Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization-Reference-Cited by-同舟云学术

Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization

Published:2022 Issue:2 Volume:22 Page:240-249
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Feng Yongxiang¹^ORCID,Cheng Zhen²,Chai Huichao¹^ORCID,He Weihua¹,Huang Liang³^ORCID,Wang Wenhui¹

Affiliation:

1. State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China

2. Department of Automation, Tsinghua University, Beijing, China

3. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, Anhui, China

Abstract

We propose to employ NN-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic metric-based cell classification at high throughput.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/LC/D1LC00755F

Reference41 articles.

1. Cellular Analysis Using Microfluidics

2. Making a big thing of a small cell – recent advances in single cell analysis

3. Recent advances in microfluidic techniques for single-cell biophysical characterization