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Advanced computational modeling for in vitro nanomaterial dosimetry

Overview of attention for article published in Particle and Fibre Toxicology, October 2015
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Mentioned by

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1 policy source

Citations

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133 Dimensions

Readers on

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96 Mendeley
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Title
Advanced computational modeling for in vitro nanomaterial dosimetry
Published in
Particle and Fibre Toxicology, October 2015
DOI 10.1186/s12989-015-0109-1
Pubmed ID
Authors

Glen M. DeLoid, Joel M. Cohen, Georgios Pyrgiotakis, Sandra V. Pirela, Anoop Pal, Jiying Liu, Jelena Srebric, Philip Demokritou

Abstract

Accurate and meaningful dose metrics are a basic requirement for in vitro screening to assess potential health risks of engineered nanomaterials (ENMs). Correctly and consistently quantifying what cells "see," during an in vitro exposure requires standardized preparation of stable ENM suspensions, accurate characterizatoin of agglomerate sizes and effective densities, and predictive modeling of mass transport. Earlier transport models provided a marked improvement over administered concentration or total mass, but included assumptions that could produce sizable inaccuracies, most notably that all particles at the bottom of the well are adsorbed or taken up by cells, which would drive transport downward, resulting in overestimation of deposition. Here we present development, validation and results of two robust computational transport models. Both three-dimensional computational fluid dynamics (CFD) and a newly-developed one-dimensional Distorted Grid (DG) model were used to estimate delivered dose metrics for industry-relevant metal oxide ENMs suspended in culture media. Both models allow simultaneous modeling of full size distributions for polydisperse ENM suspensions, and provide deposition metrics as well as concentration metrics over the extent of the well. The DG model also emulates the biokinetics at the particle-cell interface using a Langmuir isotherm, governed by a user-defined dissociation constant, K D, and allows modeling of ENM dissolution over time. Dose metrics predicted by the two models were in remarkably close agreement. The DG model was also validated by quantitative analysis of flash-frozen, cryosectioned columns of ENM suspensions. Results of simulations based on agglomerate size distributions differed substantially from those obtained using mean sizes. The effect of cellular adsorption on delivered dose was negligible for K D values consistent with non-specific binding (> 1 nM), whereas smaller values (≤ 1 nM) typical of specific high-affinity binding resulted in faster and eventual complete deposition of material. The advanced models presented provide practical and robust tools for obtaining accurate dose metrics and concentration profiles across the well, for high-throughput screening of ENMs. The DG model allows rapid modeling that accommodates polydispersity, dissolution, and adsorption. Result of adsorption studies suggest that a reflective lower boundary condition is appropriate for modeling most in vitro ENM exposures.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 2 2%
United States 1 1%
Unknown 93 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 21%
Researcher 16 17%
Student > Master 13 14%
Student > Postgraduate 6 6%
Professor > Associate Professor 5 5%
Other 14 15%
Unknown 22 23%
Readers by discipline Count As %
Environmental Science 14 15%
Chemistry 10 10%
Agricultural and Biological Sciences 9 9%
Biochemistry, Genetics and Molecular Biology 9 9%
Engineering 9 9%
Other 15 16%
Unknown 30 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2020.
All research outputs
#8,480,160
of 25,301,208 outputs
Outputs from Particle and Fibre Toxicology
#301
of 611 outputs
Outputs of similar age
#100,248
of 291,011 outputs
Outputs of similar age from Particle and Fibre Toxicology
#5
of 9 outputs
Altmetric has tracked 25,301,208 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 611 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 291,011 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.