Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry | |
Tao Tang; Xun Liu; Yapeng Yuan; Ryota Kiya; Tianlong Zhang; Yang Y(杨阳); Shiro Suetsugu; Yoichi Yamazaki; Nobutoshi Ota; Koki Yamamoto | |
刊名 | Sensors and Actuators B: Chemical |
2023-01 | |
卷号 | 374期号:132698页码:无 |
关键词 | Antibiotic susceptibility test Impedance cytometry Machine learning Microfluidics Single cell analysis |
DOI | 10.1016/j.snb.2022.132698 |
英文摘要 | Impedance cytometry has enabled label-free and fast antibiotic susceptibility testing of bacterial single cells. Here, a machine learning-based impedance system is provided to score the phenotypic response of bacterial single cells to antibiotic treatment, with a high throughput of more than one thousand cells per min. In contrast to other impedance systems, an online training method on reference particles is provided, as the parallel impedance cytometry can distinguish reference particles from target particles, and label reference and target particles as the training and test set, respectively, in real time. Experiments with polystyrene beads of two different sizes (3 and 4.5 µm) confirm the functionality and stability of the system. Additionally, antibiotic-treated Escherichia coli cells are measured every two hours during the six-hour drug treatment. All results successfully show the capability of real-time characterizing the change in dielectric properties of individual cells, recognizing single susceptible cells, as well as analyzing the proportion of susceptible cells within heterogeneous populations in real time. As the intelligent impedance system can perform all impedance-based characterization and recognition of particles in real time, it can free operators from the post-processing and data interpretation. |
资助项目 | JSPS Core-to-Core program ; JSPS[20K15151] ; Amada Foundation, Japan ; NSG Foundation, Japan ; White Rock Foundation, Japan ; Australian Research Council (ARC)[DP200102269] ; JST SPRINH[JPMJSP2140] ; NAIST Touch Stone Program |
WOS关键词 | INDUCED FILAMENT FORMATION ; ROD-SHAPE ; CELLS ; SUPPORT |
WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
语种 | 英语 |
出版者 | ELSEVIER SCIENCE SA |
WOS记录号 | WOS:000882062000004 |
资助机构 | JSPS Core-to-Core program ; JSPS ; Amada Foundation, Japan ; NSG Foundation, Japan ; White Rock Foundation, Japan ; Australian Research Council (ARC) ; JST SPRINH ; NAIST Touch Stone Program |
内容类型 | 期刊论文 |
版本 | 出版稿 |
源URL | [http://ir.idsse.ac.cn/handle/183446/9868] |
专题 | 深海工程技术部_深海资源开发研究室 |
作者单位 | 1.Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan 2.Center for Digital Green-Innovation, Nara Institute of Science and Technology, Ikoma, Japan 3.Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan 4.Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan 5.Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan 572000, PR China 6.School of Engineering, Macquarie University, Sydney 2109, Australia 7.Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan |
推荐引用方式 GB/T 7714 | Tao Tang,Xun Liu,Yapeng Yuan,et al. Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry[J]. Sensors and Actuators B: Chemical,2023,374(132698):无. |
APA | Tao Tang.,Xun Liu.,Yapeng Yuan.,Ryota Kiya.,Tianlong Zhang.,...&Yaxiaer Yalikun.(2023).Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry.Sensors and Actuators B: Chemical,374(132698),无. |
MLA | Tao Tang,et al."Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry".Sensors and Actuators B: Chemical 374.132698(2023):无. |
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