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dc.contributor.authorFondrie, W.E.
dc.contributor.authorLiang, T.
dc.contributor.authorOyler, B.L.
dc.date.accessioned2019-05-17T13:21:17Z
dc.date.available2019-05-17T13:21:17Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85055613456&doi=10.1038%2fs41598-018-33681-8&partnerID=40&md5=3b6e7a1639f04f128e61af2755851ba3
dc.identifier.urihttp://hdl.handle.net/10713/9196
dc.description.abstractWith the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications. Copyright 2018, The Author(s).en_US
dc.description.urihttps://dx.doi.org/10.1038/s41598-018-33681-8en_US
dc.language.isoen_USen_US
dc.publisherNature Publishing Groupen_US
dc.relation.ispartofScientific Reports
dc.subjectpathogen identificationen_US
dc.subject.meshCoinfectionen_US
dc.subject.meshDrug Resistance, Bacterialen_US
dc.subject.meshGlycolipidsen_US
dc.subject.meshMachine Learningen_US
dc.subject.meshMass Spectrometry, Matrix-Assisted Laser Desorption-Ionizationen_US
dc.titlePathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipidsen_US
dc.typeArticleen_US
dc.identifier.doi10.1038/s41598-018-33681-8
dc.identifier.pmid30367087


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