Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation-Reference-Cited by-同舟云学术

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation

Published:2024-03-06 Issue:11 Volume:96 Page:4419-4429
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Zhang Shuaihua¹,Han Ziyu¹,Qi Hang¹,Liu Siyuan¹,Liu Bohua¹,Sun Chongling¹,Feng Zhe²,Sun Meiqing²,Duan Xuexin¹^ORCID

Affiliation:

1. State Key Laboratory of Precision Measuring Technology & Instruments, College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China

2. Wuqing District Center for Disease Control and Prevention, Tianjin 301700, China

Funder

Nanchang Institute for Microtechnology, Tianjin University

National Natural Science Foundation of China

Higher Education Discipline Innovation Project

Wuqing District, Tianjin

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.3c04421

Reference55 articles.

1. Evaluating the Accuracy of Impedance Flow Cytometry with Cell-Sized Liposomes

2. Floating-Electrode-Enabled Impedance Cytometry for Single-Cell 3D Localization

3. Single-Cell Impedance Flow Cytometry

4. Label-Free and Simultaneous Mechanical and Electrical Characterization of Single Plant Cells Using Microfluidic Impedance Flow Cytometry

5. A comparison of microfluidic methods for high-throughput cell deformability measurements

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