Using artificial neural networks for elaboration of fluorescence biosensors on the basis of nanoparticles
Abstract
In this study, the results for the solution of the pattern recognition problem are presented — extraction of fluorescence contribution for carbon dots used as biomarkers from the background signals of natural fluorophores and the determination of relative nanoparticle concentration. To solve this problem, artificial neural networks were used. The principal opportunity for solution of the given problem was demonstrated. The used architectures for neural networks allow the detection of carbon dot-based fluorescence within the background of native fluorescent egg protein with sufficiently high accuracy (not lower than 0.002 mg/ml).
Keywords
Supporting agencies: The authors of this paper wish to thank O. Shenderova from International Technology Center (North Carolina, USA) for the synthesis of CD. This work was supported in part by RFBR grants no. 12-01-31523-mol a, 11-05-01160 a, 12-01-00958 a and 11-02-01432 a, the grant of RAS program no. 24, the grant of President of the Russian Federation for leading scientific schools no 3076.2012.
About the Authors
S. A. Burikov
Moscow M. V. Lomonosov State University
Russian Federation
Russian Federation
Physics Department
Moscow
S. A. Dolenko
D. V. Skobeltsyn Institute of Nuclear Physics, Moscow State University
Russian Federation
Russian Federation
Moscow
K. A. Laptinskiy
Moscow M. V. Lomonosov State University
Russian Federation
Russian Federation
Physics Department
Moscow
I. V. Plastinin
Moscow M. V. Lomonosov State University
Russian Federation
Russian Federation
Physics Department
Moscow
A. M. Vervald
Moscow M. V. Lomonosov State University
Russian Federation
Russian Federation
Physics Department
Moscow
I. I. Vlasov
General Physics Institute RAS
Russian Federation
Russian Federation
Moscow
T. A. Dolenko
Moscow M. V. Lomonosov State University
Russian Federation
Russian Federation
Physics Department
Moscow
References
1. D. Ho (ed.). Nanodiamonds, Applications in Biology and Nanoscale Medicine. Springer, New York, 286 p. (2009).
2. J.-H. Liu, S.-T. Yang, X.-X. Chen, H. Wang. Fluorescent Carbon Dots and Nanodiamonds for Biological Imaging: Preparation, Application, Pharmacokinetics and Toxicity. Current Drug Metabolism, 13, P. 1046–1056 (2012).
3. A.M. Schrand, S.A.C. Hens, O.A. Shenderova. Nanodiamond Particles: Properties and Perspectives for Bioapplications. Critical Reviews in Solid State and Materials Sciences, 34, P. 18–74 (2009).
4. Y.Y. Hui, C.L. Cheng, H.C. Chang. Nanodiamonds for optical bioimaging. J. Phys. D: Appl. Phys., 43, P. 374021–374031 (2010).
5. C. Eggeling, J. Widengren, R. Rigler, C.A.M. Seidel. Photobleaching of Fluorescent Dyes under Conditions Used for Single-Molecule Detection: Evidence of Two-Step Photolysis. Anal. Chem., 70, P. 2651– 2659 (1998).
6. A.M. Scutaru, A. Kr¨uger, M. Wenzel, J. Richter, R. Gust. Investigations on the Use of Fluorescence Dyes for Labeling Dendrimers: Cytotoxicity, Accumulation Kinetics, and Intracellular Distribution. Bioconjugate Chem., 21, P. 2222–2226 (2010).
7. M. Haase, C.G. Hubner, F. Nolde, K. Mullen, T. Basche. Photoblinking and photobleaching of rylene diimide dyes. Phys.Chem.Chem.Phys., 13, P. 1776–1785 (2011).
8. V. Biju, T. Itoh, A. Anas, A. Sujith, M. Ishikawa. Semiconductor quantum dots and metal nanoparticles: syntheses, optical properties, and biological applications. Anal. Bioanal. Chem., 391, P. 2469–2495 (2008).
9. A.M. Schrand, H.J. Huang, C. Carlson, J.J. Schlager, E. Osawa, S.M. Hussain, L.M. Dai. Are diamond nanoparticles cytotoxic. J. Phys. Chem. B, 111, P. 2–7 (2007).
10. E. von Haartman, H. Jiang, A.A. Khomich, J. Zhang, S.A. Burikov, T.A. Dolenko, J. Ruokolainen, H. Gu, O.A. Shenderova, I.I. Vlasov, J.M. Rosenholm. Core-shell designs of photoluminescent nanodiamonds with porous silica coatings for bioimaging and drug delivery I: Fabrication. J. Mater. Chem. B, 1(18), P. 2358–2366 (2013).
