4.7 Article

Smartphone-imaged multilayered paper-based analytical device for colorimetric analysis of carcinoembryonic antigen

Journal

ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 412, Issue 11, Pages 2517-2528

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s00216-020-02475-1

Keywords

Microfluidic paper-based analytical device (mu PAD); Carcinoembryonic antigen (CEA); Smartphone; Point-of-care testing (POCT)

Funding

  1. National Key Research and Development Program of China [2017FYA0205303, 2017FYA0205301]
  2. National Natural Science Foundation of China [81571835, 81672247]
  3. Shanghai Science and Technology Fund [15DZ225200]
  4. SJTU [ZH2018QNA03, YG2019QNB09]

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Paper-based immunoassays are effective methods that employ microfluidic paper-based analytical devices (mu PADs) for the rapid, simple, and accurate quantification of analytes in point-of-care diagnosis. In this study, we developed a wax-printed multilayered mu PAD for the colorimetric detection of carcinoembryonic antigen (CEA), where the device contained a movable and rotatable detection layer to allow the mu PAD to switch the state of the sample solutions, i.e., flowing or storing in the sensing zones. A smartphone with a custom-developed program served as an automated colorimetric reader to capture and analyze images from the mu PAD, before calculating and displaying the test results. After optimizing the crucial conditions for the assay, the proposed method exhibited a wide linear dynamic range from 0.5 to 70 ng/mL, with a low CEA detection limit of 0.015 ng/mL. The clinical performance of this method was successfully validated using 50 positive and 40 negative human serum samples, thereby demonstrating the high sensitivity of 98.0% and specificity of 97.5% in the detection of CEA. The proposed method is greatly simplified compared with the cumbersome steps required for traditional immunoassays, but without any loss of accuracy and stability, as well as reducing the time needed to detect CEA. Complex and bulky instruments are replaced with a smartphone. The proposed detection platform could potentially be applied in point-of-care testing. Graphical abstract

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