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High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning
1Jiangsu Key Laboratory for Design and Manufacturing of Precision Medicine Equipment, School of Mechanical Engineering, Southeast University, Nanjing 211100, China.
The Journal of Physical Chemistry Letters
|June 16, 2025
Summary
This study introduces a new method using nanopore technology and machine learning to detect C-peptide, a key diabetes biomarker. This approach achieves high accuracy for early diabetes diagnosis and monitoring.
Area of Science:
- Biotechnology
- Nanotechnology
- Medical Diagnostics
Background:
- C-peptide is a crucial biomarker for diabetes diagnosis, treatment, and prevention.
- Early and accurate detection of C-peptide is essential for effective diabetes management.
Purpose of the Study:
- To develop a novel detection methodology for C-peptide using solid-state nanopore technology.
- To enhance C-peptide identification sensitivity and accuracy through machine learning integration.
Main Methods:
- Fabrication of solid-state nanopores using focused ion beam milling.
- Analysis of C-peptide and serum ionic current blockade characteristics during translocation.
- Application of five-dimensional signal analysis and Support Vector Machine classification.
Main Results:
- Achieved 99.63% accuracy in distinguishing C-peptide events from serum background.
- Demonstrated enhanced discriminative capability through comprehensive signal analysis.
- Validated the potential of the nanopore sensor for sensitive C-peptide identification.
Conclusions:
- Solid-state nanopore technology combined with machine learning offers a promising platform for diabetes diagnostics.
- The developed method enables rapid, sensitive, and portable detection of C-peptide.
- This technology supports early diabetes diagnosis and continuous biomarker monitoring.
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