A Novel Therapy for Autoimmune Arthritis Using CD40 Gene‐Silenced Dendritic Cells
Bibliographic record
Abstract
CD40 plays a critical role in dendritic cell (DC)‐mediated immune modulation. We have previously achieved immune modulation through RNA interference (RNAi), implying the therapeutic potential of RNAi‐modified DC as antigen‐specific tolerogenic therapies. To date, an RNAi‐based treatment for autoimmune arthritis is not reported. We hypothesized that treatment with CD40‐silenced DC may develop antigen‐specific tolerance and may be beneficial to the prevention and intervention of autoimmune arthritis. To test this, we generated DC and knocked down CD40 using small interfering RNA (CD40‐siRNA). CD 40 silenced DC displayed inhibitory capacity of T cell response in MLR. Immunization with type II collagen (CII)‐pulsed and CD40 gene‐silenced DC (CII‐pulsed/gene‐silenced DC) resulted in antigen‐specific nonresponsiveness in T cell responses. Vaccination with CII‐pulsed/gene‐silenced DC prevented collagen‐induced arthritis (CIA) disease development in a murine rheumatoid arthritis model. Furthermore, administration of CII‐pulsed/gene‐silenced DC at pre‐clinic stage was sufficient to inhibit progression of CIA. The therapeutic effects were further evidenced by decreased clinical scores, inhibited inflammatory infiltrates, and suppressed T cell and B cell responses to CII. In conclusion, this study is the first demonstration of immunotherapy using CD40 silenced DCs for autoimmune 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) | 0.000 | 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 | 0.001 |
| 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".