Article ; Online: Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.
2022 Volume 12, Issue 1, Page(s) 1575
Abstract: The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of- ... ...
Abstract | The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced. |
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MeSH term(s) | Aspergillus flavus |
Language | English |
Publishing date | 2022-01-28 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 2615211-3 |
ISSN | 2045-2322 ; 2045-2322 |
ISSN (online) | 2045-2322 |
ISSN | 2045-2322 |
DOI | 10.1038/s41598-022-05647-4 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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