Evaluation of peanut tolerance in sensitized mice through intradermal allergen delivery with adjuvants
Bibliographic record
Abstract
Peanut allergy is a significant worldwide health issue and requires innovative approaches to provide safer and more effective treatments. This study explores the potential of intradermal immunotherapy using peanut formulations combined with adjuvants. Six groups of BALB/c mice previously sensitized to peanut received intradermal treatments every other week for three months comprising of: Group 1 (G1) = Peanut extract (PE) + epinephrine (EPN); G2 = PE + EPN + topical Aldara® pretreatment; G3 = PE + EPN + topical imiquimod (IMQ) micellar gel pretreatment; G4 = PE + EPN + monophosphoryl lipid A (MPLA) + IMQ; G5 = PE + EPN + IMQ; G6 = Peanut powder + EPN + hyaluronic acid (HA). Clinical scores of peanut allergy, serological levels of peanut-specific IgE (Pe-IgE), IgG1, IgG2a, and histamine were compared before and after treatment, alongside studies on IMQ permeation and the pharmacokinetics of major peanut allergen ara h 2. Clinical scores improved significantly in all groups except G2 (trend towards improvement without statistical significance). Pe-IgE level decreased in G1, G2, and G3, while Pe-IgG1 increased in G1, G3, and G4. G3 alone showed a significant rise in Pe-IgG2a. No group was statistically superior to G1 after adjusting for differences in cumulative allergen doses received and initial IgE levels. Ex vivo experiments revealed that IMQ micellar gel in G3 enhanced skin permeation compared to Aldara®. Additionally, ara h 2 absorption was significantly reduced in naive mice treated with PE and HA. Baseline treatment of PE and EPN effectively reduced markers of peanut allergy. However, addition of TLR-agonist adjuvants or HA did not yield statistically significant improvements compared to baseline (G1).
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".