Mast cells enhance sterile inflammation in chronic nonbacterial osteomyelitis
Bibliographic record
Abstract
ABSTRACT Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disease, and patients with active or recurrent bone inflammation at multiple sites are diagnosed with chronic recurrent multifocal osteomyelitis (CRMO). The Chronic multifocal osteomyelitis (CMO) mouse model develops IL-1β-driven sterile bone lesions reminiscent of severe CRMO. The goal of this study was to evaluate the potential involvement of mast cells in CMO/CRMO. Here, we show that mast cells accumulate in inflamed tissues from CMO mice and that mast cell protease Mcpt1 can be detected in the peripheral blood. A transgenic model of connective tissue mast cell depletion (Mcpt5-Cre:Rosa26-Stopfl/fl-DTa) was crossed with CMO mice and the resulting mice (referred to as CMO/MC–) showed a significant delay in disease onset compared with age-matched CMO mice. At 5-6 months of age, CMO/MC– mice had fewer bone lesions and immune infiltration in the popliteal lymph nodes that drain the affected tissues. In bone marrow-derived mast cell cultures from CMO mice, cytokine production in response to the alarmin IL-33 was elevated compared with wild-type cultures. To test the relevance of mast cells to human CRMO, we tested serum samples from a cohort of healthy controls and from CRMO patients at diagnosis. Interestingly, mast cell chymase was elevated in CRMO patients as well as in patients with oligoarticular juvenile arthritis. Tryptase-positive mast cells were also detected in bone lesions from CRMO patients and patients with bacterial osteomyelitis. Together, our results identify mast cells as cellular contributors to bone inflammation in CMO/CRMO and provide rationale for further study of mast cells as therapeutic targets.
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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 | 0.000 | 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".