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Immune cell subset differentiation and tissue inflammation

Overview of attention for article published in Journal of Hematology & Oncology, July 2018
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 Wikipedia page

Citations

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119 Dimensions

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172 Mendeley
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Title
Immune cell subset differentiation and tissue inflammation
Published in
Journal of Hematology & Oncology, July 2018
DOI 10.1186/s13045-018-0637-x
Pubmed ID
Authors

Pu Fang, Xinyuan Li, Jin Dai, Lauren Cole, Javier Andres Camacho, Yuling Zhang, Yong Ji, Jingfeng Wang, Xiao-Feng Yang, Hong Wang

Abstract

Immune cells were traditionally considered as major pro-inflammatory contributors. Recent advances in molecular immunology prove that immune cell lineages are composed of different subsets capable of a vast array of specialized functions. These immune cell subsets share distinct duties in regulating innate and adaptive immune functions and contribute to both immune activation and immune suppression responses in peripheral tissue. Here, we summarized current understanding of the different subsets of major immune cells, including T cells, B cells, dendritic cells, monocytes, and macrophages. We highlighted molecular characterization, frequency, and tissue distribution of these immune cell subsets in human and mice. In addition, we described specific cytokine production, molecular signaling, biological functions, and tissue population changes of these immune cell subsets in both cardiovascular diseases and cancers. Finally, we presented a working model of the differentiation of inflammatory mononuclear cells, their interaction with endothelial cells, and their contribution to tissue inflammation. In summary, this review offers an updated and comprehensive guideline for immune cell development and subset differentiation, including subset characterization, signaling, modulation, and disease associations. We propose that immune cell subset differentiation and its complex interaction within the internal biological milieu compose a "pathophysiological network," an interactive cross-talking complex, which plays a critical role in the development of inflammatory diseases and cancers.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 172 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 172 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 20%
Student > Master 22 13%
Student > Bachelor 22 13%
Researcher 17 10%
Student > Doctoral Student 12 7%
Other 17 10%
Unknown 48 28%
Readers by discipline Count As %
Immunology and Microbiology 33 19%
Biochemistry, Genetics and Molecular Biology 28 16%
Medicine and Dentistry 23 13%
Agricultural and Biological Sciences 14 8%
Neuroscience 6 3%
Other 19 11%
Unknown 49 28%
Attention Score in Context

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 05 September 2021.
All research outputs
#6,531,102
of 25,483,400 outputs
Outputs from Journal of Hematology & Oncology
#497
of 1,295 outputs
Outputs of similar age
#103,517
of 340,945 outputs
Outputs of similar age from Journal of Hematology & Oncology
#11
of 19 outputs
Altmetric has tracked 25,483,400 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,295 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one has gotten more attention than average, scoring higher than 61% 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 340,945 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.