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Methods and matrices: approaches to identifying miRNAs for Nasopharyngeal carcinoma

Overview of attention for article published in Journal of Translational Medicine, January 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
49 Mendeley
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Title
Methods and matrices: approaches to identifying miRNAs for Nasopharyngeal carcinoma
Published in
Journal of Translational Medicine, January 2014
DOI 10.1186/1479-5876-12-3
Pubmed ID
Authors

Jordan L Plieskatt, Gabriel Rinaldi, Yanjung Feng, Paul H Levine, Samantha Easley, Elizabeth Martinez, Salman Hashmi, Nader Sadeghi, Paul J Brindley, Jeffrey M Bethony, Jason P Mulvenna

Abstract

Nasopharyngeal carcinoma (NPC) is a solid tumor of the head and neck. Multimodal therapy is highly effective when NPC is detected early. However, due to the location of the tumor and the absence of clinical signs, early detection is difficult, making a biomarker for the early detection of NPC a priority. The dysregulation of small non-coding RNAs (miRNAs) during carcinogenesis is the focus of much current biomarker research. Herein, we examine several miRNA discovery methods using two sample matrices to identify circulating miRNAs (c-miRNAs) associated with NPC.

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Uruguay 2 4%
Malaysia 1 2%
Denmark 1 2%
United States 1 2%
Unknown 44 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 24%
Professor > Associate Professor 7 14%
Researcher 6 12%
Student > Master 6 12%
Student > Bachelor 5 10%
Other 6 12%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 27%
Medicine and Dentistry 12 24%
Biochemistry, Genetics and Molecular Biology 8 16%
Veterinary Science and Veterinary Medicine 2 4%
Engineering 2 4%
Other 4 8%
Unknown 8 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 August 2020.
All research outputs
#6,932,313
of 21,358,488 outputs
Outputs from Journal of Translational Medicine
#1,121
of 3,682 outputs
Outputs of similar age
#93,660
of 303,907 outputs
Outputs of similar age from Journal of Translational Medicine
#102
of 264 outputs
Altmetric has tracked 21,358,488 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,682 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 65% 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 303,907 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 51% of its contemporaries.
We're also able to compare this research output to 264 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.