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Record W2316550102 · doi:10.1021/jf403405r

Revisiting the Prospects of Plastein: Thermal and Simulated Gastric Stability in Relation to the Antioxidative Capacity of Casein Plastein

2013· article· en· W2316550102 on OpenAlexaff
Chibuike C. Udenigwe, Sihong Wu, Kiesha Drummond, Min Gong

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChemistryCaseinProteaseHydrolysisLipid peroxidationBiochemistryProteasesChelationPeptideProteolysisEnzymeChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Plastein, a product of protease-induced peptide aggregation, is thought to possess unique physical properties and bioactivity, although its formation, stability, and functional mechanisms remain unclear. This study demonstrates that plastein is formed from bovine casein peptides with Alcalase by hydrophobic and electrostatic interactions and less likely by covalent bonding. The peptide aggregation enhanced the Fe(III) reducing potential and decreased the Fe(II) chelating activity (p < 0.05) of casein peptides, but there was no difference in inhibition of Fe-induced linoleic acid peroxidation after plastein reaction. The casein plastein product retained its antioxidative activities after being heated at 100 °C. However, simulated gastric protease treatment with pepsin and pancreatic enzymes resulted in enhanced reducing potential and metal chelation of the casein plastein and reduction of the inhibitory effect on lipid peroxidation. It appears that the plasteins were disintegrated and further hydrolyzed by gastric proteases on the basis of the antioxidative capacity and RP-HPLC profile being similar to those of the casein hydrolysates. Therefore, plastein reaction may not confer metabolic stability or enhance the antioxidative capacity of casein peptides for prospective functional food applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.196
Teacher spread0.187 · 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.

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

Citations23
Published2013
Admission routes1
Has abstractyes

Explore more

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