Novel Recombinant Interleukin-13 Peptide-based Vaccine Reduces Airway Allergic Inflammatory Responses in Mice
Bibliographic record
Abstract
RATIONALE: Interleukin (IL)-13 plays a pivotal role in the pathogenesis of allergic asthma. Passive administration of its monoclonal antibody or soluble receptor to block overproduced IL-13 has been proven to be effective in controlling airway allergic responses in animal models, but these approaches have disadvantages of short half-lives, high costs, and possible adverse effects. OBJECTIVES: We sought to develop a novel therapeutic strategy through constructing an IL-13 peptide-based vaccine for blocking IL-13 on a persistent effect basis and to evaluate its in vivo effects using a murine model. METHODS: To break self-tolerance, truncated hepatitis B core antigen was used as a carrier. Vaccine was prepared by inserting a peptide derived from the receptor binding site of mouse IL-13 into the immunodominant epitope region of the carrier using gene recombination methods. Mice received vaccine subcutaneously three times, and then subjected to intraperitoneal sensitization and intranasal challenge with ovalbumin. Control animals received carrier or saline in place of vaccine. MEASUREMENTS AND MAIN RESULTS: The vaccine presented as virus-like particles and induced sustained and high titered IL-13-specific IgG without the use of conventional adjuvant. Vaccination significantly suppressed ovalbumin-induced inflammatory cell number, and IL-13 and IL-5 levels in bronchoalveolar lavage fluids. Serum total and ovalbumin-specific IgE were also significantly inhibited. Moreover, allergen-induced goblet cell hyperplasia, lung tissue inflammatory cell infiltration, and pulmonary hyperresponsiveness to inhaled methacholine were significantly suppressed in vaccinated mice. CONCLUSIONS: Our data indicate that IL-13 peptide-based vaccines could be an effective therapeutic approach in the treatment of asthma.
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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) | 0.001 | 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 | 0.001 |
| 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".