Please use this identifier to cite or link to this item: doi:10.22028/D291-40861
Title: Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning
Author(s): Abdrabou, Ahmed Mohamed Mostafa
Sy, Issa
Bischoff, Markus
Arroyo, Manuel J.
Becker, Sören L.
Mellmann, Alexander
von Müller, Lutz
Gärtner, Barbara
Berger, Fabian K.
Language: English
Title: European Journal of Clinical Microbiology & Infectious Diseases
Volume: 42
Issue: 11
Pages: 1373-1381
Publisher/Platform: Springer Nature
Year of Publication: 2023
Free key words: Clostridium difcile
Ribotypes
Anaerobic bacteria
MALDI-TOF mass spectrometry
Proteomic signature
Machine learning
Identifcation
DDC notations: 610 Medicine and health
Publikation type: Journal Article
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 of the first publication: 10.1007/s10096-023-04665-y
URL of the first publication: https://link.springer.com/article/10.1007/s10096-023-04665-y
Link to this record: 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
Date of registration: 27-Oct-2023
Description of the related object: Supplementary Information
Related object: 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
Faculty: M - Medizinische Fakultät
Department: M - Infektionsmedizin
Professorship: M - Prof. Dr. Sören Becker
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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