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Berlin and, Germany

Fasel D.,University of Basel | Mellmann A.,University of Munster | Cernela N.,University of Zurich | Hachler H.,University of Zurich | And 7 more authors.
Journal of Clinical Microbiology | Year: 2014

We report on a 65-year-old male patient with a Shiga toxin-producing Escherichia coli O51:H49 gastrointestinal infection and sepsis associated with hemolytic uremic syndrome (HUS) with a fatal outcome. The strains isolated harbored stx2e and eae, a very unusual and new virulence profile for an HUS-associated enterohemorrhagic E. coli. Copyright © 2014, American Society for Microbiology. All Rights Reserved. Source


Cnops L.,Institute of Tropical Medicine | Domingo C.,Robert Koch Institute RKI | Van den Bossche D.,Institute of Tropical Medicine | Vekens E.,Medisch Labo Medina | And 2 more authors.
Journal of Clinical Virology | Year: 2014

We report a dengue virus (DENV) co-infection in a Belgian traveler after a three-weeks holiday to Thailand. The patient recovered well without any complication. The infection was diagnosed by NS1 antigen testing and the concurrent presence of serotype DENV1 and DENV2 was demonstrated by reverse transcriptase polymerase chain reaction (RT-PCR) in acute phase serum sampled three days after symptoms onset. The predominant DENV1 serotype was identified as genotype I, lineage Asia-3 by sequencing. To our knowledge, this is the first time that a dengue co-infection is reported in a European traveler. The co-infection accounts for 1.0% of the total number of RT-PCR-positive samples (n=105) diagnosed in the reference laboratory of Belgium between 2008 and 2013. We expect that the number of reports on acute co-infections will increase in the coming years considering the increasing number of regions that are progressively becoming hyperendemic, especially in Southeast Asia. © 2014 Elsevier B.V. Source


Lasch P.,Robert Koch Institute RKI
Chemometrics and Intelligent Laboratory Systems | Year: 2012

Recent years have seen substantial progress toward the application of infrared (IR) and Raman spectroscopy as useful analytical tools in biomedical research. Vibrational spectroscopy, and (in particular) microspectroscopy, have been successfully applied to biomedical samples ranging from intact microorganisms, eukaryotic cells, body fluids, and tissues. The progress in the field was driven not only by technical developments but also by the effective implementation of modern concepts of spectral analysis.Pre-processing has been identified as an indispensable part of spectral data analysis. It involves, among others, outlier rejection, normalization, filtering, detrending, transformation, folding and feature selection. Goals of spectral pre-processing include better interpretability of the spectra, higher robustness and improved accuracy of subsequent quantitative or classification analysis. The aim of this review article is to explore the concepts and techniques of a variety of individual pre-processing methods and to discuss the applicability of different pre-processing techniques in the context of practical applications of biomedical IR or Raman spectroscopy. It is hoped that this article not only represents a useful guideline for beginners in the field of biomedical applications of vibrational spectroscopy, but serves also as a source of reference for more experienced spectroscopists. © 2012 Elsevier B.V. Source


Kilpelainen K.,Finnish National Institute for Health and Welfare | Tuomi-Nikula A.,Finnish National Institute for Health and Welfare | Thelen J.,Robert Koch Institute RKI | Gissler M.,Finnish National Institute for Health and Welfare | And 3 more authors.
European Journal of Public Health | Year: 2012

Background: The European Union (EU) lacks adequate capacity for public health monitoring. The creation of a stable European Health Information System would help Member States to carry out evidence-based health policy. Such a system would also benefit EU health priorities by providing European wide comparable information. This study is the first comprehensive assessment of the availability of general health data in Europe. Methods: The main aim was to assess the availability of the European Community Health Indicators (ECHI) in each EU Member State. This was done by means of a review of international health databases, an online survey and face-to-face discussions with experts in 31 European countries. Results: The European average availability score for all ECHI indicators was 74 ranging from 56 to 84. In most countries, about half of the ECHI indicators can be derived from routinely collected health information. This is true for demographic information, mortality and hospital discharge-based morbidity. However, many important ECHI indicators are lacking in most European countries. These include population representative data for health determinants, the provision and use of health care services, injuries, the quality of health care and health promotion. Conclusion: Valid health information is essential for improving people's health across Europe. There is an urgent need to develop harmonized methods for gathering and disseminating representative health data. These methods should be developed jointly by DG Health and Consumers, Eurostat and EU Member States. © 2011 The Author. Source


Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center | Lasch P.,Robert Koch Institute RKI
Analytical Chemistry | Year: 2013

Over the past decade, confocal Raman microspectroscopic (CRM) imaging has matured into a useful analytical tool to obtain spatially resolved chemical information on the molecular composition of biological samples and has found its way into histopathology, cytology, and microbiology. A CRM imaging data set is a hyperspectral image in which Raman intensities are represented as a function of three coordinates: a spectral coordinate λ encoding the wavelength and two spatial coordinates x and y. Understanding CRM imaging data is challenging because of its complexity, size, and moderate signal-to-noise ratio. Spatial segmentation of CRM imaging data is a way to reveal regions of interest and is traditionally performed using nonsupervised clustering which relies on spectral domain-only information with the main drawback being the high sensitivity to noise. We present a new pipeline for spatial segmentation of CRM imaging data which combines preprocessing in the spectral and spatial domains with k-means clustering. Its core is the preprocessing routine in the spatial domain, edge-preserving denoising (EPD), which exploits the spatial relationships between Raman intensities acquired at neighboring pixels. Additionally, we propose to use both spatial correlation to identify Raman spectral features colocalized with defined spatial regions and confidence maps to assess the quality of spatial segmentation. For CRM data acquired from midsagittal Syrian hamster (Mesocricetus auratus) brain cryosections, we show how our pipeline benefits from the complex spatial-spectral relationships inherent in the CRM imaging data. EPD significantly improves the quality of spatial segmentation that allows us to extract the underlying structural and compositional information contained in the Raman microspectra. © 2013 American Chemical Society. Source

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