↓ Skip to main content

Immunocompetent murine models for the study of glioblastoma immunotherapy

Overview of attention for article published in Journal of Translational Medicine, January 2014
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
217 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.
Title
Immunocompetent murine models for the study of glioblastoma immunotherapy
Published in
Journal of Translational Medicine, January 2014
DOI 10.1186/1479-5876-12-107
Pubmed ID
Authors

Taemin Oh, Shayan Fakurnejad, Eli T Sayegh, Aaron J Clark, Michael E Ivan, Matthew Z Sun, Michael Safaee, Orin Bloch, Charles D James, Andrew T Parsa

Abstract

Glioblastoma remains a lethal diagnosis with a 5-year survival rate of less than 10%. (NEJM 352:987-96, 2005) Although immunotherapy-based approaches are capable of inducing detectable immune responses against tumor-specific antigens, improvements in clinical outcomes are modest, in no small part due to tumor-induced immunosuppressive mechanisms that promote immune escape and immuno-resistance. Immunotherapeutic strategies aimed at bolstering the immune response while neutralizing immunosuppression will play a critical role in improving treatment outcomes for glioblastoma patients. In vivo murine models of glioma provide an invaluable resource to achieving that end, and their use is an essential part of the preclinical workup for novel therapeutics that need to be tested in animal models prior to testing experimental therapies in patients. In this article, we review five contemporary immunocompetent mouse models, GL261 (C57BL/6), GL26 (C57BL/6) CT-2A (C57BL/6), SMA-560 (VM/Dk), and 4C8 (B6D2F1), each of which offer a suitable platform for testing novel immunotherapeutic approaches.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 217 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
Germany 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
United States 1 <1%
Netherlands 1 <1%
Unknown 210 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 27%
Researcher 47 22%
Student > Master 23 11%
Student > Doctoral Student 19 9%
Student > Bachelor 17 8%
Other 52 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 26%
Medicine and Dentistry 40 18%
Biochemistry, Genetics and Molecular Biology 34 16%
Unspecified 23 11%
Immunology and Microbiology 21 10%
Other 42 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 February 2017.
All research outputs
#3,150,689
of 12,450,491 outputs
Outputs from Journal of Translational Medicine
#459
of 2,446 outputs
Outputs of similar age
#45,838
of 191,219 outputs
Outputs of similar age from Journal of Translational Medicine
#6
of 12 outputs
Altmetric has tracked 12,450,491 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,446 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done well, scoring higher than 79% 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 191,219 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 75% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.