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A semi-automated technique for labeling and counting of apoptosing retinal cells

Overview of attention for article published in BMC Bioinformatics, January 2014
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet
twitter
3 tweeters
patent
1 patent

Citations

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16 Dimensions

Readers on

mendeley
49 Mendeley
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Title
A semi-automated technique for labeling and counting of apoptosing retinal cells
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-169
Pubmed ID
Authors

Mukhtar Bizrah, Steve C Dakin, Li Guo, Farzana Rahman, Miles Parnell, Eduardo Normando, Shereen Nizari, Benjamin Davis, Ahmed Younis, M Cordeiro

Abstract

Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer's disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 22%
Researcher 11 22%
Student > Master 7 14%
Student > Bachelor 6 12%
Unspecified 2 4%
Other 7 14%
Unknown 5 10%
Readers by discipline Count As %
Medicine and Dentistry 15 31%
Agricultural and Biological Sciences 11 22%
Computer Science 5 10%
Neuroscience 3 6%
Biochemistry, Genetics and Molecular Biology 3 6%
Other 7 14%
Unknown 5 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 02 January 2020.
All research outputs
#1,351,072
of 15,348,605 outputs
Outputs from BMC Bioinformatics
#432
of 5,606 outputs
Outputs of similar age
#19,058
of 191,357 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 13 outputs
Altmetric has tracked 15,348,605 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,606 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 92% 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 191,357 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.