Dataset Information

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 experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for n(s) 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.

SUBMITTER: Boso DP

PROVIDER: S-EPMC3152469 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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

Boso Daniela P DP Lee Sei-Young SY Ferrari Mauro M Schrefler Bernhard A BA Decuzzi Paolo P

International journal of nanomedicine 20110719

<h4>Background</h4>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 ...[more]

PMID: 21845041

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Project description:Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.

Dataset Information

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

Publications

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

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