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doi:10.22028/D291-40861
Titel: | Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning |
VerfasserIn: | Abdrabou, Ahmed Mohamed Mostafa Sy, Issa Bischoff, Markus Arroyo, Manuel J. Becker, Sören L. Mellmann, Alexander von Müller, Lutz Gärtner, Barbara Berger, Fabian K. |
Sprache: | Englisch |
Titel: | European Journal of Clinical Microbiology & Infectious Diseases |
Bandnummer: | 42 |
Heft: | 11 |
Seiten: | 1373-1381 |
Verlag/Plattform: | Springer Nature |
Erscheinungsjahr: | 2023 |
Freie Schlagwörter: | Clostridium difcile Ribotypes Anaerobic bacteria MALDI-TOF mass spectrometry Proteomic signature Machine learning Identifcation |
DDC-Sachgruppe: | 610 Medizin, Gesundheit |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Hypervirulent ribotypes (HVRTs) of Clostridioides difcile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difcile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for diferent machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could diferentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/ RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identifcation of major European HVRTs. |
DOI der Erstveröffentlichung: | 10.1007/s10096-023-04665-y |
URL der Erstveröffentlichung: | https://link.springer.com/article/10.1007/s10096-023-04665-y |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-408617 hdl:20.500.11880/36710 http://dx.doi.org/10.22028/D291-40861 |
ISSN: | 1435-4373 0934-9723 |
Datum des Eintrags: | 27-Okt-2023 |
Bezeichnung des in Beziehung stehenden Objekts: | Supplementary Information |
In Beziehung stehendes Objekt: | https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM1_ESM.docx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM2_ESM.xlsx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM3_ESM.docx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM4_ESM.docx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM5_ESM.docx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM6_ESM.docx https://static-content.springer.com/esm/art%3A10.1007%2Fs10096-023-04665-y/MediaObjects/10096_2023_4665_MOESM7_ESM.docx |
Fakultät: | M - Medizinische Fakultät |
Fachrichtung: | M - Infektionsmedizin |
Professur: | M - Prof. Dr. Sören Becker |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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s10096-023-04665-y.pdf | 1,36 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons