Projekt per år
Sammanfattning
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 ~ 10 8 CFU/mL for Escherichia coli, 10 2 ~ 10 7 CFU/mL for E. coli-β, 10 3 ~ 10 8 CFU/mL for Staphylococcus aureus, 10 3 ~ 10 7 CFU/mL for MRSA, 10 2 ~ 10 8 CFU/mL for Pseudomonas aeruginosa, 10 3 ~ 10 8 CFU/mL for Enterococcus faecalis, 10 2 ~ 10 8 CFU/mL for Klebsiella pneumoniae, and 10 3 ~ 10 8 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. Graphical abstract: • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements’ performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms. [Figure not available: see fulltext.]
Originalspråk | Engelska |
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Artikelnummer | 273 |
Antal sidor | 14 |
Tidskrift | Microchimica Acta |
Volym | 189 |
Nummer | 8 |
DOI | |
Status | Publicerad - aug. 2022 |
MoE-publikationstyp | A1 Tidskriftsartikel-refererad |
Fingeravtryck
Fördjupa i forskningsämnen för ”2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification”. Tillsammans bildar de ett unikt fingeravtryck.Utrustning
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Åbo Akademi Functional Printing Center
Martti Toivakka (PI), Jessica Rosenholm (PI), Nicklas Anttu (PI), Johan Bobacka (PI), Tan Phat Huynh (PI), Jouko Peltonen (PI), Xiaoju Wang (PI), Carl-Eric Wilen (PI), Chunlin Xu (PI), Hongbo Zhang (PI) & Ronald Österbacka (PI)
Fakulteten för naturvetenskaper och teknikUtrustning/facilitet: Facilitet
Projekt
- 2 Aktiv
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FCFH: Finland-China Network in Food and Health Sciences
Rosenholm, J., Xu, C. & Zhang, H.
Undervisnings och kulturministeriet i Finland (UKM)
01/01/21 → 31/12/24
Projekt: Ministerier / Statliga myndigheter och verk
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Targeted delivery of CRISPR/Cas9 for advanced liver cancer therapy through c-Myc knockout
01/09/19 → 31/08/24
Projekt: FA/Övriga Forskningsråd