Novel allergen‐specific therapy for allergy through immunomodulation by CD40‐silenced dendritic cells
Bibliographic record
Abstract
Small interfering RNA (siRNA) is a potent, selective, and easily‐inducible method for blocking the expression of specifically targeted genes. siRNA is reported to be more efficient than traditional therapy such as antisense oligonucleotides. However, the use of siRNA in allergen‐specific therapy for allergy has not yet been reported. Therefore, we attempted to develop a new therapy for allergy using allergen‐pulsed dendritic cells (DCs) in which CD40 is silenced by siRNA. Bone marrow‐derived DCs were silenced with CD40 siRNA and pulsed with ovalbumin (OVA). Following administration of CD40‐silenced and OVA‐pulsed DCs (CD40‐silenced DCs), mice were sensitized with OVA intraperitoneally, and then intranasally challenged with OVA. CD40‐silenced DCs remarkably reduced allergic symptoms, and decreased eosinophilia in the nasal tissue. CD40‐silenced DCs inhibited OVA‐specific T cell responses. Additionally, anti‐OVA IgE and IgG1 in sera were significantly decreased in the mice that were treated with CD40‐silenced DCs. CD40‐silenced DCs also suppressed IL‐4 and IL‐5 production by spleen and lymph node cells after stimulated by OVA. Finally, CD40‐silenced DCs facilitated the generation of CD4+CD25+Foxp3+ regulatory T cells. This study is the first to demonstrate a novel allergen‐specific therapy for allergy through DC‐mediated immune modulation following gene silencing of CD40.
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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".