CKD urine contained the NGAL monomer as well as novel complexes of NGAL. When these species were separated, we found a significant correlation between the NGAL monomer and glomerular filtration rate (r=-0.53, P<0.001), interstitial fibrosis (mild vs. severe disease; mean 54 vs. 167μg uNGAL/g Cr, P<0.01), and tubular atrophy (mild vs. severe disease; mean 54 vs. 164μg uNGAL/g Cr, P<0.01). Monospecific assays of the NGAL monomer demonstrated a correlation with histology that typifies progressive, severe CKD

    Oral Microbiome Profiles: 16S rRNA Pyrosequencing and Microarray Assay Comparison

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    The human oral microbiome is potentially related to diverse health conditions and high-throughput technology provides the possibility of surveying microbial community structure at high resolution. We compared two oral microbiome survey methods: broad-based microbiome identification by 16S rRNA gene sequencing and targeted characterization of microbes by custom DNA microarray.Oral wash samples were collected from 20 individuals at Memorial Sloan-Kettering Cancer Center. 16S rRNA gene survey was performed by 454 pyrosequencing of the V3–V5 region (450 bp). Targeted identification by DNA microarray was carried out with the Human Oral Microbe Identification Microarray (HOMIM). Correlations and relative abundance were compared at phylum and genus level, between 16S rRNA sequence read ratio and HOMIM hybridization intensity.; Correlation = 0.70–0.84).Microbiome community profiles assessed by 16S rRNA pyrosequencing and HOMIM were highly correlated at the phylum level and, when comparing the more commonly detected taxa, also at the genus level. Both methods are currently suitable for high-throughput epidemiologic investigations relating identified and more common oral microbial taxa to disease risk; yet, pyrosequencing may provide a broader spectrum of taxa identification, a distinct sequence-read record, and greater detection sensitivity

    ViralORFeome: an integrated database to generate a versatile collection of viral ORFs

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    Large collections of protein-encoding open reading frames (ORFs) established in a versatile recombination-based cloning system have been instrumental to study protein functions in high-throughput assays. Such ‘ORFeome’ resources have been developed for several organisms but in virology, plasmid collections covering a significant fraction of the virosphere are still needed. In this perspective, we present ViralORFeome 1.0 (http://www.viralorfeome.com), an open-access database and management system that provides an integrated set of bioinformatic tools to clone viral ORFs in the Gateway® system. ViralORFeome provides a convenient interface to navigate through virus genome sequences, to design ORF-specific cloning primers, to validate the sequence of generated constructs and to browse established collections of virus ORFs. Most importantly, ViralORFeome has been designed to manage all possible variants or mutants of a given ORF so that the cloning procedure can be applied to any emerging virus strain. A subset of plasmid constructs generated with ViralORFeome platform has been tested with success for heterologous protein expression in different expression systems at proteome scale. ViralORFeome should provide our community with a framework to establish a large collection of virus ORF clones, an instrumental resource to determine functions, activities and binding partners of viral proteins

    The Art of Research: A Divergent/Convergent Framework and Opportunities for Science-Based Approaches

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    Applying science to the current art of producing engineering and research knowledge has proven difficult, in large part because of its seeming complexity. We posit that the microscopic processes underlying research are not so complex, but instead are iterative and interacting cycles of divergent (generation of ideas) and convergent (testing and selecting of ideas) thinking processes. This reductionist framework coherently organizes a wide range of previously disparate microscopic mechanisms which inhibit these processes. We give examples of such inhibitory mechanisms and discuss how deeper scientific understanding of these mechanisms might lead to dis-inhibitory interventions for individuals, networks and institutional levels

    Temporal Analysis of the Honey Bee Microbiome Reveals Four Novel Viruses and Seasonal Prevalence of Known Viruses, Nosema, and Crithidia

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    Honey bees (Apis mellifera) play a critical role in global food production as pollinators of numerous crops. Recently, honey bee populations in the United States, Canada, and Europe have suffered an unexplained increase in annual losses due to a phenomenon known as Colony Collapse Disorder (CCD). Epidemiological analysis of CCD is confounded by a relative dearth of bee pathogen field studies. To identify what constitutes an abnormal pathophysiological condition in a honey bee colony, it is critical to have characterized the spectrum of exogenous infectious agents in healthy hives over time. We conducted a prospective study of a large scale migratory bee keeping operation using high-frequency sampling paired with comprehensive molecular detection methods, including a custom microarray, qPCR, and ultra deep sequencing. We established seasonal incidence and abundance of known viruses, Nosema sp., Crithidia mellificae, and bacteria. Ultra deep sequence analysis further identified four novel RNA viruses, two of which were the most abundant observed components of the honey bee microbiome (∼1011 viruses per honey bee). Our results demonstrate episodic viral incidence and distinct pathogen patterns between summer and winter time-points. Peak infection of common honey bee viruses and Nosema occurred in the summer, whereas levels of the trypanosomatid Crithidia mellificae and Lake Sinai virus 2, a novel virus, peaked in January

    Application of Surface wave methods for seismic site characterization

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    Surface-wave dispersion analysis is widely used in geophysics to infer a shear wave velocity model of the subsoil for a wide variety of applications. A shear-wave velocity model is obtained from the solution of an inverse problem based on the surface wave dispersive propagation in vertically heterogeneous media. The analysis can be based either on active source measurements or on seismic noise recordings. This paper discusses the most typical choices for collection and interpretation of experimental data, providing a state of the art on the different steps involved in surface wave surveys. In particular, the different strategies for processing experimental data and to solve the inverse problem are presented, along with their advantages and disadvantages. Also, some issues related to the characteristics of passive surface wave data and their use in H/V spectral ratio technique are discussed as additional information to be used independently or in conjunction with dispersion analysis. Finally, some recommendations for the use of surface wave methods are presented, while also outlining future trends in the research of this topic

    Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration

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    High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well. © 2013 McShane et al.; licensee BioMed Central Ltd
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