Effective Treatment of Janus Kinase 1/3 Inhibitor in Blau Syndrome From a Multicenter Retrospective Study in Central China
Bibliographic record
Abstract
Objective We sought to investigate the effectiveness of the Janus kinase 1/3 inhibitor (JAK1/3i) tofacitinib (TOF) in treating Blau syndrome (BS), and to explore the association between various clinical and genetic features and therapeutic responses within the cohort. Methods A 5-year, multicenter, retrospective, observational study ( ClinicalTrials.gov : NCT06688838 ) was conducted across 7 centers, focusing on genetic profiles and the clinical manifestations of the cohort. Genetic analysis, including whole-exome sequencing and nucleotide-binding oligomerization domain 2 (NOD2) and signal transducer and activator of transcription 3 (STAT3) rs2293152 phenotypic comparisons, was performed to assess therapeutic responses. Results All patients had arthritis, with 2 cases being oligoarticular and 22 polyarticular. The joints primarily affected included the wrists, proximal interphalangeal joints, ankles, and knees. Radiographic analysis revealed symmetrical nonerosive arthropathy in 92.3% of patients. Notably, two-thirds of the cohort displayed previously unrecognized dysplastic bone changes. Ocular involvement was observed in all patients. Notably, no association was found between different NOD2 sequences and therapy response. Conversely, patients harboring the STAT3 rs2293152 GG polymorphism demonstrated favorable responses to treatment, regardless of whether JAK1/3i or tumor necrosis factor inhibitors (TNFi) were used. Conclusion TOF could be an effective therapeutic option for patients with BS who demonstrate resistance to TNFi or corticosteroids. Specifically, the STAT3 rs2293152 GG polymorphism was associated with improved response to treatment, suggesting a genotype-influenced therapeutic efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".