Mesenteric lymph node transcriptome profiles in BALB/c mice sensitized to three common food allergens
Bibliographic record
Abstract
BACKGROUND: Food allergy is a serious health concern among infants and young children. Although immunological mechanism of food allergy is well documented, the molecular mechanism(s) involved in food allergen sensitization have not been well characterized. Therefore, the present study analyzed the mesenteric lymph node (MLN) transcriptome profiles of BALB/c mice in response to three common food allergens. RESULTS: Microarray analysis identified a total of 1361, 533 and 488 differentially expressed genes in response to β-lactoglobulin (BLG) from cow's milk, ovalbumin (OVA) from hen's egg white and peanut agglutinin (PNA) sensitizations, respectively (p < 0.05). A total of 150 genes were commonly expressed in all antigen sensitized groups. The expression of seven representative genes from microarray experiment was validated by real-time RT-PCR. All allergens induced significant ear swelling and serum IgG1 concentrations, whereas IgE concentrations were increased in BLG- and PNA-treated mice (p < 0.05). Treatment with OVA and PNA significantly induced plasma histamine concentrations (p < 0.05). The PCA demonstrated the presence of allergen-specific IgE in the serum of previously sensitized and challenged mice. CONCLUSIONS: Immunological profiles indicate that the allergen dosages used are sufficient to sensitize the BALB/c mice and to conduct transcriptome profiling. Microarray studies identified several differentially expressed genes in the sensitization phase of the food allergy. These findings will help to better understand the underlying molecular mechanism(s) of food allergen sensitizations and may be useful in identifying the potential biomarkers of 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 | 0.000 | 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".