Novel cytokine peptide‐based vaccines: an interleukin‐4 vaccine suppresses airway allergic responses in mice
Bibliographic record
Abstract
BACKGROUND: Monoclonal antibodies or soluble receptors have been used to block over-produced endogenous cytokines. However, they have disadvantages of short half-lives, high costs, and possible adverse effects. Using interleukin (IL)-4 as a model target, we sought to develop a novel therapeutic strategy by constructing an IL-4 peptide-based vaccine for blocking IL-4 on a persistent basis, and to evaluate its efficacy in a mouse model of asthma. METHODS: A peptide was selected by antigenic prediction and structure analysis of IL-4/receptor complex. The vaccine was constructed by employing truncated hepatitis B core antigen as carrier with the peptide inserted using gene engineering methods. It was then expressed, purified and identified. Prior to intraperitoneal sensitization and intranasal challenge with ovalbumin, mice were subcutaneously immunized three times with the vaccine, or the carrier or saline as controls. Serum antibodies, inflammatory cells in bronchoalveolar lavage fluids (BALF), lung histology, and responsiveness to inhaled methacholine were analyzed. RESULTS: The vaccine presented as virus-like particles and reacted to polyclonal anti-IL-4 in Western blotting. Vaccinated mice produced high titers of IgG to IL-4. Serum ovalbumin-specific IgE, eosinophil accumulation in BALF, goblet cell hyperplasia, tissue inflammation and methacoline-induced respiratory responses were markedly suppressed in vaccinated mice with statistical significance, as compared with those in the control groups. CONCLUSIONS: Administration of this novel IL-4 vaccine led to an overall decrease in the development of airway allergic inflammatory responses. The results indicate that cytokine peptide-based vaccines hold potential for treatment of asthma and, by extension, other diseases where over-expressed cytokines play a pivotal role in pathogenesis.
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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 | 0.001 |
| 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".