Food-Cooking Processes Modulate Allergenic Properties of Hen’s Egg White Proteins
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Reducing the allergenicity of food allergens can suppress the clinical symptoms of food allergy. The objective of the present study was to investigate the effects of processing on the allergenic properties of hen's egg white proteins. METHODS: Eggs were processed by traditional Chinese cooking, including steaming, water boiling, frying, spicing and tea boiling. The contents of processed egg protein were assessed by sodium dodecyl sulfate polyacrylamide gel electrophoresis; the allergenicity was evaluated by Western blotting, enzyme-linked immunosorbent assay and enzyme allergosorbent test inhibition. Circular dichroism spectrum analysis of four major egg allergens from various egg products was performed as well. A mouse model of food allergy was developed to test the allergenicity of processed egg protein in vivo. RESULTS: Protein degradation was significant following tea boiling and spiced-tea boiling. The total allergenic potential of water-boiled egg and fried egg was relatively higher than that of steamed egg, spiced egg and tea-boiled egg. Challenge with proteins from raw egg, water-boiled egg and fried egg induced skewed T-helper 2 pattern responses (Th2 responses) in the intestine of mice sensitized to egg proteins; however, when the mice sensitized to egg proteins were challenged with proteins from steamed egg, spiced egg and tea-boiled egg, respectively, only weak Th2 responses were induced in their intestine. CONCLUSION: Processing by steaming, spicing, or tea boiling can weaken the allergenicity of egg proteins.
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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".