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dc.contributor.authorAvershina, Ekaterina
dc.contributor.authorKhezri, Abdolrahman
dc.contributor.authorAhmad, Rafi
dc.date.accessioned2024-02-01T15:26:52Z
dc.date.available2024-02-01T15:26:52Z
dc.date.created2023-04-25T00:22:27Z
dc.date.issued2023
dc.identifier.citationAntibiotics. 2023, 12 (4), 1-19.en_US
dc.identifier.issn2079-6382
dc.identifier.urihttps://hdl.handle.net/11250/3115137
dc.description.abstractAntimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment. Current DNA sequencing technologies offer the potential to develop rapid infection and AMR diagnostic tools that can provide information in a few hours rather than days. However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited for routine lab use. In this review, we give an overview of the AMR burden on healthcare, describe current pathogen identification and AMR screening methods, and provide perspectives on how DNA sequencing may be used for rapid diagnostics. Additionally, we discuss the common steps used for DNA data analysis, currently available pipelines, and tools for analysis. Direct, culture-independent sequencing has the potential to complement current culture-based methods in routine clinical settings. However, there is a need for a minimum set of standards in terms of evaluating the results generated. Additionally, we discuss the use of machine learning algorithms regarding pathogen phenotype detection (resistance/susceptibility to an antibiotic).en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectantimicrobial resistanceen_US
dc.subjectrapid diagnosticsen_US
dc.subjectwhole genome sequencingen_US
dc.subjectmicrobial genomicsen_US
dc.subjectbacterial cultureen_US
dc.subjectmachine learningen_US
dc.subjectenabling technologiesen_US
dc.titleClinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics—Current State and Whole Genome Sequencing Implementation Perspectivesen_US
dc.title.alternativeClinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics—Current State and Whole Genome Sequencing Implementation Perspectivesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 by the authors.en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400en_US
dc.source.pagenumber1-19en_US
dc.source.volume12en_US
dc.source.journalAntibioticsen_US
dc.source.issue4en_US
dc.identifier.doi10.3390/antibiotics12040781
dc.identifier.cristin2143011
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal