| 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
|
| Resource |
| General Microbes |
JCM1652
JCM2152 |
