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Decoding Optical Spectra with Neural Networks to Monitor the Elimination of Carbon Nanoagents from the Body

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Abstract

This study is devoted to the problem of controlling the removal of carbon nanocomplexes from the body via neural networks. We model the renal excretion of nanocomplexes based on carbon dots (CD) that deliver anticancer drug—doxorubicin (Dox). Optical absorption and fluorescence spectra of nanocomposites containing CD and Dox in urine were used to model the process of elimination of nanocomposites components from the body. These spectral data were decoded with multilayer perceptrons to determine CD and Dox concentrations in the studied samples. To increase the accuracy of monitoring the excretion of CD and Dox with urine, principal component analysis was additionally used. Our sensing technique allows controlling the excretion of CD and Dox with 43.8 and 20.9 µg/L mean absolute errors, which are comparable to similar values of analogues. However, the proposed method can be used for simultaneous rapid monitoring of numerous substances, while most CD-based sensors in literature are calibrated to measure only one or two parameters. The obtained results have great value for the analysis of the parameters of nanostructured objects in microvolumes, particularly for monitoring in microfluidic devices. Combination of microfluidics and the use of machine learning methods shows a future innovative approach for conduction massive biological material study and implement smart decision making systems in support of clinical diagnostics.

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Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation, agreement no. 075-15-2021-1363, contract no. 210EΠ from 29 November 2021.

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Authors and Affiliations

  1. Faculty of Physics, Lomonosov Moscow State University, 119991, Moscow, Russia

    O. Sarmanova, S. Burikov & T. Dolenko

  2. Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991, Moscow, Russia

    K. Laptinskiy & S. Dolenko

  3. Biomedical Engineering Department, First Moscow State Medical University (Sechenov University), 119435, Moscow, Russia

    D. Trushina

Authors
  1. O. Sarmanova
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  2. K. Laptinskiy
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  3. S. Burikov
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  4. S. Dolenko
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  5. D. Trushina
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  6. T. Dolenko
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Correspondence to O. Sarmanova.

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Sarmanova, O., Laptinskiy, K., Burikov, S. et al. Decoding Optical Spectra with Neural Networks to Monitor the Elimination of Carbon Nanoagents from the Body. Opt. Mem. Neural Networks 31, 256–265 (2022). https://doi.org/10.3103/S1060992X22030109

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