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Record W2772689259 · doi:10.1021/acs.jafc.7b04562

Identification of New Anti-inflammatory Peptides from Zein Hydrolysate after Simulated Gastrointestinal Digestion and Transport in Caco-2 Cells

2017· article· en· W2772689259 on OpenAlexafffund
Qiufang Liang, Meram Chalamaiah, Xiaofeng Ren, Haile Ma, Jianping Wu

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsHydrolysateCaco-2Gastrointestinal tractChemistryProteasesDigestion (alchemy)BiochemistryTumor necrosis factor alphaCell adhesionInflammationPeptideCellBiologyEnzymeImmunologyHydrolysisChromatography

Abstract

fetched live from OpenAlex

Chronic inflammation is an underlying contributor to various chronic diseases. The objectives of this study were to investigate the anti-inflammatory activity of zein hydrolysate after simulated gastrointestinal digestion and Caco-2 cell absorption and to identify novel anti-inflammatory peptides after transport across Caco-2 cells. Three zein hydrolysates were prepared and further digested using gastrointestinal proteases; their transports were studied in Caco-2 cells. Anti-inflammatory activity was studied in endothelial EA.hy926 cells. Three zein hydrolysates and their digests significantly decreased the expression of tumor necrosis factor-α (TNF-α) induced pro-inflammatory vascular cell adhesion molecule-1 (VCAM-1) by 37.3-66.0%. Eleven novel peptides with 5-9 amino acid residues were sequenced; three peptides showed strong anti-inflammatory activity by inhibiting the VCAM-1 by 54-38.9% and intercellular cell adhesion molecule-1 (ICAM-1) by 36.5-28.6% at 0.2 mM. A new approach to identify novel anti-inflammatory peptides that could survive gastrointestinal digestion and absorption was developed.

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 score0.367
Threshold uncertainty score0.371

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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

Citations104
Published2017
Admission routes2
Has abstractyes

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