Tissue degrading and remodelling molecules in giant cell arteritis
Bibliographic record
Abstract
OBJECTIVES: GCA is a granulomatous vasculitis affecting large vessels, leading to intimal occlusion accompanied by the accumulation of myofibroblasts. Histopathologically, GCA is characterized by destruction of the tunica media and hypertrophy of the intima with invasion of activated CD4+ T cells, macrophages and multinucleated giant cells (MNGCs). Despite these well-defined histopathological features, the molecular pathology of GCA has largely remained elusive. We aimed to characterize the pathologic features of GCA at the molecular level. METHODS: To identify key molecules involved in GCA pathogenesis, we conducted genome-wide gene expression profiling on arterial lesions obtained through temporal artery biopsy of 16 patients who had not received any prior treatment. The resulting data were examined to reveal specific pathways and genes, and some of the molecules were followed up by immunohistochemistry. RESULTS: Our analysis revealed a unique gene expression pattern in GCA lesions, including enrichment of immune cells and phagocytic pathways related to microglia and osteoclasts. Subsequent immunohistochemistry analysis identified the presence of MMP12 (macrophage elastase), HLA-DRA, and phagocytosis- and osteoclast-associated molecules in infiltrating macrophages and MNGCs. Additionally, we discovered LRRC15-expressing cells in the tunica intima, suggesting a myofibroblast subpopulation that suppresses cytotoxic CD8+ T cells. These molecules were upregulated in other granulomatous diseases affecting not only arteries but also lymph nodes. CONCLUSION: Our study revealed novel molecules associated with the pathological features of GCA, providing a foundation for better understanding of GCA pathogenesis and development of targeted therapeutic strategies.
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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 | 0.000 |
| 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".