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Record W2944564267 · doi:10.17975/sfj-2018-008

Making Milk Less Allergenic

2018· article· en· W2944564267 on OpenAlexfundvenueno aff
Fiona A. Ewart

Bibliographic record

VenueSTEM Fellowship Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsnot available
FundersDalhousie University
KeywordsFood scienceChemistryFood proteinCaseinFood allergyAmyloid (mycology)PolyphenolMilk proteinProtein aggregationMaillard reactionAllergenPasteurizationFish <Actinopterygii>BiochemistryAllergyBiologyImmunologyAntioxidant

Abstract

fetched live from OpenAlex

The formation of stable aggregates by food proteins is associated with allergenicity. In particular, amyloid formation by the fish allergen parvalbumin was recently shown to favor IgE binding and subsequent allergic recognition. Therefore, reducing amyloid content in an allergenic food might offer a direct way to make that food less likely to trigger an allergy. In this project, protein aggregation and amyloid formation were studied in milk using gel electrophoresis and fluorescence-based assays. The results suggested that ordinary pasteurized milk from the grocery store contained protein aggregates and specifically amyloid. Processing the milk as normally done during food preparation did not appreciably affect general aggregation or amyloid formation. However, the addition of some polyphenol-containing food products to the milk appeared to result in reduced amyloid levels. Moreover, cranberry juice also appeared to reduce amyloid formation by the milk protein casein. These results suggest that the addition of cranberry or other polyphenol-rich foods to milk products for young children may reduce the risk of milk allergy development by diminishing protein aggregation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.122
GPT teacher head0.367
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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