Reference - Detail
| RRC ID | 66773 |
|---|---|
| Author | Hattori S, Sekido R, Leong IW, Tsutsui M, Arima A, Tanaka M, Yokota K, Washio T, Kawai T, Okochi M. |
| Title | Machine learning-driven electronic identifications of single pathogenic bacteria. |
| Journal | Sci Rep |
| Abstract |
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. |
| Volume | 10(1) |
| Pages | 15525 |
| Published | 2020-9-23 |
| DOI | 10.1038/s41598-020-72508-3 |
| PII | 10.1038/s41598-020-72508-3 |
| PMID | 32968098 |
| PMC | PMC7512020 |
| MeSH | Bacillus cereus / ultrastructure Bacterial Infections / diagnosis* Bacterial Infections / microbiology Biosensing Techniques / methods* Electronics Escherichia coli / ultrastructure Machine Learning* Micropore Filters Microscopy, Electron, Scanning Pseudomonas fluorescens / ultrastructure Salmonella enterica / ultrastructure Staphylococcus aureus / ultrastructure |
| Altmetric score |
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| The most frequently cited source | X(Twitter) |
| Total number of mentions | 8 |
| Altmetric score changes over past 6months | 0.0 |
| Resource | |
| General Microbes | JCM1652 JCM2152 |