Antigenic Liposomes for Generation of Disease-specific Antibodies
Bibliographic record
Abstract
Antibody responses provide critical protective immunity to a wide array of pathogens. There remains a high interest in generating robust antibodies for vaccination as well as understand how pathogenic antibody responses develop in allergies and autoimmune disease. Generating robust antigen-specific antibody responses is not always trivial. In mouse models, it often requires multiple rounds of immunizations with adjuvant that leads to a great deal of variability in the levels of induced antibodies. One example is in mouse models of peanut allergies where more robust and reproducible models that minimize mouse numbers and the use of adjuvant would be beneficial. Presented here is a highly reproducible mouse model of peanut allergy anaphylaxis. This new model relies on two key factors: (1) antigen-specific splenocytes are adoptively transferred from a peanut-sensitized mouse into a naïve recipient mouse, normalizing the number of antigen-specific memory B- and T-cells across a large number of mice; and (2) recipient mice are subsequently boosted with a strong multivalent immunogen in the form of liposomal nanoparticles displaying the major peanut allergen (Ara h 2). The major advantage of this model is its reproducibility, which ultimately lowers the number of animals used in each study, while minimizing the number of animals receiving multiple injections of adjuvant. The modular assembly of these immunogenic liposomes provides relatively facile adaptability to other allergic or autoimmune models that involve pathogenic antibodies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".