A novel vaccination platform for epicutaneous allergen-specific immunotherapy based on beta-glucan neoglycoconjugates
Bibliographic record
Abstract
Abstract Coupling of long chain carbohydrates to allergens can mask IgE epitopes and thus reduce the allergenicity of the neoglycoconjugates. The resulting molecules can target and activate dendritic cells via C-type lectin receptors. Neoglycoconjugates therefore have a high potential for epicutaneous immunotherapy (EPIT) by delivering a hypoallergenic vaccine to a tissue rich in dendritic cells. In the current study we test the therapeutic efficiency of the beta-glucan laminarin conjugated to ovalbumin (LamOVA) in a mouse model of allergic asthma. Sensitized Balb/C mice were treated epicutaneously via micropores created by fractional laser ablation. Humoral and cellular responses were evaluated by ELISA, RBL assay and BAT. Lung function was analyzed by whole body plethysmography. Laminarin remained biologically active after coupling to ovalbumin as indicated by binding to Dectin-1 receptor. IgE epitopes were successfully masked according to in vitro IgE binding aasay and skin reaction scores in therapeutic setup.LamOVA was more potent in stimulating humoral immune responses in comparison to OVA and induced the highest IgG1 and IgG2a production. Epicutaneous treatment with OVA and LamOVA reduced lung inflammation and improved lung function of sensitized mice more efficiently compared to s.c. injection with OVA/alum. Epicutaneous immunotherapy can alleviate lung inflammation and reduce airway hyperresponsiveness in sensitized mice more efficiently than s.c. injection with alum. Coupling of allergen to laminarin reduces local side effects and increases the immunogenicity, resulting in a safer 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".