4.7 Article

Cost-effective portable microfluidic impedance cytometer for broadband impedance cell analysis based on viscoelastic focusing

期刊

TALANTA
卷 242, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.talanta.2022.123274

关键词

Microfluidic impedance cytometer; Viscoelastic focusing; Maximum length sequence; Cell type identification

资金

  1. National Natural Science Foundation of China [81727801, 51875103, 51775111]
  2. Natural Science Foundation of Jiangsu Province [BK20190064]
  3. Six Talent Peaks Project of Jiangsu Province [SWYY-005]
  4. Zhishan Youth Scholar Program of SEU
  5. China Postdoctoral Science Foundation [2020M671450]
  6. Jiangsu Planned Projects for Postdoctoral Research Funds [2020Z042]
  7. Open Research Fund of Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University [KF202008]

向作者/读者索取更多资源

This paper proposes a cost-effective portable microfluidic impedance cytometer (MIC) for broadband impedance analysis of cells using maximum length sequence (MLS) and viscoelastic focusing. Results show that MIC has the potential for rapid detection of circulating tumor cells.
Single-cell impedance analysis can provide valuable information for characterizing and discriminating cells. In this paper, a cost-effective portable microfluidic impedance cytometer (MIC) was proposed to realize the broadband impedance analysis of cells by using maximum length sequence (MLS) and viscoelastic focusing. The MIC comprised a microfluidic chip with a straight microchannel, two indium tin oxide (ITO) electrodes, and a home-made platform performing maximum length sequence technique. The viscoelastic focusing enabled cells to focus into a single train to eliminate the influence of cell position variation on acquired electrical signals and allow the cells to pass through the detection region one by one. The MLS technique realized the fast broadband impedance detection of single cells at a low hardware cost. The impedance data under multiplex frequencies was obtained to uncover the dielectric properties of white blood cells (WBCs) and MCF-7 cancer cells. The machine learning was used to train the impedance data and to identify cell types. The results indicated that 98.98% of MCF-7 cells and 98.65% of WBCs were correctly identified. Our MIC showed a potential to be developed as a cost-effective and portable device for point-of-care testing of circulating tumor cells from patients' peripheral blood.

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