However, the kinds of sensing elements in these sensing arrays are limited, with the confined ability for the special kind of microorganism identification. Combining the machine learning approach, a large amount of data can be processed for multi-target recognition. These sensing arrays collect features from multiple dimensions to differentiate information for multiple target detection, including different receptors on the membrane of bacteria, the interaction between bacteria and sensing elements, and the specific metabolites. have reported a sensing array of different thiopropionic acid, thiosuccinic acid, cysteine, and CTAB-functionalized AuNPs for identified 15 microorganisms. reported a GO-antimicrobial peptide (AMP) sensing array for identified 13 different bacteria. have reported a fluorescence sensing array for identified five different bacteria, which six types of metal ion-protein-AuNC as sensing elements Fan et al. In recent years, a variety of sensing array has been developed, targeting to fulfill the requirement of multiple target microorganism detection. However, these methods require expensive reagents, instruments, higher operating skills, and low throughput, which limit the application of these methods in clinical practice. Some traditional methods for identifying microorganisms have been developed, including the morphological recognition method, the immunodiagnostic method, and the molecular diagnostics method. To reduce the dose of the antibiotics, the accurate recognition of microbial taxonomic for multiple microorganism recognition is essential to precisely guide the medical therapy. Antibiotics that are applied to treat pathogenic microorganism infection have been overused, leading to the thriving of antibiotic-resistance microorganisms. Pathogenic microorganisms have rich varied, diverse surface morphology and complex biochemical characteristics, which essentially threaten human health and cause social panic upon their infection. MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.Each of these sensing elements’ performance has competitive reaction with the microorganism. Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array.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.Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. 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. For each microorganism, the detection concentration is 10 5 ~ 10 8 CFU/mL for Escherichia coli, 10 2 ~ 10 7 CFU/mL for E. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. 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. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. 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. 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.
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