Association of M2 Macrophages, Th2, and B Cells With Pathomechanism in Microscopic Polyangiitis Complicated by Interstitial Lung Disease
Bibliographic record
Abstract
OBJECTIVE: To address the pathomechanism of microscopic polyangiitis (MPA) complicated by interstitial lung disease (ILD) using serum biomarker profile and pulmonary histopathology. METHODS: Serum biomarkers from patients with MPA-ILD (n = 32), MPA without ILD (n = 17), and healthy controls (n = 10) were examined. Based on the biomarker profiles, principal component analysis (PCA) and cluster analysis were performed to classify patients with MPA-ILD into subgroups. Clinical characteristics and prognosis were assessed for each subgroup. Two lung biopsies were examined following H&E staining and immunostaining. RESULTS: T cell and macrophage polarization was skewed toward the T helper (Th) 2 cells and M2 macrophages in the MPA-ILD group relative to that in MPA without ILD group. The PCA allowed classification of the 19 biomarker profiles into 3 groups: (1) B cell- and neutrophil-related cytokines, vascular angiogenesis-related factors, extracellular matrix-producing factors; (2) Th1-driven cytokines, M1 macrophage-driven cytokines, and Th2-driven cytokines; and (3) M2 macrophage-induced and driven cytokines. The cluster analysis stratified the patients with MPA-ILD into clinically fibrotic-dominant (CFD) and clinically inflammatory-dominant (CID) groups. Notably, severe infections were significantly higher in the CFD group than in the CID group. Immunohistochemical staining demonstrated intense CXC motif chemokine ligand 13 staining in B cells and Th2 cells in the interstitium of the lungs of patients with MPA-ILD. CONCLUSION: The activation of M2 macrophages, Th2 cells, and B cells plays a key role in the pathomechanism of MPA-ILD. Classification of MPA-ILD based on serum biomarker profile would be useful in predicting the disease activity and the complications of severe infection in MPA-ILD.
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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".