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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability
|
|---|---|
| Published in |
Journal of Translational Medicine, June 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 Brett 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 22% |
| Student > Postgraduate | 5 | 16% |
| Other | 3 | 9% |
| Student > Master | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Other | 7 | 22% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 22% |
| Medicine and Dentistry | 6 | 19% |
| Agricultural and Biological Sciences | 4 | 13% |
| Computer Science | 3 | 9% |
| Engineering | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 8 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 08 March 2015.
All research outputs
#16,720,137
of 25,371,288 outputs
Outputs from Journal of Translational Medicine
#2,335
of 4,634 outputs
Outputs of similar age
#139,954
of 242,710 outputs
Outputs of similar age from Journal of Translational Medicine
#38
of 86 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,634 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 43rd percentile – i.e., 43% 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 242,710 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 86 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 50% of its contemporaries.