IL-4 gene therapy for collagen arthritis suppresses synovial IL-17 and osteoprotegerin ligand and prevents bone erosion
Bibliographic record
Abstract
Bone destruction is the most difficult target in the treatment of rheumatoid arthritis (RA).Here, we report that local overexpression of IL-4, introduced by a recombinant human type 5 adenovirus vector (Ad5E1mIL-4) prevents joint damage and bone erosion in the knees of mice with collagen arthritis (CIA).No difference was noted in the course of CIA in the injected knee joints between Ad5E1mIL-4 and the control vector, but radiographic analysis revealed impressive reduction of joint erosion and more compact bone structure in the Ad5E1mIL-4 group.Although severe inflammation persisted in treated mice, Ad5E1mIL-4 prevented bone erosion and diminished tartrate-resistant acid phosphatase (TRAP) activity, indicating that local IL-4 inhibits the formation of osteoclast-like cells.Messenger RNA levels of IL-17, IL-12, and cathepsin K in the synovial tissue were suppressed, as were IL-6 and IL-12 protein production.Osteoprotegerin ligand (OPGL) expression was markedly suppressed by local IL-4, but no loss of OPG expression was noted with Ad5E1mIL-4 treatment.Finally, in in vitro studies, bone samples of patients with arthritis revealed consistent suppression by IL-4 of type I collagen breakdown.IL-4 also enhanced synthesis of type I procollagen, suggesting that it promoted tissue repair.These findings may have significant implications for the prevention of bone erosion in arthritis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".