Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration

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dc.contributor.authorHegerová, Dagmarcs
dc.contributor.authorČíhalová, Kristýnacs
dc.contributor.authorGuráň, Romancs
dc.contributor.authorDostálová, Simonacs
dc.contributor.authorŠmerková, Kristýnacs
dc.contributor.authorVeselý, Radekcs
dc.contributor.authorGumulec, Jaromírcs
dc.contributor.authorMasařík, Michalcs
dc.contributor.authorHeger, Zbyněkcs
dc.contributor.authorAdam, Vojtěchcs
dc.contributor.authorKizek, Renécs
dc.coverage.issue6cs
dc.coverage.volume19cs
dc.date.issued2015-12-01cs
dc.description.abstractBackground: Infections, mostly those associated with colonization of wound by different pathogenic microorganisms, are one of the most serious health complications during a medical treatment. Therefore, this study is focused on the isolation, characterization, and identification of microorganisms prevalent in superficial wounds of patients (n=50) presenting with bacterial infection. Methods: After successful cultivation, bacteria were processed and analyzed. Initially the identification of the strains was performed through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on comparison of protein profiles (2-30 kDa) with database. Subsequently, bacterial strains from infected wounds were identified by both matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and sequencing of 16S rRNA gene 108. Results: The most prevalent species was Staphylococcus aureus (70%), and out of those 11% turned out to be methicillin-resistant (mecA positive). Identified strains were compared with patients' diagnoses using the method of artificial neuronal network to assess the association between severity of infection and wound microbiome species composition. Artificial neuronal network was subsequently used to predict patients' prognosis (n=9) with 85% success. Conclusions: In all of 50 patients tested bacterial infections were identified. Based on the proposed artificial neuronal network we were able to predict the severity of the infection and length of the treatment.en
dc.formattextcs
dc.format.extent604-613cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationBRAZ J INFECT DIS. 2015, vol. 19, issue 6, p. 604-613.en
dc.identifier.doi10.1016/j.bjid.2015.08.013cs
dc.identifier.issn1413-8670cs
dc.identifier.orcid0000-0003-0826-234Xcs
dc.identifier.orcid0000-0002-2912-714Xcs
dc.identifier.orcid0000-0002-1667-7660cs
dc.identifier.orcid0000-0002-9658-3444cs
dc.identifier.orcid0000-0003-1172-7195cs
dc.identifier.orcid0000-0002-3915-7270cs
dc.identifier.orcid0000-0002-8527-286Xcs
dc.identifier.other145197cs
dc.identifier.researcheridJ-7030-2012cs
dc.identifier.researcheridC-2610-2016cs
dc.identifier.researcheridD-7638-2012cs
dc.identifier.researcheridD-9920-2012cs
dc.identifier.researcheridD-1973-2013cs
dc.identifier.researcheridD-7686-2012cs
dc.identifier.scopus55769747816cs
dc.identifier.urihttp://hdl.handle.net/11012/201034
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofBRAZ J INFECT DIScs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1413867015001889cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1413-8670/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectBacterial strainsen
dc.subjectMALDI-TOFen
dc.subjectSequencingen
dc.subjectSuperficial woundsen
dc.titleInfluence of microbiome species in hard-to-heal wounds on disease severity and treatment durationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-145197en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:50:06en
sync.item.modts2025.01.17 18:45:43en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Chytré nanonástrojecs
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