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Record W2812191565 · doi:10.1039/c8fo00589c

A spent hen muscle protein hydrolysate: a potential IL-10 stimulator in a murine model

2018· article· en· W2812191565 on OpenAlexafffund
Wenlin Yu, Catherine J. Field, Jianping Wu

Bibliographic record

VenueFood & Function · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat Agency
KeywordsHydrolysateMuscle proteinChemistryBiochemistryFood scienceBiologyCell biologySkeletal muscleAnatomy

Abstract

fetched live from OpenAlex

Dietary proteins harbour bioactive peptides that exert various physiological activities. Chicken meat prepared from spent layers from the egg industry is an inexpensive source of protein for the production of bioactive peptides. This study explored the effect of hen muscle-derived peptides prepared by enzymatic hydrolysis on immune functions. The hydrolysate was incorporated into the diet of weanling Sprague-Dawley rats (n = 8 per diet) for 3 weeks at 2% or 5% addition (w/w diet). At a dose of 5% (w/w) the hydrolysate exhibited immunomodulatory effects on splenocytes, including a lower proportion of OX6+ (professional antigen presenting cells) and a higher proportion of CD11b/c+ cells (macrophages/monocytes) (p < 0.05) compared to the isonitrogenous control diet. Meanwhile, the production of anti-inflammatory cytokine interleukin (IL)-10 by splenocytes stimulated ex vivo with mitogens was significantly higher from hydrolysate treatment; there was no significant difference in the other cytokines (IL-1β, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, IL-6 and IL-2) investigated. Supplementing with the hydrolysate did not alter the growth, food intake and organ weights in young rodents. These results indicated that the spent hen muscle protein hydrolysate has the potential to be developed for value-added products with anti-inflammatory properties.

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 score0.110
Threshold uncertainty score0.718

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 head0.223
Teacher spread0.213 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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