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Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

Overview of attention for article published in EPMA Journal, July 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 318)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

news
5 news outlets
blogs
1 blog
twitter
4 X users
patent
4 patents

Citations

dimensions_citation
258 Dimensions

Readers on

mendeley
330 Mendeley
Title
Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine
Published in
EPMA Journal, July 2016
DOI 10.1186/s13167-016-0065-3
Pubmed ID
Authors

Suzanne Hagan, Eilidh Martin, Amalia Enríquez-de-Salamanca

Abstract

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 330 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 <1%
Unknown 329 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 16%
Researcher 43 13%
Student > Master 42 13%
Student > Bachelor 36 11%
Student > Doctoral Student 16 5%
Other 62 19%
Unknown 78 24%
Readers by discipline Count As %
Medicine and Dentistry 65 20%
Biochemistry, Genetics and Molecular Biology 45 14%
Engineering 33 10%
Chemistry 17 5%
Agricultural and Biological Sciences 15 5%
Other 54 16%
Unknown 101 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 62. 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 01 August 2023.
All research outputs
#636,895
of 23,999,200 outputs
Outputs from EPMA Journal
#13
of 318 outputs
Outputs of similar age
#13,332
of 360,318 outputs
Outputs of similar age from EPMA Journal
#2
of 8 outputs
Altmetric has tracked 23,999,200 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 318 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done particularly well, scoring higher than 95% 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 360,318 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 96% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.