Immune modulation through silencing CD80 and CD86 in dendritic cells using siRNA (88.4)
Bibliographic record
Abstract
Abstract Dendritic cells (DC) are central cells of the immune system that can stimulate or suppress immune responses, partially depending on their expression of costimulatory molecules. CD80 and CD86 are critical costimulatory molecules for both T cell responses and Th differentiation. Thus, silencing DC expression of CD80 and CD86, using siRNA, could potentially enable manipulation of immune responses. Since silencing of CD80 or CD86 in DC has not been reported, in this study we investigated the tolerogenesis and Th differentiation capacity of CD80/CD86 in DC after siRNA-mediated gene silencing. Gene silencing was performed by transfecting mouse bone marrow-derived DC with CD80 or CD86 siRNA. The gene silencing efficacy was measured by flow cytometry, RT-PCR, and real time PCR. DC that were transfected with CD80 or CD86 siRNA induced inhibition of T cell responses in MLR and generated T regulatory cells. The CD4+CD25+FoxP+ cell subset was significantly increased in a co-culture system that contained CD80- or CD86-silenced DC and allogeneic T cells. Furthermore, CD80 siRNA significantly decreased IFN-gamma, although a reduction in IL-4 was not observed. Conversely, CD86 siRNA significantly reduced IL-4 but did not reduce IFN-gamma. This study suggested, for the first time, that CD80 siRNA and CD86 siRNA can be useful tools for generating tolerogenic DC with a differential effect on modulating cytokine production.
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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".