Machine learning-driven electronic identifications of single pathogenic bacteria
抄録
<jats:title>Abstract</jats:title><jats:p>A rapid method for screening pathogens can revolutionize health care by enabling infection control through medication before symptom. Here we report on label-free single-cell identifications of clinically-important pathogenic bacteria by using a polymer-integrated low thickness-to-diameter aspect ratio pore and machine learning-driven resistive pulse analyses. A high-spatiotemporal resolution of this electrical sensor enabled to observe galvanotactic response intrinsic to the microbes during their translocation. We demonstrated discrimination of the cellular motility via signal pattern classifications in a high-dimensional feature space. As the detection-to-decision can be completed within milliseconds, the present technique may be used for real-time screening of pathogenic bacteria for environmental and medical applications.</jats:p>
収録刊行物
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- Scientific Reports
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Scientific Reports 10 (1), 2020-09-23
Springer Science and Business Media LLC