4.6 Article

Rapid Detection System for Hepatitis B Surface Antigen (HBsAg) Based on Immunomagnetic Separation, Multi-Angle Dynamic Light Scattering and Support Vector Machine

Journal

IEEE ACCESS
Volume 8, Issue -, Pages 107373-107386

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3000357

Keywords

Light scattering; Immune system; Support vector machines; Photodetectors; Laser theory; Laser beams; HBsAg detection; immunomagnetic separation; laser light scattering; support vector machine

Funding

  1. National Key Special Science Program [2017YFA0205301]
  2. NSFC of China [61527806, 61401217]
  3. Clinical Advanced Technology of Social Development Projects in Jiangsu Province [BE2018695]
  4. Natural Science Foundation of Jiangsu Province [BK20140900]
  5. Medical Science and Technology Development Foundation, Nanjing Department of Health [ZKX18029, ZKX18016]
  6. Southeast University
  7. Nanjing Medical University

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Hepatitis B virus (HBV) is a significant public health problem worldwide. Hepatitis B surface antigen (HBsAg) is the principle marker for laboratory testing of HBV, but the rapid identification of HBsAg is challenging in a resource-limited setting. Antibodies to HBsAg (Anti-HBs) levels are measured as markers for an immune response to vaccination as well as for decision making for specific treatment against Hepatitis-B. This research developed a prototype for the rapid detection of HBsAg using immunomagnetic separation, dynamic light scattering, and support vector machine. Magnetic beads coated with polyclonal anti-HBsAg were used to isolate HBsAg from the sample. The performance characteristics of quantitative real-time detection of HBsAg were characterized under optimized conditions. Twelve photodetectors were arranged on four concentric curvatures at different angles. The photodetectors were positioned around the sample flask in forward direction. The prototype acquires the real-time laser scattering light from the sample, and the noise was removed. The power spectral features were extracted from the acquired signal. Support vector machines (SVM) were used for training a classification algorithm by using extracted features. The overall classification accuracy for the identification of HBsAg was 87.7& x0025;. The HBsAg detection test was also performed on 20 serum specimens, with 10 serum samples were positive for HBsAg and 10 were healthy control subjects. The test had a dynamic range of 98.86 IU/mL to 3163.5 IU/mL. Results of HBsAg detection agreed completely with those of conventional Chemiluminescence Immunoassay (CLIA). In conclusion, the proposed HBsAg detection method can differentiate the sample that contains HBsAg enriched IM beads and blank IM beads.

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