High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification

Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. This study explores a novel detection methodology using solid-state nanopore technology coupled with machine learning for the sensitive identification of C-peptide molecules. Solid-state nanopores were fabricated <i>via</i> focused ion beam milling and systematically tested to analyze ionic current blockade characteristics of C-peptide and fetal bovine serum during its translocation. A comprehensive five-dimensional signal analysis incorporating current blockade amplitude, dwell time, standard deviation, kurtosis, and skewness significantly enhanced the discriminative capability of the nanopore sensor. Employing Support Vector Machine classification with a radial basis function kernel, the proposed platform achieved an outstanding identification accuracy of 99.63% for distinguishing C-peptide events from serum background. These results highlight the potential of solid-state nanopore technology integrated with advanced machine learning as a rapid, sensitive, and portable platform for early diabetes diagnostics and continuous biomarker monitoring.