Original ArticleA method for optical imaging and monitoring of the excretion of fluorescent nanocomposites from the body using artificial neural networks
Graphical Abstract
The new express method for solving the problem of optical visualization of nanocomposites in biological medium based on application of neural networks and fluorescence signal. Developed method provides information on the presence and quantity of nanocomposites and its components and is applicable for solution of such a problem with any fluorescent nanoparticles.
Section snippets
Materials characterization
Graphene oxide nanoparticles (nGO) are products of the chemical reactions of intercalation, delamination and oxidation of 400 nm nanographite plates (Nanostructured&Amorphous Materials Inc., Houston, TX) in a 3:1 mixture of sulfuric and nitric acids.48 After evaporation of the acids from the supernatant (at 350°C), the nGO pellet was washed and dispersed in deionized water.
mPEG (molecular weight 5000), PEI (molecular weight 25000), hexamethylene diisocyanate (> 99%), folic acid ligands (FA,
Preparation and characterization of synthesized nanocomposites and their suspensions
Synthesized nGO was coated with PEG-PEI copolymers conjugated to FA by methods developed by the authors in Ref.14, 51 (Figure 2). As a result, nGO+Cop+FA nanocomposites were obtained. Owing to the copolymer, the resulting nanoparticles are highly dispersible, biocompatible and able to carry molecular agents (FA) on their surfaces.14
FA was chosen as ligand for the nanocomposites due to the following reason. FA is necessary for growth of new cells, including oncological ones.52 Therefore, tumors
Discussion
The obtained results of the ANN application to the problem of monitoring the excretion of nanocomposites and their components from the organism with urine showed the wide possibilities of ANN methods. Several approaches can be distinguished even to the specific problem stated in this study:
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If it is necessary to perform a quick estimation of the possibilities to determine the concentrations of all classes of nanoparticles in urine, it is advisable to use one ANN architecture that demonstrates
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Support from: This study has been performed at the expense of the grant of Russian Science Foundation (project no. 17-12-01481): O.S., S.B., K.L., T.D.: measurement of sizes and fluorescence quantum yields of nanocomposites, conducting experiments, processing spectra; and at the expense of the grant of Russian Science Foundation (project no. 14-11-00579): I.I., S.D.: data pre-processing, design and methodology of application of ANN. Partial funding from the Academy of Finland (project# 284542, 309374) is also acknowledged (N.P., D.S.K., J.M.R.: synthesis and characterization of nanocomposites).
The authors declare no conflicts of interests.