Advertisement

Optical Memory and Neural Networks

, Volume 22, Issue 3, pp 156–165 | Cite as

Use of neural network algorithms for elaboration of fluorescent biosensors on the base of nanoparticles

  • S. A. BurikovEmail author
  • A. M. Vervald
  • I. I. Vlasov
  • S. A. Dolenko
  • K. A. Laptinskiy
  • T. A. Dolenko
Article

Abstract

In this paper, the results of application of artificial neural networks for extraction of fluorescence contribution of nanoparticles used in biomedicine as biomarkers and drug carriers against the fluorescence background of inherent fluorophores of biological objects are presented. Principle possibility of solving this problem is shown. The used architectures of ANN allow detecting fluorescence of carbon dots against the background of proper fluorescence of egg protein with reasonably high accuracy-not worse than 0.002 mg/mL.

Keywords

fluorescence carbon dots biomarkers autofluorescence artificial neural networks data aggregation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Evanko, D., The new fluorescent probes on the block, Nat. Methods, 2008, vol. 5, pp. 218–219.CrossRefGoogle Scholar
  2. 2.
    Hui, Y.Y., Cheng, C.L., and Chang, H.C., Nanodiamonds for optical bioimaging, J. Phys. D. Appl. Phys., 2010, vol. 43, pp. 374021–374031.CrossRefGoogle Scholar
  3. 3.
    Liu, J.-H., Yang, S.-T., Chen, X.-X., and Wang, H., Fluorescent carbon dots and nanodiamonds for biological imaging: preparation, application, pharmacokinetics and toxicity, Current Drug Metabolism, 2012, vol. 13, pp. 1046–1056.CrossRefGoogle Scholar
  4. 4.
    Biju, V., Itoh, T., Anas, A., Sujith, A., and Ishikawa, M., Semiconductor quantum dots and metal nanoparticles: syntheses, optical properties, and biological applications, Anal. Bioanal. Chem., 2008, vol. 391, pp. 2469–2495.CrossRefGoogle Scholar
  5. 5.
    Oleinikov, V.A., Sukhanova, A.V., and Nabiev, I.R., Fluorescent semiconductive nanocrystalls in biology and medicine, Russian Nanotechnologies, 2007, vol. 2, nos. 1–2, pp. 160–173.Google Scholar
  6. 6.
    Schrand, A.M., Huang, H.J., Carlson, C., Schlager, J.J., Osawa, E., Hussain, S.M., and Dai, L.M., Are diamond nanoparticles cytotoxic, J. Phys. Chem. B., 2007, vol. 111, pp. 2–7.CrossRefGoogle Scholar
  7. 7.
    Schrand, A.M., Hens, S.A.C, and Shenderova, O.A., Nanodiamond particles: properties and perspectives for bioapplications. Critical reviews in solid state and materials sciences, 2009, vol. 34, pp. 18–74.CrossRefGoogle Scholar
  8. 8.
    Nanodiamonds, applications in biology and nanoscale medicine, Ho, D., Ed., New York: Springer, 2009.Google Scholar
  9. 9.
    Haartman von E., Jiang, H., Khomich, A.A., Zhang, J., Burikov, S.A., Dolenko, T.A., Ruokolainen, J., Gu, H., Shenderova, O.A., Vlasov, I.I., and Rosenholm, J.M., Core-shell designs of photoluminescent nanodiamonds with porous silica coatings for bioimaging and drug delivery I: Fabrication, J. of Materials Chemistry B., 2013, vol. 1, no. 18, pp. 2358–2366.CrossRefGoogle Scholar
  10. 10.
    Prabhakar, N., Nareoja, T., Haartman von E., Karaman, D.S., Jiang, H., Koho, S., Dolenko, T.A., Hanninen, P., Vlasov, D.I., Ralchenko, V.G., Hosomi, S., Vlasov, I.I., Sahlgren, C., and Rosenholm, J.M., Core-shell designs of photoluminescent nanodiamonds with porous silica coatings for bioimaging and drug delivery II: Application, Nanoscale, 2013, vol. 5, no. 9, pp. 3713–3722.CrossRefGoogle Scholar
  11. 11.
    Rosenholm, J.M., Penninkangas, A., and Lindan, M., Amino-functionalization of large-pore mesoscopically ordered silica by a one-step hyperbranching polymerization of a surface-grown polyethyleneimine, Chem. Commun., 2006, vol. 37, pp. 3909–3911.CrossRefGoogle Scholar
  12. 12.
    Feofanov, A.V., Spectral laser scanning confocal microscopy in biology researches, Uspekhi biologicheskih nauk, 2007, vol. 47, pp. 371–410.Google Scholar
  13. 13.
    Keedwell, E. Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems, Wiley, 2005.CrossRefGoogle Scholar
  14. 14.
    Zagoruiko, N.G., Applied Methods of Analysis of Data and Knowledge, Novosibirsk: IM SD RAS, 1999 [in Russian].zbMATHGoogle Scholar
  15. 15.
    Gorban’, A.N., Dunin-Barkovskiy, V.L., et al., Neiroinformatics. Part 4. Terekhov S.A. Neural Network Based Information Models of Complex Engineering Systems, Novosibirsk: Nauka, SD RAS, 1998 [in Russian].Google Scholar
