Olga Sarmanova

Monitoring of the excretion of fluorescent nanocomposites out of the body using artificial neural networks

In this study we propose a new approach to monitoring of the removal of luminescent nanocomposites and their components with urine using artificial neural networks. A complex multiparametric problem of optical imaging of synthesized nanocomposites - nanometer graphene oxides, covered by the poly(ethylene imine)–poly(ethylene glycol) copolymer and by the folic acid in a biomaterial is solved. The proposed method is applicable for optical imaging of any fluorescent nanoparticles used as imaging nanoagents in biological tissue.

A method for optical imaging and monitoring of the excretion of fluorescent nanocomposites from the body using artificial neural networks

In this study, a new approach to the implementation of optical imaging of fluorescent nanoparticles in a biological medium using artificial neural networks is proposed. The studies were carried out using new synthesized nanocomposites - nanometer graphene oxides, covered by the poly(ethylene imine)-poly(ethylene glycol) copolymer and by the folic acid. We present an example of a successful solution of the problem of monitoring the removal of nanocomposites based on nGO and their components with urine using fluorescent spectroscopy and artificial neural networks. However, the proposed method is applicable for optical imaging of any fluorescent nanoparticles used as theranostic agents in biological tissue.

Neural Network Classification Method for Solution of the Problem of Monitoring Theremoval of the Theranostics Nanocomposites from an Organism

In this study artificial neural networks were used for elaboration of the new method of monitoring of excreted nanocomposites-drug carriers and their components in human urine by their fluorescence spectra. The problem of classification of nanocomposites consisting of fluorescence carbon dots covered by copolymers and ligands of folic acid in urine was solved. A set of different architectures of neural networks and 4 alternative procedures of the selection of significant input features: by cross-correlation, cross-entropy, standard deviation and by analysis of weights of a neural network were used. The best solution of the problem of classification of nanocomposites and their components in urine provides the perceptron with 8 neurons in a single hidden layer, trained on a set of significant input features selected using cross-correlation. The percentage of correct recognition averaged over all five classes, is 72.3%.

Determination of type and concentration of DNA nitrogenous bases by Raman spectroscopy using artificial neural networks

In this article the results of solution of two-parametrical inverse problems of laser Raman spectroscopy of identification and determination of concentration of DNA nitrogenous bases in two-component solutions are presented. Elaboration of methods of control of reactions with DNA strands in remote real-time mode is necessary for solution of one of the basic problems of creation of biocomputers – increase of reliability of molecular DNA-computations. The comparative analysis of two used methods of solution of stated problems has demonstrated convincing advantages of technique of artificial neural networks. Use of artificial neural networks allowed to reach the accuracy of determination of concentration of each base in two-component solutions 0.2-0.3 g/l.

Adaptive methods of solving inverse problems for improvement of fidelity of molecular DNA computations

Elaboration of methods of monitoring of biochemical reactions with DNA strands is necessary to solve one of the main problems in creation of biocomputers—improvement of fidelity of molecular DNA computations. In this paper, the results of solution of inverse two-parameter problems of laser Raman spectroscopy on determination of the types and concentration of DNA nitrogenous bases in multicomponent solutions are presented. Comparative analysis of the three used methods of solving these problems has demonstrated convincing advantages of artificial neural networks and of the method of projection to latent structures. Use of adaptive methods allowed achieving the accuracy of determining the concentration of each base in two-component solutions about 0.2–0.4 g/L.

Improvement of the fidelity of molecular DNA computations: control of DNA duplex melting using Raman spectroscopy

In this study it is demonstrated that use of laser Raman spectroscopy for monitoring biochemical reactions provides the detection and control of the processes of renaturation and denaturation of DNA strands, the determination of state of strands, and also the control of possible mutations in DNA molecules. The obtained results are very promising to improve the fidelity of DNA computations, i.e. to provide the greater convergence of the estimated and exact values.