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Updated: Sep 10, 2025

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
Published on: May 24, 2019
A Mn-O-Ce interface-engineered nanozyme platform for visual stratified exosome screening of lung cancer: Toward
Xin Xu1, Ting Ying2, Zhanghuan Yang1
1Department of Clinical Laboratory, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, 201399, China.
Abstract:
The development of portable exosome detection platforms is crucial for noninvasive early diagnosis of lung cancer, particularly in resource-limited settings. Herein, we engineered a paper-based colorimetric biosensor utilizing a MnO2/CeO2 heterojunction nanozyme for visual quantification of plasma exosomes. The heterojunction synergistically integrates the high surface area of CeO2 (45.689 m2/g) with the nanorod shaped MnO2, generating an interfacial electron transfer pathway that enhances peroxidase-like activity by 1.77 and 5.87 fold (Km = 1.197 mM, Vmax = 0.884 M/min) compared to individual components. Molecular recognition was achieved through dual-target immobilization: exosome-specific aptamers and anti-CD63/FGG/FGB antibodies conjugated on a chitosan-modified cellulose substrate. A smartphone-based hue-saturation-value (HSV) analysis system embedded in the platform enabled rapid on-site screening within 15 min, followed by microplate scanning of suspected high-risk samples, achieving a detection range of 102-108 particles/μL with an ultralow detection limit of ∼2 particles/μL. Integration of a logistic regression model incorporating CD63, FGG, and FGB absorbance values further improved classification robustness. Clinical validation using 194 plasma samples demonstrated exceptional diagnostic accuracy in discriminating malignant from benign pulmonary nodules (AUC = 0.996), with a 95 % confidence interval ranging from 0.797 to 1.05, with 98.51 % sensitivity and 95.52 % specificity. This work establishes a multimodal strategy combining heterojunction nanozyme catalysis, paper-based microfluidics, smartphone-assisted imaging, and machine learning, offering a scalable paradigm for liquid biopsy applications in primary healthcare.
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