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Automated tests of ANA immunofluorescence as throughput autoantibody detection technology: strengths and limitations

Overview of attention for article published in BMC Medicine, March 2014
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Mentioned by

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2 tweeters

Citations

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37 Dimensions

Readers on

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54 Mendeley
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Title
Automated tests of ANA immunofluorescence as throughput autoantibody detection technology: strengths and limitations
Published in
BMC Medicine, March 2014
DOI 10.1186/1741-7015-12-38
Pubmed ID
Authors

Pier Luigi Meroni, Nicola Bizzaro, Ilaria Cavazzana, Maria Orietta Borghi, Angela Tincani

Abstract

Anti-nuclear antibody (ANA) assay is a screening test used for almost all autoimmune rheumatic diseases, and in a number of these cases, it is a diagnostic/classification parameter. In addition, ANA is also a useful test for additional autoimmune disorders. The indirect immunofluorescence technique on monolayers of cultured epithelial cells is the current recommended method because it has higher sensitivity than solid phase assays. However, the technique is time-consuming and requires skilled operators. Automated ANA reading systems have recently been developed, which offer the advantage of faster and much easier performance as well as better harmonization in the interpretation of the results. Preliminary validation studies of these systems have given promising results in terms of analytical specificity and reproducibility. However, these techniques require further validation in clinical studies and need improvement in their recognition of mixed or less common staining patterns.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Austria 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 15%
Student > Ph. D. Student 7 13%
Researcher 7 13%
Other 5 9%
Professor > Associate Professor 4 7%
Other 16 30%
Unknown 7 13%
Readers by discipline Count As %
Medicine and Dentistry 16 30%
Agricultural and Biological Sciences 8 15%
Biochemistry, Genetics and Molecular Biology 7 13%
Immunology and Microbiology 5 9%
Chemistry 3 6%
Other 7 13%
Unknown 8 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 March 2014.
All research outputs
#7,853,681
of 12,517,134 outputs
Outputs from BMC Medicine
#1,804
of 2,010 outputs
Outputs of similar age
#97,510
of 187,797 outputs
Outputs of similar age from BMC Medicine
#14
of 17 outputs
Altmetric has tracked 12,517,134 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.9. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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 187,797 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.