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Smart microfluidic device for automated exosome quantification and classification using deep learning
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DOI：10.1016/j.snb.2025.137919.png)
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Abstract
En 中文
Microfluidic resistive pulse sensing offers a streamlined alternative for detecting and analyzing microparticles across various fields, including environmental science, chemistry, biomedicine, and disease diagnostics. This study introduces a novel microfluidic platform designed to analyze and characterize the physical properties of cancer-derived exosomes through automated, efficient analysis. The platform employs capillary-and vacuum-chamber-assisted passive-driven fluid injection, enabling fully automated operation. To ensure precise measurements, the platform incorporates hydrodynamic particle focusing without the need for a sheath and utilizes a reference gate to minimize noise during resistive pulse detection. The proposed microfluidic chip accurately measures the size, concentration, and zeta potential of exosomes derived from MCF-7, MDA-MB-231, and MCF-10A cell lines with high sensitivity. Furthermore, the microfluidic platform is fabricated through a cost-effective soft lithography method. Moreover, a deep learning-based model is applied to classify and distinguish exosomes based on the collected data, achieving an impressive accuracy of 96.6 %. The microfluidic platform combines high sensitivity with advanced classification capabilities. Moreover, the proposed microfluidic devices' compact and minimal operation advances Lab on a Chip for practical applications.
Keywords:
Microfluidics
Exosome analysis
Resistive pulse sensing
Lab on a Chip
Deep neural network
Journal
IF:
7.7
Papers:
3.4W
Citations:
12.6W
Organization
Cited Papers
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