Brief Report: Interferon‐α Induction and Detection of Anti‐Ro, Anti‐La, Anti‐Sm, and Anti‐RNP Autoantibodies by Autoantigen Microarray Analysis in Juvenile Dermatomyositis
Bibliographic record
Abstract
OBJECTIVE: To evaluate serum interferon-α (IFNα) activity in the context of autoantibody profiles in patients with juvenile dermatomyositis (JDM). METHODS: Sera from 36 patients with JDM were analyzed. Autoantibody profiles were determined by probing microarrays, which were fabricated with ∼80 distinct autoantigens, with serum and a Cy3-conjugated secondary antibody. Arrays were scanned and analyzed to determine antigen reactivity. Serum IFNα activity was measured using a functional reporter cell assay. Sera were assayed alone or in combination with cellular material released from necrotic U937 cells to stimulate peripheral blood mononuclear cells from healthy donors in vitro, and IFNα production in culture was measured by a dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA). RESULTS: Reactivity against at least 1 of 41 autoantigens on the microarray, including Ro 52, Ro 60, La, Sm, and RNP, was observed in 75% of the serum samples from patients with JDM. IFNα activity was detected in 7 samples by reporter cell assay. The reporter cell assay showed a significant association of reactivity against Ro, La, Sm, and proliferating cell nuclear antigen with serum IFNα activity (P = 0.005). Significance Analysis of Microarrays (SAM) identified increased reactivity against Sm, RNP, Ro 52, U1-C, and Mi-2 in these sera. Sixteen samples induced IFNα production as measured by DELFIA, and there was a significant association of reactivity against Ro, La, Sm, and RNP with the induction of IFNα by serum and necrotic cell material (P = 0.034). SAM identified increased reactivity against Ro 60 in these sera. CONCLUSION: These data support the hypothesis that nucleic acid-associated autoantibodies, including the Ro/La and Sm/RNP complexes, may stimulate the production of active IFNα in children with JDM.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".