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Record W2506759640 · doi:10.1021/bk-2008-0993.ch019

Antioxidative Stress Peptides

2008· book-chapter· en· W2506759640 on OpenAlexaff
Yoshinori Mine, Shigeru Katayama

Bibliographic record

VenueACS symposium series · 2008
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOxidative stressReactive oxygen speciesLipid peroxidationChemistryGlutathioneBiochemistryAntioxidantHydrogen peroxideOxidative phosphorylationInflammationCell biologyEnzymeImmunologyBiology

Abstract

fetched live from OpenAlex

Oxidative stress is a state characterized by an excess of reactive oxygen species (ROS) in the body, which creates a potentially unstable cellular environment linked to tissue damage, degenerative disease, and accelerated aging. Many environmental factors are implicated in the generation of ROS. Specific dietary antioxidants have been considered to modulate oxidative stress and suppress gut inflammation and carcinogenesis. Recently, we have developed phosphopeptides (PPPs) from hen egg yolk phosvitin and found PPPs act as inhibitors of lipid peroxidation and radical scavengers. In the present study, the protective effects of PPPs against hydrogen peroxide (H 2 O 2 )-induced oxidative stress in an in vitro assay was evaluated using human intestinal epithelial cells, Caco-2. The effects of PPPs on intracellular glutathione (GSH) levels and several antioxidative enzymes were also investigated. The results suggest that PPPs could suppress oxidative stressinduced gut inflammatory disorders and may potentially provide new bioactive peptides against tissue oxidative stress.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.021

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 head0.010
GPT teacher head0.213
Teacher spread0.204 · 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 designBench or experimental
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

Citations1
Published2008
Admission routes1
Has abstractyes

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