Predicting in situ nanoparticle behavior using multiple particle tracking and artificial neural networks
| Autor: | Alex Choe, Hugo Ferreira Pontes, Elizabeth Nance, Michael McKenna, Chad Curtis, Dorsa Toghani |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
In situ
Artificial neural network Mathematical model Computer science Nanoparticle 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Tracking (particle physics) 01 natural sciences Article 0104 chemical sciences Brain region Trajectory Particle General Materials Science 0210 nano-technology Biological system |
| Zdroj: | Nanoscale |
| Popis: | Predictive models of nanoparticle transport can drive design of nanotherapeutic platforms to overcome biological barriers and achieve localized delivery. In this paper, we demonstrate the ability of artificial neural networks to predict both nanoparticle properties, such as size and protein adsorption, and aspects of the brain microenvironment, such as cell internalization, viscosity, and brain region by using large (>100 000) trajectory datasets collected via multiple particle tracking in in vitro gel models of the brain and cultured organotypic brain slices. Our neural network achieved a 0.75 recall score when predicting gel viscosity based on trajectory datasets, compared to 0.49 using an obstruction scaling model. When predicting in situ nanoparticle size based on trajectory datasets, neural networks achieved a 0.90 recall score compared to 0.83 using an optimized Stokes–Einstein predictor. To distinguish between nanoparticles of different sizes in more complex nanoparticle mixtures, our neural network achieved up to a recall score of 0.85. Even in cases of more nuanced output variables where mathematical models are not available, such as protein adhesion, neural networks retained the ability to distinguish between particle populations (recall score of 0.89). These findings demonstrate how trajectory datasets in combination with machine learning techniques can be used to characterize the particle-microenvironment interaction space. |
| Databáze: | OpenAIRE |
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