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Record W1847231704 · doi:10.1039/c5fo00654f

Interfacial dilational properties of tea polyphenols and milk proteins with gut epithelia and the role of mucus in nutrient adsorption

2015· article· en· W1847231704 on OpenAlexafffund
Anilda Guri, Yang Li, Milena Corredig

Bibliographic record

VenueFood & Function · 2015
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversity of Guelph
FundersCanada Research Chairs
KeywordsMucusChemistryMucinAdsorptionRheologyAbsorption (acoustics)ViscoelasticityBiophysicsFood scienceBiochemistryChemical engineeringOrganic chemistryMaterials scienceBiologyComposite material

Abstract

fetched live from OpenAlex

By interacting with nutrients, the mucus layer covering the intestinal epithelium may mediate absorption. This study aimed to determine possible interactions between epigallocatechin-3-gallate (EGCG), skim milk proteins or their complexes with human intestinal mucin films. The films were extracted from postconfluent monolayers of HT29-MTX, a human intestinal cell line, and a model system was created using drop shape tensiometry. The EGCG uptake tested in vitro on postconfluent Caco-2 cells or co-cultures of Caco-2/HT29-MTX (mucus producing) showed recovery of bioavailable EGCG only for Caco-2 cell monolayers, suggesting an effect of mucus on absorption. Interfacial dilational rheology was employed to characterize the properties of the interface mixed with mucus dispersion. Adsorption of polyphenols greatly enhanced the viscoelastic modulus of the mucus film, showing the presence of interactions between the nutrient molecules and mucus films. On the other hand, in situ digestion of milk proteins using trypsin showed higher surface activities as a result of protein unfolding and competitive adsorption of the hydrolyzed products. There was an increase of viscoelastic modulus over the drop ageing time for the mixed interfaces, indicating the formation of a stiffer interfacial network. These results bring new insights into the role of the mucus layer in nutrient absorption and the interactions of mucus and dairy products.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.180

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 head0.015
GPT teacher head0.197
Teacher spread0.182 · 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 designObservational
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

Citations22
Published2015
Admission routes2
Has abstractyes

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