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Updated: Jun 13, 2025

In Vitro Multiparametric Cellular Analysis by Micro Organic Charge-modulated Field-effect Transistor Arrays

In Vitro Multiparametric Cellular Analysis by Micro Organic Charge-modulated Field-effect Transistor Arrays

Published on: September 20, 2021

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors.

Hyun-June Jang^1,2, Hyou-Arm Joung³, Artem Goncharov³

¹Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States.

|September 10, 2024

Summary

This summary is machine-generated.

This study integrates field-effect transistor (FET) biosensors with deep learning (DL) for accurate, low-cost cholesterol testing. The novel approach enhances sensitivity and reliability for point-of-care diagnostics.

Keywords:

FET biosensors cholesterol deep learning dry chemistry paper cartridge

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Area of Science:

Biosensing and Analytical Chemistry
Electrical Engineering
Computational Biology

Background:

Paper-based analytical devices often suffer from low sensitivity.
Field-effect transistor (FET) sensors offer electrical measurement capabilities but face challenges like sample matrix interference.
Integrating these technologies can overcome existing limitations in biosensing.

Purpose of the Study:

To develop a novel quantitative biosensing platform by combining FET sensors, paper-based cartridges, and deep learning (DL).
To enhance sensitivity and accuracy in biosensing through kinetic data analysis.
To create a cost-effective and user-friendly diagnostic tool for point-of-care applications.

Main Methods:

Utilized a field-effect transistor (FET) integrated with a paper-based analytical cartridge for biosensing.
Employed deep learning (DL) algorithms to analyze kinetic data from bioreactions.
Performed quantitative analysis of cholesterol concentration using the developed platform.

Main Results:

The integrated system demonstrated improved sensitivity compared to traditional paper analytical devices.
Deep learning analysis effectively mitigated sample matrix interference, a common challenge in FET biosensors.
Cholesterol testing showed a coefficient of variation of <6.46% and a strong correlation (r² >0.976) with clinical laboratory results.

Conclusions:

The fusion of FET sensors, paper cartridges, and DL offers a powerful approach for quantitative biosensing.
This technology has the potential to significantly advance point-of-care diagnostics and at-home testing.
The developed platform provides enhanced accessibility, ease-of-use, and accuracy in diagnostic measurements.