2021-333 Quantitative Particle Agglutination Assay for Point-Of-Care Testing Using Mobile Holographic Imaging and Deep Learning

Summary

UCLA researchers in the Department of Electrical and Computer Engineering have developed a method that increases the sensitivity of particle agglutination assays and reduces the required sample volume.

Background

Particle agglutination assays are simple immunological tests that can be utilized to diagnose a wide range of illnesses. These tests can be easily produced and have a high sensitivity. Currently, these assays quantify particle agglutination with a spectrometer or by eye; however, this requires large sample volumes and can be inaccurate. Optical microscopes can overcome these issues, but this equipment can be more expensive than standard spectrometers. Therefore, there is a need for a rapid and cost-effective quantitative method that uses less volume to measure particle agglutination for point-of-care diagnostics. 

Innovation

UCLA researchers have developed a rapid and cost-effective particle agglutination assay. By utilizing deep learning techniques, the method was able to measure agglutination particles with high sensitivity and was approximately 100 times more sensitive in the detection of protein levels in human serum compared to C-reactive protein test (CRP). The innovation’s microfluidic device reduces the required volume and is rapid, requiring around 3 minutes from initial sample collection. It is cost-effective, portable, and can be utilized for point-of-care diagnostics to help diagnose a wide range of medical diseases.  

Potential Applications

  • Diagnostics
  • Bodily fluids analysis
  • Medical tests 

Advantages:

  • Cost-effective
  • High sensitivity
  • Fast analysis
  • Low sample volume 

Development to Date:

First successfully demonstration of particle agglutination analysis with human serum samples 

Related Papers

Y. Luo, H. Joung, S. Esparza, J. Rao, O. Garner, and A. Ozcan “Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning,” Lab on a Chip (2021) DOI: 10.1039/D1LC00467K.  

­

Patent Information:
For More Information:
Nikolaus Traitler
Business Development Officer (BDO)
nick.traitler@tdg.ucla.edu
Inventors:
Aydogan Ozcan
Hyou-Arm Joung
Yi Luo