Ultrasensitive interferons quantification reveals different cytokine profile secretion in inflammatory myopathies and can serve as biomarkers of activity in dermatomyositis
Bibliographic record
Abstract
Objective: The objective of this study was to evaluate the presence of different types of interferon in idiopathic inflammatory myopathies (IIM) and their subgroups using ultrasensitive cytokine detection techniques (SIMOA) and to assess their potential as activity biomarkers. Methods: Disease activity was measured at the time of serum collection and assessed by manual muscle testing eight (MMT8 score 0-150), muscle enzymes to calculate the Physician Global Assessment (PGA) (0-10). Patients were classified as active if PGA>5.Serum IFN-α and IFN-γ levels was measured using the single molecule array (SIMOA) technique. Serum IFN-β level was measured by Elisa. Correlation between IFN levels and disease activity were performed. Results: We included 242 IIM patients and found a good correlation between type I Interferon (IFN) and dermatomyositis disease activity. IFN-α and IFN-β was highly correlated with disease activity (r=0.76 and r=0,58). To evaluate whether the different types of Interferons could serve as biomarkers of activity, we generated ROC curves. Patients with active DM had a higher median IFN-α level (0.49 pg/ml [0.1-3.7]) compared with non-active patients (0.03 pg/ml [0.01-0.07] p<0.05). The area under the curve was 0.90 IC95 (0.76-0.97) p<0.05. Furthermore, Myositis-specific antibodies appear to be associated with a different secretion profile; patients with anti-MDA 5 antibodies had higher level of IFN-α than most other antibodies (6.58 vs 0.14 p<0.005). NXP2 had higher IFN-β level than patients with Tif1γ antibodies. Conclusion: Serum IFN-α level measured by SIMOA is a reliable biomarker of DM activity. Myositis-specific antibodies appear to be associated with a different secretion profile. This data needs to be confirmed in order to select the good therapeutics strategies in DM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".