ArticleBiomedical optics express2023
Three-dimensional deep regression-based light scattering imaging system for nanoscale exosome analysis.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.Biomedical optics express · 2024Article
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Authors and funding
4 authors.
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Abstract
Exosomes are extracellular vesicles that serve as promising intrinsic nanoscale biomarkers for disease diagnosis and treatment. Nanoparticle analysis technology is widely used in the field of exosome study. However, the common particle analysis methods are usually complex, subjective, and not robust. Here, we develop a three-dimensional (3D) deep regression-based light scattering imaging system for nanoscale particle analysis. Our system solves the problem of object focusing in common methods and acquires light scattering images of label-free nanoparticles as small as 41 nm in diameter. We develop a new method for nanoparticle sizing with 3D deep regression, where the 3D time series Brownian motion data of single nanoparticles are input as a whole, and sizes are output automatically for both entangled and untangled nanoparticles. Exosomes from the normal and cancer liver cell lineage cells are observed and automatically differentiated by our system. The 3D deep regression-based light scattering imaging system is expected to be widely used in the field of nanoparticle analysis and nanomedicine.
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