Ultrasensitive Interferons quantification in idiopathic inflammatory myopathies serve as biomarkers of activity in dermatomyositis and anti-synthetase syndrome
Bibliographic record
Abstract
Abstract Objectives Inflammatory idiopathic myopathies (IIM) are a heterogeneous group of disorders, ranging from a muscle-specific autoimmune disease to a systemic one that are difficult to assess. Recent insights into IIM pathogenesis highlighted the role of interferon (IFN) in the pathophysiology. The aim of this study was to test if IFN serum levels can a use as a biomarker of disease activity in IIM. Methods IFN type I and II were measured using an ultrasensitive detection technology and assess the potential of IFN. Results One hundred and fifty-two patients (dermatomyositis (DM); n=50, anti-synthetase syndrome (ASyS); n=46, immune-mediated necrotizing myopathy (IMNM); n=32, inclusion body myositis (IBM); n=24) and 33 age-matched healthy donors were included. IFN-α levels were higher only in DM (0.07 pg/ml [0.03-0.23], p<0.005) and ASyS groups (0.07 [0.02-0.16], p<0.05) compared with controls (0.02 [0.01-0.05]). IFN-β was increased only in DM and IFN-γ among all IIM. IFN-α levels were correlated with disease activity in DM (r=0.76, p<0.0001). The predictive accuracy of IFN-α level to discriminate active and non-active disease was excellent as reflected by an area under the ROC-curve of 0.88. Using an IFN-α level cut-off above 0.11 pg/ml, the sensitivity was 75% and the specificity was 96% in DM patient. IFN-α and IFN-γ were correlated with disease activity in ASyS groups (r=0.55 and r=0.46 p<0.05)). Conclusions IFNs are promising biomarker for DM and ASyS disease activity.
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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 | 0.000 | 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".