Mast cells play a key role in autoinflammatory disease development in PSTPIP2-deficient cmo mice (BA7P.141)
Bibliographic record
Abstract
Abstract Chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory disease characterized by fevers, bone lesions, and skin lesions. A CRMO-like disease develops in cmo mice, which harbor a mutation in proline serine threonine phosphatase-interacting protein 2 (PSTPIP2). Loss of PSTPIP2 in cmo mice has been linked to elevated macrophage and osteoclast activation, and IL-1β-dependent disease progression. Since mast cells were detected in cmo lesions, we have tested the role of mast cells in cmo disease. In diseased cmo mice, increased connective tissue mast cells were observed in both normal skin and within skin lesions. Crossing cmo mice with a transgenic model of mast cell deficiency (Mcpt5-Cre:Rosa26-Stopfl/fl-DTa) resulted in mice (cmo/MC-) that were protected from developing cmo disease, including skin lesions and joint swelling. Fewer osteolytic lesions were also observed in cmo/MC- mice by micro-computed tomography. Within these tissues, cmo/MC- mice had reduced levels of IL-1β, and less activated immune cells in popliteal lymph nodes compared to cmo. Intravital microscopy experiments on young cmo mice revealed increased leukocyte recruitment in response to lipopolysaccharide compared to cmo/MC- and wild-type mice. Together, these results implicate mast cells in promoting leukocyte recruitment to inflamed tissues during autoinflammatory disease progession, and suggest that therapies targeting mast cells or their mediators may benefit CRMO patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".