High-pressure and temperature autoclaving of peanuts reduces the proportion of intact allergenic proteins
Bibliographic record
Abstract
BACKGROUND Peanut allergy is a particularly common cause of anaphylaxis and utilization of hospital emergency room resources. Peanut protein allergens do not appear to denature under normal cooking conditions. We evaluated the effects of thermal processing on the protein allergens Ara h 2, associated with a risk for anaphylaxis, and Ara h 8, a protein analogous to birch pollen associated with oral allergy symptoms. METHODS Raw, roasted and autoclaved peanuts were evaluated. Solution 1H NMR spectroscopy was used to obtain molecular profiles and identify chemical changes across processing conditions. Western blot and ELISA analyses were used to detect relative levels of specific peanut allergens. RESULTS NMR analysis of peanut-soaked solutions demonstrated an overall reduction of total intact protein in autoclaved peanuts as shown by the broadening of peaks in the spectral regions corresponding to peptide fragments when compared to raw. The results also showed that autoclaving reduces the amount of allergenic proteins Ara h 2 (50% reduction) and Ara h 8 (100% reduction). Upon skin prick testing of allergic subjects, this differential degradation demonstrated that the autoclaved peanut could be used to categorize patients into two groups: those at risk for anaphylaxis and those who only experience oral symptoms to peanut (predominantly Ara h 2- and Ara h 8-specific IgE, respectively). CONCLUSION The data reported in this study suggest that high-pressure and temperature autoclaving reduces the amount of intact protein in the peanut, including allergenic proteins. This could be further developed into an improved diagnostic test for peanut 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".