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Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study

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Abstract

The development of an automatic method of identifying microplastic particles within live cells and organisms is crucial for high-throughput analysis of their biodistribution in toxicity studies. State-of-the-art technique in the data analysis tasks is the application of deep learning algorithms. Here, we propose the approach of polystyrene microparticle classification differing only in pigmentation using enhanced dark-field microscopy and a residual neural network (ResNet). The dataset consisting of 11,528 particle images has been collected to train and evaluate the neural network model. Human skin fibroblasts treated with microplastics were used as a model to study the ability of ResNet for classifying particles in a realistic biological experiment. As a result, the accuracy of the obtained classification algorithm achieved up to 93% in cell samples, indicating that the technique proposed will be a potent alternative to time-consuming spectral-based methods in microplastic toxicity research.

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Funding

The work was funded by the Russian Science Foundation grant 21–73-00097.

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  1. Bionanotechnology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kreml uramı 18, Kazan, Republic of Tatarstan, Russian Federation, 420008

    Ilnur Ishmukhametov, Läysän Nigamatzyanova, Gӧlnur Fakhrullina & Rawil Fakhrullin

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  1. Ilnur Ishmukhametov
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  2. Läysän Nigamatzyanova
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  3. Gӧlnur Fakhrullina
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  4. Rawil Fakhrullin
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Ishmukhametov, I., Nigamatzyanova, L., Fakhrullina, G. et al. Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study. Anal Bioanal Chem 414, 1297–1312 (2022). https://doi.org/10.1007/s00216-021-03749-y

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  • DOI: https://doi.org/10.1007/s00216-021-03749-y

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