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Record W4415355040 · doi:10.1016/j.psj.2025.105980

Protein oxidation in feed: Occurrence, monitoring, and mitigation strategies

2025· article· en· W4415355040 on OpenAlexaff
Peng Lu, Peng Wang, Yanmin Zhou, Chengbo Yang

Bibliographic record

VenuePoultry Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAntioxidantAmino acidFood proteinReactive oxygen speciesDigestion (alchemy)Oxidation reductionCovalent bondProtein quality

Abstract

fetched live from OpenAlex

Protein oxidation is defined as a covalent modification caused by reactive oxygen species (ROS) or secondary oxidized by-products. The mechanisms and influences of protein oxidation on animal-derived food quality have been studied for many years, but its effects on feedstuff and animal health are largely unknown. Protein oxidation causes oxidation in both amino acid side chains and protein backbones, resulting in the formation of harmful substances and a decrease in nutritional value. Excessive levels of protein oxidation in animal diets have been associated with various biological consequences in animals, including reduced growth performance, compromised antioxidant capacity, immune disorders, and intestinal digestion and absorption, and antioxidant capacity. Protein oxidation in feeds can be detected by various methods such as spectrophotometric assay and fluorescence spectroscopy and controlled by several approaches such as supplementation of antioxidants and nutrition regulation. In this review, the basic principles of protein oxidation and its detection methods, effects on animals, and preventive strategies are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.288
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes1
Has abstractyes

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