S100A12 and S100A8/9 proteins are biomarkers of articular disease activity in Blau syndrome
Bibliographic record
Abstract
OBJECTIVE: To identify biomarkers of articular and ocular disease activity in patients with Blau syndrome (BS). METHODS: Multiplex plasma protein arrays were performed in five BS patients and eight normal healthy volunteers (NHVs). Plasma S100A12 and S100A8/9 were subsequently measured by ELISA at baseline and 1-year follow-up in all patients from a prospective multicentre cohort study. CRP was measured using Meso Scale Discovery immunoassay. Active joint counts, standardization uveitis nomenclature for anterior uveitis cells and vitreous haze by Nussenblatt scale were the clinical parameters. RESULTS: Multiplex Luminex arrays identified S100A12 as the most significantly elevated protein in five selected BS vs eight NHVs and this was confirmed by ELISA on additional samples from the same five BS patients. In the patient cohort, S100A12 (n = 39) and S100A8/9 (n = 33) were significantly higher compared with NHVs (n = 44 for S100A12, n = 40 for S100A8/9) (P = 0.0000004 and P = 0.0003, respectively). Positive correlations between active joint counts and S100 levels were significant for S100A12 (P = 0.0008) and S100A8/9 (P = 0.015). CRP levels did not correlate with active joint count. Subgroup analysis showed significant association of S100 proteins with active arthritis (S100A12 P = 0.01, S100A8/9 P = 0.008). Active uveitis was not associated with increased S100 levels. CONCLUSION: S100 proteins are biomarkers of articular disease activity in BS and potential outcome measures in future clinical trials. As secreted neutrophil and macrophage products, S100 proteins may reflect the burden of granulomatous tissue in BS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".