| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 1% |
| Unknown | 68 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 9 | 13% |
| Other | 6 | 9% |
| Student > Ph. D. Student | 6 | 9% |
| Researcher | 6 | 9% |
| Student > Postgraduate | 5 | 7% |
| Other | 21 | 30% |
| Unknown | 16 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 15 | 22% |
| Agricultural and Biological Sciences | 8 | 12% |
| Immunology and Microbiology | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 6 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 6% |
| Other | 12 | 17% |
| Unknown | 18 | 26% |
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 01 December 2021.
All research outputs
#7,196,997
of 22,747,498 outputs
Outputs from BMC Medicine
#2,548
of 3,413 outputs
Outputs of similar age
#70,615
of 221,905 outputs
Outputs of similar age from BMC Medicine
#45
of 57 outputs
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So far Altmetric has tracked 3,413 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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