| Literature DB >> 27225554 |
Konstantinos Mitsakakis1, Fabian Stumpf1, Oliver Strohmeier1, Vanessa Klein1, Daniel Mark1, Felix Von Stetten1, Johannes R Peham2, Christopher Herz2, Pune Nina Tawakoli3, Florian Wegehaupt3, Thomas Attin3, Nagihan Bostanci4, Kai Bao4, Georgios N Belibasakis4, John P Hays5, Gijs Elshout5, Robin C Huisman5, Stephanie Klein5, Andrew P Stubbs5, Lutz Doms6, Andreas Wolf6, Viorel Rusu7, Sven Goethel7, Thomas Binsl8, Alex Michie8, Jana Jancovicova9, Vladimir Kolar9, Michal Kostka9, Jiri Smutny9, Michal Karpisek9, Caroline Estephan10, Camille Cocaud10, Roland Zengerle1.
Abstract
Global healthcare systems are struggling with the enormous burden associated with infectious diseases, as well as the incessant rise of antimicrobial resistance. In order to adequately address these issues, there is an urgent need for rapid and accurate infectious disease diagnostics. The H2020 project DIAGORAS aims at diagnosing oral and respiratory tract infections using a fully integrated, automated and user-friendly platform for physicians' offices, schools, elderly care units, community settings, etc. Oral diseases (periodontitis, dental caries) will be detected via multiplexed, quantitative analysis of salivary markers (bacterial DNA and host response proteins) for early prevention and personalised monitoring. Respiratory Tract Infections will be diagnosed by means of DNA/RNA differentiation so as to identify their bacterial or viral nature. Together with antibiotic resistance screening on the same platform, a more efficient treatment management is expected at the point-of-care. At the heart of DIAGORAS lies a centrifugal microfluidic platform (LabDisk and associated processing device) integrating all components and assays for a fully automated analysis. The project involves an interface with a clinical algorithm for the comprehensive presentation of results to end-users, thereby increasing the platform's clinical utility. DIAGORAS' performance will be validated at clinical settings and compared with gold standards.Entities:
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Year: 2016 PMID: 27225554
Source DB: PubMed Journal: Stud Health Technol Inform ISSN: 0926-9630