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Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker

Changhao Dai1,2, Huiwen Xiong3, Rui He4,5

  • 1State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China.

Advanced Materials (Deerfield Beach, Fla.)
|January 30, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a dual-mode diagnostic platform for rapid, accurate on-site disease detection. It achieves near 99% accuracy for infectious biomarkers like tuberculosis, human rhinovirus, and group B streptococcus in just 5 minutes.

Keywords:
diagnostic accuracydual‐mode sensingmulticlassificationpoint‐of‐care test

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Area of Science:

  • Biomedical Engineering
  • Point-of-Care Diagnostics
  • Biosensor Technology

Background:

  • Current on-site diagnostic tests often lack accuracy, particularly for multiplexed detection within short timeframes.
  • Single-mode signal reliance limits the precision of rapid point-of-care tests (POCTs).

Purpose of the Study:

  • To develop a dual-mode diagnostic platform integrating electrochemiluminescence and field-effect transistor sensors for enhanced accuracy in disease biomarker detection.
  • To improve the accuracy and reliability of multiplexed POCTs for infectious diseases.

Main Methods:

  • A microfluidic chip integrating electrochemiluminescence and field-effect transistor sensors was developed.
  • Dual-mode sensor readouts were used to detect biomarkers for tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS).
  • Machine learning classifiers were employed for multiclassification diagnosis based on high-dimensional data.

Main Results:

  • The dual-mode platform achieved a diagnostic accuracy approaching 99% in clinical validation with 501 unprocessed samples.
  • Diagnosis time was reduced to 5 minutes without compromising accuracy.
  • Performance significantly surpassed existing rapid POCT technologies (77%-93% accuracy).

Conclusions:

  • The developed platform overcomes the accuracy limitations of current rapid on-site diagnostic methods.
  • It offers laboratory-level accuracy for POCTs, enabling reliable personalized healthcare applications.
  • This technology holds significant promise for complex healthcare scenarios requiring fast and precise diagnostics.