Effect of heat processing on antibody reactivity to allergen variants and fragments of black tiger prawn: A comprehensive allergenomic approach
Bibliographic record
Abstract
SCOPE: Prawn allergy is one of the leading causes of IgE-mediated hypersensitivity to food. Alterations of IgE-antibody reactivity to prawn allergens due to thermal processing are not fully understood. The aim of this study was to analyze the impact of heating on prawn allergens using a comprehensive allergenomic approach. METHODS AND RESULTS: Proteins from raw and heat-processed black tiger prawn (Penaeus monodon) extracts as well as recombinant tropomyosin (rPen m1) were analyzed by SDS-PAGE and immunoblotting using sera from 16 shellfish allergic patients. IgE antibody binding proteins were identified by advanced mass spectroscopy, characterized by molecular structure analysis and their IgE reactivity compared among the prepared black tiger prawn extracts. Heat processing enhanced the overall patient IgE binding to prawn extracts and increased recognition of a number of allergen variants and fragments of prawn allergens. Allergens identified were tropomyosin, myosin light chain, sarcoplasmic calcium binding protein, and putative novel allergens including triose phosphate isomerase, aldolase, and titin. CONCLUSION: Seven allergenic proteins are present in prawns, which are mostly heat-stable and form dimers or oligomers. Thermal treatment enhanced antibody reactivity to prawn allergens as well as fragments and should be considered in the diagnosis of prawn allergy and detection of crustacean allergens in processed food.
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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".