  16. 16.
    Li, M., Verma, B., Fan, X., and Tickle, K., RBF neural networks for solving the inverse problem of backscattering spectra, Neural Computing & Applications, 2008, vol. 17, no. 4, pp. 391–397.CrossRefGoogle Scholar
  17. 17.
    Yang, H. and Xu, M., Solving inverse bimodular problems via artificial neural network. Inverse Problems in Science and Engineering, 03 July 2009, pp. 1741–5977.Google Scholar
  18. 18.
    Neimark, Yu.I., Batalova, Z.S., et al., Pattern Recognition and Medical Diagnostics, Moscow: Nauka, 1972 [in Russian].Google Scholar
  19. 19.
    Zhang, R., Liu, Y., Yu, L., Li, Z., and Sun, S., Preparation of high-quality biocompatible carbon dots by extraction, with new thoughts on the luminescence mechanisms, Nanotechnology, 2013, vol. 24, no. 22, pp. 1–8.CrossRefGoogle Scholar
  20. 20.
    Cao, L., Wang, X., Meziani, M.J., Lu, F.S., Wang, H.F., Luo, P.J.G., Lin, Y., Harruff, B.A., Veca, L.M., Murray, D., et al., Carbon dots for multiphoton bioimaging, J. Am. Chem. Soc., 2007, vol. 129, pp. 11318–11319.CrossRefGoogle Scholar
  21. 21.
    Shenderova, O., Vlasov, I., Hens, S.A.C., and Borjanovic, V., Enhancement of photoluminescence of nanodiamond particles, US Patent Application, ITC: USA, 2010; p. 28.Google Scholar
  22. 22.
    Hens, S.C., Lawrence, W., Kumbhar, A.S., and Shenderov, O., Photoluminescent nanostructures from graphite oxidation, J. of Phys. Chem. C., 2012, vol. 116, pp. 20015–20022.CrossRefGoogle Scholar
  23. 23.
    Sun, X., Liu, Z., Welsher, K., Robinson, J.T., Goodwin, A., Zaric, S., and Dai, H., nano-graphene oxide for cellular imaging and drug delivery, Nano Res., 2008, vol. 1, pp. 203–212.CrossRefGoogle Scholar
  24. 24.
    Zellweger, M., Fluorescence spectroscopy of exogenous, exogenously-induced and endogenous fluorofores for the photodetection and photodynamic therapy of cancer. Lausanne, Fevrier, 2000.Google Scholar
  25. 25.
    Gerdova (Boichuk, I.V.) I.V., Dolenko, S.A., Dolenko, T.A., Churina, I.V., and Fadeev, V.V., New approaches solutions to inverse problems in laser spectroscopy involving artificial neural networks, Izvestiya Akademii Nauk. Seriya Fizicheskaya, 2002, vol. 66, no. 8, pp. 1116–1124.Google Scholar
  26. 26.
    Dolenko, S.A., Dolenko, T.A., Persiantsev, I.G., Fadeev, V.V., and Burikov, S.A., Solution of inverse problems of optical spectroscopy using of artificial neural networks, Neurocompuners: Development, application, 2005, nos. 1–2, pp. 89–97.Google Scholar
  27. 27.
    Dolenko, S.A., Neural network based methods of solution of inverse problems (Proc. XV Russian Scientific-Technical Conference “Neuroinformatics-2013”: Lectures on Neuroinformatics), Moscow, Moscow Engineering Physical Institute, 2013, pp. 214–269, ISBN 978-5-7262-1777-2.Google Scholar
  28. 28.
    Haykin, S., Neural Networks. A Comprehensive Foundation. Prentice Hall International, 1999, p. 842, ISBN 0139083855, 9780139083853.zbMATHGoogle Scholar
  29. 29.
    Specht, D., A general regression neural network, IEEE Trans. on Neural Networks, Nov. 1991, vol. 2, no. 6, pp. 568–576.CrossRefGoogle Scholar
  30. 30.
    Madala, H.R. and Ivakhnenko, A.G., Inductive Learning Algorithms for Complex Systems Modeling, CRC Press, 1994, p. 368, ISBN 0-8493-4438-7.zbMATHGoogle Scholar
  31. 31.
  32. 32.
    Dolenko, S., Dolenko, T., Burikov, S., Fadeev, V., Sabirov, A., and Persiantsev, I., Comparison of input data compression methods in neural network solution of inverse problem in laser raman spectroscopy of natural waters in Part II. Lecture Notes in Computer Science, Villa, A.E.P., et al., Eds., ICANN 2012, 2012, vol. 7553, pp. 443–450.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2013

Authors and Affiliations

  • S. A. Burikov
    • 1
    Email author
  • A. M. Vervald
    • 1
  • I. I. Vlasov
    • 2
  • S. A. Dolenko
    • 3
  • K. A. Laptinskiy
    • 1
  • T. A. Dolenko
    • 1
  1. 1.Physics DepartmentM.V. Lomonosov Moscow State UniversityMoscowRussia
  2. 2.A.M. Prokhorov General Physics InstituteRASMoscowRussia
  3. 3.D.V. Skobeltsyn Institute of Nuclear PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussia

Personalised recommendations