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Mechanistic insight into the impact of nanomaterials on asthma and allergic airway disease

Overview of attention for article published in Particle and Fibre Toxicology, November 2017
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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3 tweeters

Citations

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30 Dimensions

Readers on

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59 Mendeley
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Title
Mechanistic insight into the impact of nanomaterials on asthma and allergic airway disease
Published in
Particle and Fibre Toxicology, November 2017
DOI 10.1186/s12989-017-0228-y
Pubmed ID
Authors

Kirsty Meldrum, Chang Guo, Emma L. Marczylo, Timothy W. Gant, Rachel Smith, Martin O. Leonard

Abstract

Asthma is a chronic respiratory disease known for its high susceptibility to environmental exposure. Inadvertent inhalation of engineered or incidental nanomaterials is a concern for human health, particularly for those with underlying disease susceptibility. In this review we provide a comprehensive analysis of those studies focussed on safety assessment of different nanomaterials and their unique characteristics on asthma and allergic airway disease. These include in vivo and in vitro approaches as well as human and population studies. The weight of evidence presented supports a modifying role for nanomaterial exposure on established asthma as well as the development of the condition. Due to the variability in modelling approaches, nanomaterial characterisation and endpoints used for assessment in these studies, there is insufficient information for how one may assign relative hazard potential to individual nanoscale properties. New developments including the adoption of standardised models and focussed in vitro and in silico approaches have the potential to more reliably identify properties of concern through comparative analysis across robust and select testing systems. Importantly, key to refinement and choice of the most appropriate testing systems is a more complete understanding of how these materials may influence disease at the cellular and molecular level. Detailed mechanistic insight also brings with it opportunities to build important population and exposure susceptibilities into models. Ultimately, such approaches have the potential to more clearly extrapolate relevant toxicological information, which can be used to improve nanomaterial safety assessment for human disease susceptibility.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Student > Bachelor 8 14%
Researcher 7 12%
Student > Master 6 10%
Professor 2 3%
Other 6 10%
Unknown 14 24%
Readers by discipline Count As %
Engineering 5 8%
Environmental Science 5 8%
Agricultural and Biological Sciences 5 8%
Immunology and Microbiology 4 7%
Medicine and Dentistry 4 7%
Other 20 34%
Unknown 16 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 November 2017.
All research outputs
#7,632,563
of 12,211,623 outputs
Outputs from Particle and Fibre Toxicology
#217
of 357 outputs
Outputs of similar age
#188,510
of 338,926 outputs
Outputs of similar age from Particle and Fibre Toxicology
#8
of 20 outputs
Altmetric has tracked 12,211,623 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 357 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 33rd percentile – i.e., 33% 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 338,926 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.