Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks

Daniela P Boso1, Sei-Young Lee2, Mauro Ferrari3, Bernhard A Schrefler1, Paolo Decuzzi31Department of Structural and Transportation Engineering, University of Padova, Padova, Italy; 2Global Production Technology Center, Samsung Electronics Co Ltd, Republic of Korea; 3The Methodist Hospital Research I...

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Autores principales:	Schrefler BA, Ferrari M, Lee S, Boso DP, Decuzzi P
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2011
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/811560c5597b4e789d3254da72839350
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spelling	oai:doaj.org-article:811560c5597b4e789d3254da728393502021-12-02T00:39:11ZOptimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks1176-91141178-2013https://doaj.org/article/811560c5597b4e789d3254da728393502011-07-01T00:00:00Zhttp://www.dovepress.com/optimizing-particle-size-for-targeting-diseased-microvasculature-from--a7902https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013Daniela P Boso1, Sei-Young Lee2, Mauro Ferrari3, Bernhard A Schrefler1, Paolo Decuzzi31Department of Structural and Transportation Engineering, University of Padova, Padova, Italy; 2Global Production Technology Center, Samsung Electronics Co Ltd, Republic of Korea; 3The Methodist Hospital Research Institute, Department of Nanomedicine and Biomedical Engineering, Houston, TX, USABackground: Nanoparticles with different sizes, shapes, and surface properties are being developed for the early diagnosis, imaging, and treatment of a range of diseases. Identifying the optimal configuration that maximizes nanoparticle accumulation at the diseased site is of vital importance. In this work, using a parallel plate flow chamber apparatus, it is demonstrated that an optimal particle diameter (dopt) exists for which the number (ns) of nanoparticles adhering to the vessel walls is maximized. Such a diameter depends on the wall shear rate (S). Artificial neural networks are proposed as a tool to predict ns as a function of S and particle diameter (d), from which to eventually derive dopt. Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for ns and its complex functional dependence on d and S. This demonstrates that artificial neural networks can be used effectively to minimize the number of experiments needed without compromising the accuracy of the study. A similar procedure could potentially be used equally effectively for in vivo analysis.Keywords: nanoparticle, optimal configuration, vascular adhesion, laminar flow, wall shear rate, artificial neural networksSchrefler BAFerrari MLee SBoso DPDecuzzi PDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2011, Iss default, Pp 1517-1526 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Schrefler BA Ferrari M Lee S Boso DP Decuzzi P Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
description	Daniela P Boso1, Sei-Young Lee2, Mauro Ferrari3, Bernhard A Schrefler1, Paolo Decuzzi31Department of Structural and Transportation Engineering, University of Padova, Padova, Italy; 2Global Production Technology Center, Samsung Electronics Co Ltd, Republic of Korea; 3The Methodist Hospital Research Institute, Department of Nanomedicine and Biomedical Engineering, Houston, TX, USABackground: Nanoparticles with different sizes, shapes, and surface properties are being developed for the early diagnosis, imaging, and treatment of a range of diseases. Identifying the optimal configuration that maximizes nanoparticle accumulation at the diseased site is of vital importance. In this work, using a parallel plate flow chamber apparatus, it is demonstrated that an optimal particle diameter (dopt) exists for which the number (ns) of nanoparticles adhering to the vessel walls is maximized. Such a diameter depends on the wall shear rate (S). Artificial neural networks are proposed as a tool to predict ns as a function of S and particle diameter (d), from which to eventually derive dopt. Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for ns and its complex functional dependence on d and S. This demonstrates that artificial neural networks can be used effectively to minimize the number of experiments needed without compromising the accuracy of the study. A similar procedure could potentially be used equally effectively for in vivo analysis.Keywords: nanoparticle, optimal configuration, vascular adhesion, laminar flow, wall shear rate, artificial neural networks
format	article
author	Schrefler BA Ferrari M Lee S Boso DP Decuzzi P
author_facet	Schrefler BA Ferrari M Lee S Boso DP Decuzzi P
author_sort	Schrefler BA
title	Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
title_short	Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
title_full	Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
title_fullStr	Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
title_full_unstemmed	Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
title_sort	optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks
publisher	Dove Medical Press
publishDate	2011
url	https://doaj.org/article/811560c5597b4e789d3254da72839350
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Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks

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