11. N. Prabhakar, T. Nareoja, E. von Haartman, D.S. Karaman, H. Jiang, S. Koho, T.A. Dolenko, P. Hanninen, D.I. Vlasov, V.G. Ralchenko, S. Hosomi, I.I. Vlasov, C. Sahlgren, J.M. Rosenholm. Core-shell designs of photoluminescent nanodiamonds with porous silica coatings for bioimaging and drug delivery II: Application. Nanoscale, 5(9), P. 3713–3722 (2013).
12. M. Zellweger. Fluorescence spectroscopy of exogenous, exogenously- induced and endogenous fluorofores for the photodetection and photodynamic therapy of cancer. Thesis. Lausanne, Fevrier, 224 p. (2000).
13. R. Richards-Kortum, E. Sevick-Muraca. Quantitative Optical Spectroscopy for Tissue Diagnosis. Annu. Rev. Phys. Chem., 47, P. 555–606 (1996).
14. A.V. Feofanov. Spectral laser scanning confocal microscopy in biology researches. Uspekhi biologicheskih nauk, 47, P. 371–410 (2007) [in Russian].
15. J.M. Rosenholm, A. Penninkangas, M. Lindan. Amino-functionalization of large-pore mesoscopically ordered silica by a one-step hyperbranching polymerization of a surface-grown polyethyleneimine. Chem. Commun., 37, P. 3909–3911 (2006).
16. B.D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK, 415 p. (1995).
17. E. Keedwell, A. Narayanan Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems. John Wiley&Sons Ltd, Chichester, UK, 294 p. (2005).
18. Yu.I. Neimark, Z.S. Batalova, Y.G. Vasin, M.D. Breido. Pattern recognition and medical diagnostics. Nauka, Moscow, 328 p. (1972) [in Russian].
19. R. Zhang, Y. Liu, L. Yu, Z. Li, S. Sun. Preparation of high-quality biocompatible carbon dots by extraction, with new thoughts on the luminescence mechanisms. Nanotechnology, 24(22), P. 1-8 (2013).
20. L. Cao, X. Wang, M.J. Meziani, F.S. Lu, H.F. Wang, P.J.G. Luo, Y. Lin, B.A. Harruff, L.M. Veca, D. Murray, S.Y. Xie, Y.P. Sun. Carbon Dots for Multiphoton Bioimaging. J. Am. Chem. Soc., 129, P. 11318–11319 (2007).
21. S.C. Hens, W. Lawrence, A.S. Kumbhar, O. Shenderova. Photoluminescent Nanostructures from Graphite Oxidation. J. of Phys. Chem. C, 116, P. 20015–20022 (2012).
22. X. Sun, Z. Liu, K. Welsher, J.T. Robinson, A. Goodwin, S. Zaric, H. Dai. Nano-Graphene Oxide for Cellular Imaging and Drug Delivery. Nano Res., 1, P. 203–212 (2008).
23. D. Specht. A General Regression Neural Network. IEEE Trans. on Neural Networks, 2(6), P. 568–576 (1991).
24. I.V. Gerdova (Boichuk), S.A. Dolenko, T.A. Dolenko, I.V. Churina, V.V. Fadeev. New approaches to solution of inverse prolems of laser spectroscopy by the method of artificial neural networks. Izvestiya RAS (seriya fizicheskaya), 66(8), P. 1116–1124 (2002) [in Russian].
25. S.A. Dolenko, T.A. Dolenko, I.G. Persiantsev, V.V. Fadeev, S.A. Burikov. Solution of inverse problems of optical spectroscopy using of artificial neural networks. Neurocomputers: elaboration and application, 1-2, P. 89–97 (2005) [in Russian].
26. S.A. Dolenko. Neural network based methods of solution of inverse problems. Proc.“Neuroinformatics2013”: Lections on Neuroinformatics. Moscow, Moscow Engineering Physical Institute, P. 214–269 (2013) [in Russian]
27. H.R. Madala, A.G. Ivakhnenko. Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, 368 p. (1994).
28. http://www.neuroproject.ru/aboutproduct.php?info=ns2info
Review
For citations:
Burikov S.A., Dolenko S.A., Laptinskiy K.A., Plastinin I.V., Vervald A.M., Vlasov I.I., Dolenko T.A. Using artificial neural networks for elaboration of fluorescence biosensors on the basis of nanoparticles. Nanosystems: Physics, Chemistry, Mathematics. 2014;5(1):195-202.
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