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A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability

Overview of attention for article published in Journal of Translational Medicine, January 2014
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Mentioned by

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1 tweeter

Citations

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18 Dimensions

Readers on

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25 Mendeley
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Title
A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability
Published in
Journal of Translational Medicine, January 2014
DOI 10.1186/1479-5876-12-185
Pubmed ID
Authors

Héctor De León, Stéphanie Boué, Walter K Schlage, Natalia Boukharov, Jurjen W Westra, Stephan Gebel, Aaron VanHooser, Marja Talikka, R Fields, Emilija Veljkovic, Michael J Peck, Carole Mathis, Vy Hoang, Carine Poussin, Renee Deehan, Katrin Stolle, Julia Hoeng, Manuel C Peitsch

Abstract

Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of "omics" data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Student > Postgraduate 4 16%
Other 3 12%
Student > Master 3 12%
Professor 2 8%
Other 7 28%
Unknown 1 4%
Readers by discipline Count As %
Medicine and Dentistry 6 24%
Biochemistry, Genetics and Molecular Biology 5 20%
Agricultural and Biological Sciences 4 16%
Computer Science 3 12%
Engineering 2 8%
Other 2 8%
Unknown 3 12%

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 26 June 2014.
All research outputs
#13,300,527
of 15,060,518 outputs
Outputs from Journal of Translational Medicine
#2,556
of 2,858 outputs
Outputs of similar age
#155,673
of 189,181 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 1 outputs
Altmetric has tracked 15,060,518 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,858 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 1st percentile – i.e., 1% 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 189,181 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them