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Updated: Jul 13, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
Published on: May 10, 2024
Smartphone-integrated tri-mode RCA-CRISPR/Cas12a biosensor with Fe3O4@Au nanozyme for on-site detection of sugarcane
Rongshuai Che1, Danyao Tang1, Bingtao Fu1
1Education Department of Guangxi Zhuang Autonomous Region, Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Engineering Research Center of Low-carbon and High-quality Utilization of Forest Biomass, Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning, 530006, China.
Abstract:
The devastating sugarcane smut causes up to 70 % sugar yield loss and secondary infections, but field-deployable diagnostics remain challenging due to the limitations of lab-dependent methods. Herein, we report a portable CRISPR/Cas12a-powered biosensor integrated with tri-functional Fe3O4@Au nanozymes and triple-modal signal readout for precise and on-site pathogen detection. By synergizing rolling circle amplification (RCA) with CRISPR/Cas12a trans-cleavage activity, the system achieves ultrasensitive target recognition (detection limit: 32.11 aM for electrochemical mode). The Fe3O4@Au@GOD bioconjugates simultaneously enables magnetic separation, optimizes GOD-mediated colorimetric signals (visual LOD: 49.28 fM), and enhances photothermal responses (LOD: 42.17 fM) via precise biocatalyst-catalyzed TMB oxidation. A smartphone-coupled 3D-printed device integrates electrochemical, colorimetric, and photothermal detection modes, providing cross-validated results that eliminate false positives in complex matrices (recovery: 98-104 %). This field-portable platform completes detection within 2.5-4.5 h (80 % cost reduction vs. qPCR) and demonstrates high specificity against non-target pathogens. The fusion of nanozyme engineering, CRISPR amplification, and multi-modal sensing offers a transformative tool for precision agriculture.
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