CD38 Deficiency Downregulates the Onset and Pathogenesis of Collagen-Induced Arthritis through the NF-<i>κ</i>B Pathway
Bibliographic record
Abstract
Aim . The RelB gene plays an important role in guiding the progression of arthritis. We have previously demonstrated that the expression of the RelB gene is decreased significantly in bone marrow DCs of CD38 -/- mice. In this study, we demonstrate that the cluster of the differentiation (CD38) gene could be a potentially therapeutic target for autoimmune arthritis. Method . Collagen-induced arthritis (CIA) models were generated with both the wild-type (WT) C57BL/6 and CD38 -/- mice. The expression of the RelB gene and maturation of bone marrow-derived dendritic cells (DCs) from the WT and CD38 -/- mice were detected. Antigen-specific T cell responses, joint damage, and expression of proinflammatory cytokines were assessed. The effects of the Nuclear Factor Kappa B (NF- κ B) transcription factor and its mechanisms were characterized. Results . We demonstrated that in CD38 -/- mice, the expression of the RelB gene and major histocompatibility complex II (MHC II) was decreased, accompanied with the inhibited T cell reaction in a mixed lymphocyte reaction (MLR) in bone marrow-derived DCs. Compared to the serious degeneration of the cartilage and the enlarged gap of the cavum articular in WT CIA mice, joint pathological changes of the CD38 -/- CIA mice revealed marked attenuation, while the joint structures were well preserved. The preserved effects were observed by the inhibition of proinflammatory cytokines and promotion of anti-inflammatory cytokines. Furthermore, decreased phosphorylation of NF- κ B was also observed in CD38 -/- CIA mice. Conclusion . We demonstrate that CD38 could regulate CIA through NF- κ B and this regulatory molecule could be a novel target for the treatment of autoimmune inflammatory joint disease.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".