Signal Regulatory Protein α Expression in Systemic Vasculitis
Bibliographic record
Abstract
Objective Signal regulatory protein α (SIRPα) is found primarily on myeloid cells, including macrophages and neutrophils; binds to CD47; and regulates phagocytosis, antigen presentation, cellular fusion, cell proliferation, and migration. Therefore, SIRPα may be involved in the pathogenesis of autoimmune diseases, including systemic vasculitis. This study aimed to assess SIRPα expression in tissue samples from patients with vasculitis. Methods Immunohistochemical staining for SIRPα was performed on temporal artery (TA), kidney, and lung biopsy samples from patients with giant cell arteritis (GCA), patients with microscopic polyangiitis (MPA), patients with granulomatosis with polyangiitis (GPA), and patients without vasculitis. A score of SIRPα + expression was calculated, derived from the percentages of monocytes, macrophages, and dendritic cells and neutrophils with different staining intensities in affected tissues. Results A total of 46 samples from patients with different vasculitides (GCA, MPA, and GPA) were included in the study. Tissue samples included TA samples from 15 patients with GCA; kidney samples from 11 and 9 patients with GPA and MPA, respectively; and lung samples from 11 patients with GPA. Most tissue samples from patients with active vasculitis (15 of 15 TA samples, 17 of 20 kidney samples, and 9 of 11 lung samples) showed SIRPα staining. SIRPα staining intensity was less in kidney samples compared to TA and lung samples. Conclusion This study demonstrates high‐level expression of SIRPα in macrophages and monocytes in affected tissue in systemic vasculitis. These findings provide a foundation for further studies exploring the role of the SIRPα–CD47 pathway in the pathogenesis of systemic vasculitis and the potential for the blockade of SIRPα and/or the depletion of SIRPα + cells as treatment of systemic vasculitis.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".