Insights into pathogenesis and clinical implications in myositis-associated interstitial lung diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: Interstitial lung diseases (ILDs) have been reported to be associated with myositis (including polymyositis and dermatomyositis). These myositis-associated ILDs carry significant morbidity and mortality. This review summarizes recent findings on myositis-associated ILD with a focus on pathogenesis and emerging treatment. RECENT FINDINGS: Recent advances in genetics have revealed 22 myositis-associated genome-wide loci, which were significantly enriched in regulatory regions in immune cells. An analysis of such disease-associated loci elucidated potential drug targets (e.g., TYK2 targeted by tofacitinib). In another study, an intronic variant in WDFY4 in association with clinically amyopathic dermatomyositis (CADM) had an effect for higher expression of a truncated WDFY4 isoform. Truncated WDFY4 markedly enhanced the MDA5-mediated NF-κB activation and cell apoptosis, indicating the dysregulated WDFY4-MDA5 pathway as a novel pathogenesis of CADM. As a novel strategy, tofacitinib treatment showed a promising improvement in survival and clinical features of CADM-associated ILD. SUMMARY: The genetic differences in the myositis-susceptible loci may explain the heterogeneous phenotypes and treatment responses in myositis-associated ILD. The understanding of pathogenesis with the genetic background as well as autoantibodies will enable the practice of personalized treatment in the management of the disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".