2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification



: 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

: 2022

: Microchimica Acta

: MICROCHIMICA ACTA

: MICROCHIM ACTA

:  273

: 189

: 8

: 14

: 0026-3672

: 1436-5073

DOI: https://doi.org/10.1007/s00604-022-05368-5

: https://link.springer.com/article/10.1007/s00604-022-05368-5

: 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.

Last updated on 2024-26-11 at 22:33