at Google ScholarTitle data
Moawad, Amira A. ; Silge, Anja ; Bocklitz, Thomas ; Fischer, Katja ; Rösch, Petra ; Roesler, Uwe ; Elschner, Mandy C. ; Popp, Jürgen ; Neubauer, Heinrich:
A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species.
In: Molecules.
Vol. 24
(2019)
Issue 24
.
- 4516.
ISSN 1420-3049
DOI: https://doi.org/10.3390/molecules24244516
Abstract in another language
Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | Glanders; melioidosis; Raman spectroscopy; SVM; PCA; Burkholderia mallei; Burkholderia pseudomallei; heat inactivation |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 15 May 2023 13:46 |
| Last Modified: | 15 May 2023 13:46 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76346 |