4.7 Article

A sample-to-answer quantitative platform for point-of-care testing of biochemical markers in whole blood

期刊

SENSORS AND ACTUATORS B-CHEMICAL
卷 308, 期 -, 页码 -

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2020.127750

关键词

Point-of-care platform; Sample-to-answer quantitation; 3D printing; Smartphone

资金

  1. National Natural Science Foundation of China [81430053, 81430054, 81972027, 31400087, 21778071]
  2. Army's Logistics Research Program Major Project [AWS14C003-2]
  3. Youth Innovation Promotion Association CAS [2015257]
  4. Suzhou Institute of Nano-Tech and Nano-Bionics Owned Foundation [Y5AAS11001]
  5. China Scholarship Council [201803170116]

向作者/读者索取更多资源

Point-of-care quantitative detection of biochemical markers has broad applications in resource-limited settings. However, current challenges including difficulty in sample preparation and limitation in result quantitation make point-of-care detection platforms failed to achieve fully sample-to-answer detection or meet the clinical quantitative demands. Here, we developed a novel point-of-care platform to realize fully sample-to-answer biochemical marker quantitation in whole blood. Our platform integrated a plasma separation module and a detection module. The plasma separation module contained a plasma separation strip could rapidly separate the plasma from whole blood samples by lateral flow. The detection module generated a colorimetric signal which could be quantified by the smartphone ambient light sensor (ALS) in an equipment-free manner. As a proof-ofconcept, two hepatobiliary disease-associated markers, total bilirubin (TBil) and direct bilirubin (DBi1), were detected from whole blood to quantitative result. The quantitative performance for clinical samples was highly consistent with the well-established clinical chemistry analyzer. Our platform exhibited remarkable advantages of significant portability ( < 40 g), low cost ( < $5), rapidity ( < 5 min), instrument-free, high sensitivity ( < 1 mu M), and accuracy (89.5 % for TBil, 94.7 % for DBi1), which showed up a great potential for biochemical markers detection in resource-limited settings.

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