Novel IgE peptide‐based vaccine prevents the increase of IgE and down‐regulates elevated IgE in rodents
Bibliographic record
Abstract
BACKGROUND: Immunotherapy with anti-IgE antibodies for treatment of allergy is promising but a short half-life and extremely high cost limit its application. OBJECTIVE: We sought to develop IgE vaccines that induce longer-lasting auto-antibodies to neutralize self-IgE as an alternative therapy. METHODS: The vaccine was made by conjugating three synthetic peptides corresponding to human IgE receptor-binding sites to a carrier, hepatitis B surface antigen. To test the immunogenicity of the vaccine, rats were immunized with the vaccine or hepatitis B surface antigen as control. Serum IgG titres to human IgE and the IgE of other species were measured. The inhibition by rat antisera of the binding of human IgE to its receptor was assessed by ELISA, flow cytometry analysis, and passive cutaneous anaphylaxis (PCA), and its ability to recognize receptor-bound IgE was examined. The in vivo effect of the vaccine was evaluated in trichosanthin-sensitized mice and rats. In the preventative study, vaccination started before sensitization commenced, while in the treatment study, vaccination started after sensitization. Sensitized mice and rats receiving injections of the carrier served as controls. Trichosanthin-specific IgE was measured using PCA. RESULTS: Sera from vaccine-immunized rats contained high titre antibodies that reacted with soluble and plate-bound but not with receptor-bound human IgE; they also reacted with mouse, rat, and dog IgE. Furthermore, the sera inhibited the binding of human IgE to its receptor in a dose-dependent manner. In preventative and treatment studies, serum trichosanthin-specific IgE levels were significantly reduced in vaccinated groups compared with controls. CONCLUSION: Antibodies against self-IgE can be induced by IgE peptide-based vaccines, which are effective in preventing the increase of IgE and in down-regulating IgE in sensitized animals.
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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".