Novel therapy for allergic disease through gene silencing of CD40 (37.7)
Bibliographic record
Abstract
Abstract Gene silencing is a potent, selective, and easily-inducible method for specifically blocking expression of desired genes. Gene silencing strategies have been successfully tested in animal models of various diseases. However, the therapeutic potential of gene silencing allergic diseases has not yet been reported. CD40 is an important costimulatory molecule that plays a critical role in immune responses. We attempted to develop a new therapy for allergic diseases through gene silencing using a short hairpin siRNA-expressing vector (shRNA) specific to CD40. The allergic mouse model was made by intraperitoneal immunization with ovalbumin (OVA), followed by intranasal challenges with the same antigen. CD40 shRNA was administered before or after OVA immunization. CD40 shRNA treatment reduced CD40 expression in splenic DC, remarkably reduced nasal allergic symptoms, and decreased nasal eosinophilia. The OVA-specific T cell response was inhibited after CD40 shRNA treatment. Additionally, anti-OVA specific IgE was significantly decreased in CD40-shRNA treated mice, as detected by ELISA. The production of IL-4 and IL-5 was suppressed in the CD40-shRNA treated mice. Finally, CD40 shRNA facilitated the generation of regulatory T cells, in particular, the CD4+CD25+Foxp3+ subset of cells. This study, for the first time, has demonstrated a novel therapy for allergic disease through gene silencing.
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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".