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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 |
Files for this record:
| File | Description | Size | Format | |
|---|---|---|---|---|
| s10096-023-04665-y.pdf | 1,36 MB | Adobe PDF | View/Open |
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