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Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling

Overview of attention for article published in BMC Bioinformatics, February 2013
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Title
Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-41
Pubmed ID
Authors

Yun-feng Qi, Yan-xin Huang, Hong-yan Wang, Yu Zhang, Yong-li Bao, Lu-guo Sun, Yin Wu, Chun-lei Yu, Zhen-bo Song, Li-hua Zheng, Ying Sun, Guan-nan Wang, Yu-xin Li

Abstract

Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 23%
Student > Ph. D. Student 11 18%
Student > Bachelor 9 15%
Student > Postgraduate 6 10%
Student > Doctoral Student 4 7%
Other 13 21%
Unknown 4 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 38%
Agricultural and Biological Sciences 10 16%
Mathematics 5 8%
Immunology and Microbiology 5 8%
Medicine and Dentistry 4 7%
Other 8 13%
Unknown 6 10%
Attention Score in Context

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 06 February 2013.
All research outputs
#20,180,477
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#6,827
of 7,254 outputs
Outputs of similar age
#249,676
of 282,949 outputs
Outputs of similar age from BMC Bioinformatics
#128
of 135 outputs
Altmetric has tracked 22,694,633 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 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 282,949 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 135 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.