Maternal immunization with ovalbumin or Dermatophagoides pteronyssinus has opposing effects on FcγRIIb expression on offspring B cells
Bibliographic record
Abstract
BACKGROUND: Over the last decade, our group has demonstrated that murine preconception immunization with allergens has a protective effect on allergy development in offspring. The murine model used in the present study allowed us to compare allergy induction by ovalbumin (OVA) and dust mite extract from Dermatophagoides pteronyssinus (Dp). FINDINGS: Female mice were immunized with OVA or Dp. Pups from immunized and non-immune mothers were immunized at 3 days old (do) with the same antigen used for the maternal immunization. The offspring were analyzed at 20 do. Preconceptional immunization with OVA or Dp did not increase maternal IgE serum levels, although the immunizations induced an increase in allergen-specific IgG1 Ab levels. Offspring serum analyses revealed that maternal immunization with OVA suppressed IgE production only in offspring immunized with OVA. Both preconception immunization protocols inhibited cellular influx into the airways of immunized offspring compared with controls. Similar frequencies of offspring IgM + B cells were found in the OVA- and Dp-immunized groups compared with their respective control groups. Moreover, preconception immunization with OVA enhanced FcγRIIb expression on OVA-immunized offspring B cells. In contrast, decreased FcγRIIb expression was detected on Dp-immunized offspring B cells compared with cells from the offspring of non-immune mothers. CONCLUSIONS: Together, these results show that preconception OVA immunization and Dp immunization can inhibit allergy development but have opposite effects on FcγRIIb expression on offspring B cells.
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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 | 0.000 |
| 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".