Summary:
UCLA researchers in the Department of Electrical and Computer Engineering in collaboration with the University of Chicago have developed a new diagnostic biosensor by combining field-effect transistors with porous media.
Background:
Point-of-care (POC) and self-testing tools rely on the real-time monitoring of biomarkers in bodily fluids. Existing technologies can only monitor single target biomarkers and are limited by complex detection mechanisms. Current methods are additionally limited by reduced shelf life and complex configurations, leading to increased manufacturing challenges and costs. There remains an unmet need for a biosensor for use in point-of-care and self-testing diagnostics that can overcome these traditional challenges and be utilized for multiplexed biomarker detection for a wide array of diseases
Innovation:
UCLA researchers led by Professor Aydogan Ozcan have developed a biosensor for the real-time detection and monitoring of several biomolecules from bodily fluids. The device combines field-effect transistors (FETs) with porous media, allowing for the monitoring of the kinetics of several specific enzyme reactions in real-time. Biologically active components (e.g., enzymes) can be incorporated into the media, and can react with biomarkers from bodily fluids. These reactions generate electrochemical signals that can be measured with the electrode. The proposed technology provides a scalable and low-cost approach for the real-time monitoring of multiple biomarkers.
Potential Applications:
- Point-of-care testing
- Lipid profiling
- Kinetics studies
- Environmental monitoring
- Drug screening
- Pandemic monitoring
- Disease monitoring (e.g., diabetes)
Advantages:
- Real-time monitoring of biomarkers
- Multiplexed analysis
- Cost-effective
- Increased shelf-life
- Reduced sample matrix effects
- Reduced manufacturing complexity
Development To Date:
The first demonstration of the technology is complete.
Reference:
UCLA Case No. 2023-093
Lead Inventor:
Aydogan Ozcan
Relevant Publications:
Deep Learning-based Kinetic Analysis in Paper-based Analytical Cartridges Integrated with Field-effect Transistors