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

ABSTRACT:

Background

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 (d(opt)) exists for which the number (n(s)) 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 n(s) as a function of S and particle diameter (d), from which to eventually derive d(opt). Artificial neural networks are trained using data from flow chamber experime

SUBMITTER: Boso DP 

PROVIDER: S-EPMC3152469 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature