Immune Modulation and Tolerance Induction by RelB-Silenced Dendritic Cells through RNA Interference
Bibliographic record
Abstract
Dendritic cells (DC), the most potent APCs, can initiate the immune response or help induce immune tolerance, depending upon their level of maturation. DC maturation is associated with activation of the NF-kappaB pathway, and the primary NF-kappaB protein involved in DC maturation is RelB, which coordinates RelA/p50-mediated DC differentiation. In this study, we show that silencing RelB using small interfering RNA results in arrest of DC maturation with reduced expression of the MHC class II, CD80, and CD86. Functionally, RelB-silenced DC inhibited MLR, and inhibitory effects on alloreactive immune responses were in an Ag-specific fashion. RelB-silenced DC also displayed strong in vivo immune regulation. An inhibited Ag-specific response was seen after immunization with keyhole limpet hemocyanin-pulsed and RelB-silenced DC, due to the expansion of T regulatory cells. Administration of donor-derived RelB-silenced DC significantly prevented allograft rejection in murine heart transplantation. This study demonstrates for the first time that transplant tolerance can be induced by means of RNA interference using in vitro-generated tolerogenic DC.
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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".