Confirmation of anti-DFS70 antibodies is needed in routine clinical samples with DFS staining pattern
Recognition of nuclear dense fine speckled (DFS) pattern by indirect immunofluorescence (IIF) is not easy. Thus, confirming the presence of these antibodies might be needed. In this study, we aimed to determine the frequency of DFS pattern in our diagnostic laboratory and to investigate the presence...
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Veröffentlicht in: | Central-European journal of immunology 2016, Vol.41 (1), p.6-11 |
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Zusammenfassung: | Recognition of nuclear dense fine speckled (DFS) pattern by indirect immunofluorescence (IIF) is not easy. Thus, confirming the presence of these antibodies might be needed. In this study, we aimed to determine the frequency of DFS pattern in our diagnostic laboratory and to investigate the presence of anti-DFS70 antibodies in samples showing DFS pattern by two commercially available research kits retrospectively.
Seventy-four sequential serum samples with DFS pattern on HEp2010 cell substrates by IIF were included in this study. The semiquantitative DFS70 ELISA Kit (MBL International Corporation, Woburn, UK) was used for detection of anti-DFS70 antibodies in these samples. Twenty selected samples were tested for the presence of anti-DFS70 antibodies using ANA Line Immunoassay (LIA) (Immco Diagnostics, New York, USA).
Sixty-two (83.8%) of 74 serum samples were found positive with ELISA, when 15 U/ml was taken as a reference value. Among 18 samples that were found positive by ELISA, five were negative for anti-DFS70 antibodies by LIA, while 13 were found positive. The lowest ELISA result of the sample that was positive by LIA was found to be 45.3 U/ml. When 45.3 U/ml was considered as a reference value, 45 (60.8%) of 74 serum samples were positive by ELISA. Nineteen of 20 patients had no SARD, while one had systemic lupus erythematosus (SLE).
DFS pattern should be confirmed with an objective method such as ELISA, LIA, or IB. We think that confirmation tests for detection of anti-DFS70 antibodies should be included in diagnostic algorithms. |
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ISSN: | 1426-3912 1644-4124 |
DOI: | 10.5114/ceji.2016.58812 |