Integrated Urinalysis Devices Based on Interface-Engineered Field-Effect Transistor Biosensors Incorporated With
Yanbing Yang1, Jingfeng Wang1, Wanting Huang1
1Department of Cardiology, Institute of Molecular Medicine, Renmin Hospital of Wuhan University, College of Chemistry and Molecular Sciences, School of Computer Science, Wuhan University, Wuhan, 430072, China.
Advanced Materials (Deerfield Beach, Fla.)
|July 19, 2022
Summary
A new urinalysis device offers non-invasive bladder cancer detection. This advanced biosensor technology accurately identifies bladder cancer and its stages using trace protein analysis in urine.
Area of Science:
- Biomedical Engineering
- Oncology
- Analytical Chemistry
Background:
- Bladder cancer diagnosis traditionally relies on invasive cystoscopy.
- Urinalysis presents challenges due to low biomarker concentrations and urine's complex matrix.
- Developing non-invasive, accurate diagnostic tools for bladder cancer is crucial.
Purpose of the Study:
- To develop and validate a clinically adoptable urinalysis device for bladder cancer diagnosis and classification.
- To integrate molecular-specificity biosensors with machine learning for enhanced accuracy.
- To assess the device's capability in detecting cancer stages and recurrence.
Main Methods:
- Utilized indium gallium zinc oxide field-effect transistor (IGZO FET) biosensor arrays for detecting five bladder-tumor-associated proteins.
- Engineered sensing interfaces on biosensors for high sensitivity and selectivity in complex urine.
- Integrated the biosensor device with a machine-learning algorithm for data analysis.
Main Results:
- The device achieved 95.0% accuracy in identifying bladder cancer in a cohort of 197 patients and 75 controls.
- Cancer staging was distinguished with an overall accuracy of 90.0%.
- The system demonstrated potential for assessing post-surgical bladder cancer recurrence.
Conclusions:
- The developed non-invasive urinalysis device provides a robust technology for bladder cancer diagnosis and classification.
- This technology supports remote healthcare and personalized medicine approaches.
- The IGZO FET biosensor integrated with machine learning offers a sensitive and selective method for early bladder cancer detection.
