A novel in vivo siRNA delivery technology for dendritic cell-specific gene silencing and immune modulation (89.5)
Bibliographic record
Abstract
Abstract Silencing immune molecules, such as the costimulatory molecule CD40, using siRNA was shown to have therapeutic potential and promise in immune modulation. However, a major barrier to the clinical application of siRNA is the current lack of an effective and cell-specific delivery system. Herein, we present a new method of selectively delivering siRNA to DC in vivo using stealth immunoliposomes (SILs). CD40 siRNA-containing SILs were generated using 4 types of lipids and decorated with surface-bound, DC-specific mAbs. DC-specific binding capacity of SILs was demonstrated by fluorescence microscopy and flow cytometry. Upon treatment with CD40 siRNA-SILs, DC expression of CD40 was successfully suppressed in vitro. Administration of CD40 siRNA-SILs resulted in DC-specific tissue targeting, as evidenced by increased uptake of fluorescence-tagged siRNA. Additionally, DC from mice treated with CD40 siRNA-SILs exhibited CD40-specific gene silencing in vivo. Tolerogenic properties of DC treated with CD40 siRNA-SILs were determined by inhibition of allogeneic T cell proliferation in MLR. Furthermore, a strong in vivo immune modulation was observed in mice treated with CD40 siRNA-SILs. In conclusion, this is the first demonstration of DC-specific siRNA delivery and gene silencing in vivo, which highlights the potential of DC-mediated immune modulation and the feasibility of siRNA-based clinical therapy.
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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 | 0.001 |
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".