Microfluidic immunosensor with integrated liquid core waveguides for sensitive Mie scattering detection of avian

Brian C Heinze1, Jessica R Gamboa, Keesung Kim

  • 1Department of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, AZ 85721-0038, USA.

Insights

This study introduces a novel microfluidic device for rapid avian influenza (AI) detection. It uses optical waveguides to detect viral antigens in chicken feces with high sensitivity in under two minutes.

Area of Science:

  • Biomedical Engineering
  • Optical Physics
  • Veterinary Diagnostics

Background:

  • Avian influenza (AI) poses a significant threat to poultry health and global food security.
  • Rapid and sensitive detection of AI viral antigens is crucial for effective disease control.
  • Current diagnostic methods can be time-consuming and require specialized laboratory equipment.

Purpose of the Study:

  • To develop and validate a microfluidic device for rapid and sensitive detection of avian influenza (AI) viral antigens.
  • To utilize integrated liquid core optical waveguides for measuring immunoagglutination-induced light scattering.
  • To establish a robust platform for point-of-care disease diagnostics.

Main Methods:

  • Mie scattering simulations were performed to optimize waveguide geometry for enhanced scattering efficiency.
  • A microfluidic device integrated with liquid core optical waveguides was fabricated.
  • On-chip diffusional mixing of AI antigens and assay reagents was employed for real-time monitoring of light scattering.

Main Results:

  • The developed device achieved a low detection limit of 1 pg mL(-1) for AI viral antigens.
  • Detection was demonstrated in both clean buffer and a real biological matrix (chicken feces).
  • The assay provided results in less than 2 minutes, indicating rapid detection capabilities.

Conclusions:

  • The integrated optical waveguide microfluidic device enables rapid and highly sensitive detection of AI viral antigens.
  • The technology shows promise for point-of-care diagnostics, adaptable for detecting various biological molecules.
  • This approach offers a significant advancement for veterinary disease surveillance and management.

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