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Mendeley readers
Attention Score in Context
| Title |
A Generally Applicable Translational Strategy Identifies S100A4 as a Candidate Gene in Allergy
|
|---|---|
| Published in |
Science Translational Medicine, January 2014
|
| DOI | 10.1126/scitranslmed.3007410 |
| Pubmed ID | |
| Authors |
Sören Bruhn, Yu Fang, Fredrik Barrenäs, Mika Gustafsson, Huan Zhang, Aelita Konstantinell, Andrea Krönke, Birte Sönnichsen, Anne Bresnick, Natalya Dulyaninova, Hui Wang, Yelin Zhao, Jörg Klingelhöfer, Noona Ambartsumian, Mette K. Beck, Colm Nestor, Elsa Bona, Zou Xiang, Mikael Benson |
| Abstract |
The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (TH2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 20% |
| Student > Ph. D. Student | 7 | 12% |
| Student > Master | 7 | 12% |
| Professor | 6 | 10% |
| Other | 5 | 8% |
| Other | 15 | 25% |
| Unknown | 8 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 16 | 27% |
| Agricultural and Biological Sciences | 15 | 25% |
| Medicine and Dentistry | 7 | 12% |
| Immunology and Microbiology | 4 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 2 | 3% |
| Unknown | 14 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 18 January 2016.
All research outputs
#4,684,911
of 22,738,543 outputs
Outputs from Science Translational Medicine
#3,247
of 5,098 outputs
Outputs of similar age
#56,860
of 304,743 outputs
Outputs of similar age from Science Translational Medicine
#53
of 79 outputs
Altmetric has tracked 22,738,543 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,098 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 81.1. This one is in the 36th percentile – i.e., 36% 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 304,743 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 79% of its contemporaries.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.