Improved Serological Differentiation between Systemic Lupus Erythematosus and Mixed Connective Tissue Disease by Use of an SmD3 Peptide-Based Immunoassay
Bibliographic record
Abstract
Autoantibodies to the Sm antigens are specifically found in 5 to 30% of patients with systemic lupus erythematosus (SLE) depending on the detection system and the patient group. Several immunoassays designed for research and diagnostic laboratory use have been developed. The autoantigens employed in these tests include purified native proteins, recombinant polypeptides, and synthetic peptides. In the present study, we compared the clinical accuracy of anti-Sm autoantibody assays from commercial suppliers including different conventional enzyme-linked immunosorbent assay (ELISA) systems based on purified Sm antigens, an addressable laser bead assay and a newly developed anti-Sm peptide assay. Although the clinical sensitivity of all assays under investigation was comparable, relatively poor correlations and significant differences in specificity were found with a patient cohort of 150 patients. The sensitivity and specificity were 10 and 94%, respectively, for the anti-Sm ELISA from Euroimmun, 10 and 90%, respectively, for the QuantaLite Sm (INOVA), 12 and 88%, respectively, for the Sm assay in the Varelisa ReCombi ANA profile (Pharmacia Diagnostics), 10 and 94%, respectively, for the QuantaPlex Sm (INOVA), and 12 and 100%, respectively, for the new SmD3 peptide-based ELISA (Varelisa Sm Antibodies). The majority of positive test results within the control groups were found in patients with mixed connective tissue disease. Based on the results, we conclude that the detection of anti-Sm antibodies strongly depends both on the nature of the antigen and on the detection system. Finally, we conclude that the recently identified SmD peptide containing a symmetrical dimethylarginine at position 112 of D3 represents a promising tool for the detection of a highly specific subpopulation of anti-Sm antibodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".