Refereed journal article or data article (A1)
2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification
List of Authors: Li Zhijun, Jiang Yizhou, Tang Shihuan, Zou Haixia, Wang Wentao, Qi Guangpei, Zhang Hongbo, Jin Kun, Wang Yuhe, Chen Hong, Zhang Liyuan, Qu Xiangmeng
Publisher: SPRINGER WIEN
Publication year: 2022
Journal: Microchimica Acta
Journal name in source: MICROCHIMICA ACTA
Journal acronym: MICROCHIM ACTA
Article number: 273
Volume number: 189
Issue number: 8
Number of pages: 14
ISSN: 0026-3672
eISSN: 1436-5073
DOI: http://dx.doi.org/10.1007/s00604-022-05368-5
URL: https://link.springer.com/article/10.1007/s00604-022-05368-5
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175913092
An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n=288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10(5) similar to 10(8) CFU/mL for Escherichia coli, 10(2) similar to 10(7) CFU/mL for E. coli beta, 10(3) similar to 10(8) CFU/mL for Staphylococcus aureus, 10(3) similar to 10(7) CFU/mL for MRSA, 10(2) similar to 10(8) CFU/ mL for Pseudomonas aeruginosa, 10(3) similar to 10(8) CFU/mL for Enterococcus faecalis, 10(2) similar to 10(8) CFU/mL for Klebsiella pneumoniae, and 10(3) similar to 10(8) CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.
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