Using BALB/c mice as a model of food allergy to study gene expression profiles in response to common food allergens
Bibliographic record
Abstract
Food allergy is a serious health concern among infants and young children, and its prevalence is growing in westernized countries. Although the immunological mechanism(s) of food allergy are well documented, our understanding of its molecular mechanism(s) is very limited and a suitable animal model for such studies has not been established. The aim of this study therefore, was to use BALB/c mice as a model to characterize genes involved in the sensitization and elicitation phases of the immune response to common food allergens. Female BALB/c mice received intraperitoneal (i.p.) injections for two days with common food allergens cow's milk [beta]-lactoglobulin (BLG), egg ovalbumin (OVA) and peanut agglutinin (PNA), or were intragastrically (i.g.) exposed to egg ovomucoid (OVM) at weekly intervals for 5 weeks. Mice were euthanized to collect for immunological analysis, and mesenteric lymph node (MLN) and spleen tissues that were used for analysis of gene expression during the sensitization and elicitation phases respectively. The MLNs from BLG, OVA and PNA injected mice were used for cDNA microarray analysis, and spleen from OVM mice used for Affymetrix microarray analysis. Mice responded to BLG, OVA and PNA injection and developed type-1 hypersensitivity responses that were indicated by increased concentrations of histamine, and immunoglobulins IgG1 and 19E. The ear swelling response to topical exposure to these antigens provided a suitable tool to quantify the magnitude of allergic response, and a passive cutaneous anaphylaxis (PCA) test confirmed the presence of allergen specific IgEs. Gene expression profiling of the MLN from BLG, OVA or PNA injected mice and spleen from OVM-treated mice revealed a complex network of genes that are involved in the immune response to common food allergens. Some of these genes may be potential candidate biomarker genes for food allergy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".