Antibodies to RNA polymerase III in systemic sclerosis detected by ELISA.
Bibliographic record
Abstract
OBJECTIVE: To determine serological and clinical variables associated with anti-RNA polymerase III (RNAP-III) antibodies in patients with systemic sclerosis (SSc) using a new ELISA method. METHODS: Sera from 242 patients with SSc were collected from 14 Canadian clinics. Control sera were from 287 blood donors, and 42 patients with infectious disease, 30 with rheumatoid arthritis (RA), and 30 with systemic lupus erythematosus (SLE). Antibodies to RNAP-III were detected by an ELISA kit and antibodies to other cellular antigens were identified by indirect immunofluorescence (IIF) on HEp-2 cell substrate, line immunoassay, immunoprecipitation of recombinant protein, and addressable laser bead immunoassay (ALBIA). RESULTS: Anti-RNAP-III antibodies were detected in 47/242 (19.4%) SSc sera, 0% RA and SLE sera, 1/287 blood donor sera, and 2/42 infectious disease sera. Diffuse disease (59.5%) was more common than limited disease (36.1%) in the anti-RNAP-III-positive patients (p = 0.006) and there was an association between the presence of anti-RNAP-III and kidney and joint/tendon involvement, but there was no association with a nucleolar IIF pattern, lung involvement, or other clinical indicators. There was a negative association between the presence of anti-RNAP-III antibodies and anticentromere by IIF (p = 0.00004) and anti-Scl-70 by ALBIA (p = 0.0005) and line immunoassay (p = 0.003), suggesting a virtually exclusive presence of these antibodies in SSc. CONCLUSION: Anti-RNAP-III autoantibodies were found in nearly 20% of SSc patients but in less than 1% of controls, thus detection of this antibody is a useful marker to help diagnose SSc. As well, this antibody has prognostic utility, since it is associated with scleroderma renal crisis and the diffuse cutaneous form of SSc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".