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Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials

Overview of attention for article published in Particle and Fibre Toxicology, May 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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2 policy sources
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1 X user

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63 Mendeley
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Title
Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials
Published in
Particle and Fibre Toxicology, May 2016
DOI 10.1186/s12989-016-0137-5
Pubmed ID
Authors

Jake Nikota, Andrew Williams, Carole L. Yauk, Håkan Wallin, Ulla Vogel, Sabina Halappanavar

Abstract

The increasing use of engineered nanomaterials (ENMs) of varying physical and chemical characteristics poses a great challenge for screening and assessing the potential pathology induced by these materials, necessitating novel toxicological approaches. Toxicogenomics measures changes in mRNA levels in cells and tissues following exposure to toxic substances. The resulting information on altered gene expression profiles, associated pathways, and the doses at which these changes occur, are used to identify the underlying mechanisms of toxicity and to predict disease outcomes. We evaluated the applicability of toxicogenomics data in identifying potential lung-specific (genomic datasets are currently available from experiments where mice have been exposed to various ENMs through this common route of exposure) disease outcomes following exposure to ENMs. Seven toxicogenomics studies describing mouse pulmonary responses over time following intra-tracheal exposure to increasing doses of carbon nanotubes (CNTs), carbon black, and titanium dioxide (TiO2) nanoparticles of varying properties were examined to understand underlying mechanisms of toxicity. mRNA profiles from these studies were compared to the publicly available datasets of 15 other mouse models of lung injury/diseases induced by various agents including bleomycin, ovalbumin, TNFα, lipopolysaccharide, bacterial infection, and welding fumes to delineate the implications of ENM-perturbed biological processes to disease pathogenesis in lungs. The meta-analysis revealed two distinct clusters-one driven by TiO2 and the other by CNTs. Unsupervised clustering of the genes showing significant expression changes revealed that CNT response clustered with bleomycin injury and bacterial infection models, both of which are known to induce lung fibrosis, in a post-exposure-time dependent manner, irrespective of the CNT's physical-chemical properties. TiO2 samples clustered separately from CNTs and disease models. These results indicate that in the absence of apical toxicity data, a tiered strategy beginning with short term, in vivo tissue transcriptomics profiling can effectively and efficiently screen new ENMs that have a higher probability of inducing pulmonary pathogenesis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 62 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Ph. D. Student 11 17%
Student > Master 11 17%
Other 5 8%
Student > Bachelor 4 6%
Other 7 11%
Unknown 11 17%
Readers by discipline Count As %
Environmental Science 7 11%
Pharmacology, Toxicology and Pharmaceutical Science 7 11%
Agricultural and Biological Sciences 6 10%
Medicine and Dentistry 6 10%
Computer Science 4 6%
Other 17 27%
Unknown 16 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 11 December 2023.
All research outputs
#4,858,568
of 25,463,724 outputs
Outputs from Particle and Fibre Toxicology
#178
of 615 outputs
Outputs of similar age
#72,418
of 324,102 outputs
Outputs of similar age from Particle and Fibre Toxicology
#6
of 21 outputs
Altmetric has tracked 25,463,724 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 615 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has gotten more attention than average, scoring higher than 69% of its peers.
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 324,102 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 21 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 71% of its contemporaries.