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

International Journal of Nanomedicine

Volumn 6, Issue 1, 2011, Pages 1517-1526

  Boso, Daniela P ^a   Lee, Sei Young ^b   Ferrari, Mauro ^c   Schrefler, Bernhard A ^a   Decuzzi, Paolo ^c  

^a UNIVERSITY OF PADOVA   (Italy)

^b SAMSUNG Electronics   (South Korea)

^c HOUSTON METHODIST RESEARCH INSTITUTE   (United States)

Author keywords

Artificial neural networks; Laminar flow; Nanoparticle; Optimal configuration; Vascular adhesion; Wall shear rate

Indexed keywords

NANOPARTICLE;

ACCURACY; ADHESION; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FLUORESCENCE MICROSCOPY; HYDRODYNAMICS; IN VIVO STUDY; LAMINAR FLOW; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MICROANGIOPATHY; NANOFABRICATION; PARTICLE SIZE; PREDICTION; PROBABILITY; PROCESS OPTIMIZATION; SHEAR RATE; SYSTEMATIC ERROR; BIOLOGY; DIAGNOSTIC IMAGING; DRUG DELIVERY SYSTEM; MICROVASCULATURE; THEORETICAL MODEL;

ADHESIVENESS; COMPUTATIONAL BIOLOGY; DIAGNOSTIC IMAGING; DRUG DELIVERY SYSTEMS; MICROVESSELS; MODELS, THEORETICAL; NANOPARTICLES; NEURAL NETWORKS (COMPUTER); PARTICLE SIZE;

EID: 84862833470     PISSN: 11769114     EISSN: 11782013     Source Type: Journal    
DOI: 10.2147/IJN.S20283     Document Type: Article

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