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dc.contributor.authorvan den Beld, Maaike J C
dc.contributor.authorde Boer, Richard F
dc.contributor.authorReubsaet, Frans A G
dc.contributor.authorRossen, John W A
dc.contributor.authorZhou, Kai
dc.contributor.authorKuiling, Sjoerd
dc.contributor.authorFriedrich, Alexander W
dc.contributor.authorKooistra-Smid, Mirjam A M D
dc.date.accessioned2018-08-02T13:09:00Z
dc.date.available2018-08-02T13:09:00Z
dc.date.issued2018-07-18
dc.identifier.citationEvaluation of a culture dependent algorithm and a molecular algorithm for identification of Shigella spp., Escherichia coli, and enteroinvasive E. coli (EIEC). 2018 J. Clin. Microbiol.en
dc.identifier.issn1098-660X
dc.identifier.pmid30021824
dc.identifier.doi10.1128/JCM.00510-18
dc.identifier.urihttp://hdl.handle.net/10029/622104
dc.description.abstractIdentification of Shigella spp., Escherichia coli and enteroinvasive E. coli is challenging, because of their close relatedness. Distinction is vital, as infections with Shigella spp. are under surveillance of health authorities, in contrast to EIEC infections. In this study, a culture dependent identification algorithm and a molecular identification algorithm were evaluated. Discrepancies between the two algorithms and original identification were assessed using Whole Genome Sequencing (WGS). After discrepancy analysis, with the molecular algorithm, 100% of the evaluated isolates were identified in concordance with original identification. However, the resolution for certain serotypes was lower than previously described methods and lower than the culture dependent algorithm. Although, the resolution of the culture dependent algorithm is high, 100% of non-invasive E. coli, S. sonnei, S. dysenteriae, 93% of S. boydii and EIEC and 85% of S. flexneri were identified in concordance with the original identification. Discrepancy analysis using WGS was able to confirm one of the used algorithms in four discrepant results. However, it failed to clarify three other discrepant results as it added yet another identification. Both proposed algorithms performed well for the identification of Shigella spp and EIEC, and are applicable in low-resource settings in contrast to earlier described methods that require WGS for daily diagnostics. Evaluation of the algorithms showed that both algorithms are capable of identifying Shigella species and EIEC isolates. The molecular algorithm is more applicable in clinical diagnostics for fast and accurate screening, while the culture dependent algorithm is more suitable for reference laboratories to identify Shigella spp. and EIEC up to serotype level.
dc.language.isoenen
dc.rightsArchived with thanks to Journal of clinical microbiologyen
dc.titleEvaluation of a culture dependent algorithm and a molecular algorithm for identification of Shigella spp., Escherichia coli, and enteroinvasive E. coli (EIEC).en
dc.typeArticleen
dc.identifier.journalJ Clin Microbiol 2018; 56(10):e00510en
html.description.abstractIdentification of Shigella spp., Escherichia coli and enteroinvasive E. coli is challenging, because of their close relatedness. Distinction is vital, as infections with Shigella spp. are under surveillance of health authorities, in contrast to EIEC infections. In this study, a culture dependent identification algorithm and a molecular identification algorithm were evaluated. Discrepancies between the two algorithms and original identification were assessed using Whole Genome Sequencing (WGS). After discrepancy analysis, with the molecular algorithm, 100% of the evaluated isolates were identified in concordance with original identification. However, the resolution for certain serotypes was lower than previously described methods and lower than the culture dependent algorithm. Although, the resolution of the culture dependent algorithm is high, 100% of non-invasive E. coli, S. sonnei, S. dysenteriae, 93% of S. boydii and EIEC and 85% of S. flexneri were identified in concordance with the original identification. Discrepancy analysis using WGS was able to confirm one of the used algorithms in four discrepant results. However, it failed to clarify three other discrepant results as it added yet another identification. Both proposed algorithms performed well for the identification of Shigella spp and EIEC, and are applicable in low-resource settings in contrast to earlier described methods that require WGS for daily diagnostics. Evaluation of the algorithms showed that both algorithms are capable of identifying Shigella species and EIEC isolates. The molecular algorithm is more applicable in clinical diagnostics for fast and accurate screening, while the culture dependent algorithm is more suitable for reference laboratories to identify Shigella spp. and EIEC up to serotype level.


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