Reticular-Induced Energy Transfer Driven Renewable ECL System with Machine Learning for Glioma-Specific Dual-Biomarker Detection and Expression Correlation Mechanism

system, enabling ultrasensitive detection. Building on this advancement, we constructed an automated and renewable ECL biosensor for sequential analysis of dopamine (DA) and miRNA-21. Also, this automated bioassay was applied to elucidate the correlation of DA depletion with miRNA-21 upregulation in glioma and the expression mechanism, achieving glioma staging. To enhance staging performance, a logistic regression-based machine learning algorithm was utilized, showing 100% accuracy in classifying healthy controls and low- and high-grade cases. This work provides instructive insights into development of next-generation renewable ECL biosensors and data algorithm models, paving the way for early glioma diagnostics, tumor staging, and pathogenesis research.