↓ Skip to main content
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
17 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Readers on

mendeley
54 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Systematic Screening of Chemokines to Identify Candidates to Model and Create Ectopic Lymph Node Structures for Cancer Immunotherapy
Published in
Scientific Reports, November 2017
DOI 10.1038/s41598-017-15924-2
Pubmed ID
Authors
Abstract

The induction of ectopic lymph node structures (ELNs) holds great promise to augment immunotherapy against multiple cancers including metastatic melanoma, in which ELN formation has been associated with a unique immune-related gene expression signature composed of distinct chemokines. To investigate the therapeutic potential of ELNs induction, preclinical models of ELNs are needed for interrogation of these chemokines. Computational models provide a non-invasive, cost-effective method to investigate leukocyte trafficking in the tumor microenvironment, but parameterizing such models is difficult due to differing assay conditions and contexts among the literature. To better achieve this, we systematically performed microchemotaxis assays on purified immune subsets including human pan-T cells, CD4(+) T cells, CD8(+) T cells, B cells, and NK cells, with 49 recombinant chemokines using a singular technique, and standardized conditions resulting in a dataset representing 238 assays. We then outline a groundwork computational model that can simulate cellular migration in the tumor microenvironment in response to a chemoattractant gradient created from stromal, lymphoid, or antigen presenting cell interactions. The resulting model can then be parameterized with standardized data, such as the dataset presented here, and demonstrates how a computational approach can help elucidate developing ELNs and their impact on tumor progression.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 7 13%
Student > Master 7 13%
Student > Bachelor 6 11%
Researcher 5 9%
Other 3 6%
Other 8 15%
Unknown 18 33%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 9 17%
Immunology and Microbiology 9 17%
Biochemistry, Genetics and Molecular Biology 8 15%
Medicine and Dentistry 8 15%
Unspecified 1 2%
Other 1 2%
Unknown 18 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 29 November 2018.
All research outputs
#4,041,658
of 28,083,244 outputs
Outputs from Scientific Reports
#33,673
of 154,937 outputs
Outputs of similar age
#74,662
of 454,450 outputs
Outputs of similar age from Scientific Reports
#827
of 4,016 outputs
Altmetric has tracked 28,083,244 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 154,937 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done well, scoring higher than 78% 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 454,450 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 83% of its contemporaries.
We're also able to compare this research output to 4,016 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.