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Immunomodulatory Properties of Fermented Soy and Dairy Milks Prepared with Lactic Acid Bacteria

2009· article· en· W2017291903 on OpenAlexaff
Lisa E. Wagar, Claude P. Champagne, Nicole D. Buckley, Yves Raymond, Julia M. Green-Johnson

Bibliographic record

VenueJournal of Food Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsCanadian Space AgencyOntario Tech University
Fundersnot available
KeywordsFood scienceStreptococcus thermophilusFermentationLactobacillus helveticusLactic acidBifidobacteriumBifidobacterium longumLactobacillusChemistryBacteriaBiology

Abstract

fetched live from OpenAlex

Fermented soy and dairy milk preparations provide a means for delivering lactic acid bacteria and their fermentation products into the diet. Our aims were to test immunomodulatory bioactivity of fermented soy beverage (SB) and dairy milk blend (MB) preparations on human intestinal epithelial cells (IEC) and to determine the impact of freezing medium on culture survival prior to bioactivity analyses. Fermented SB and MB were prepared using pure or mixed cultures of Streptococcus thermophilus ST5, Bifidobacterium longum R0175, and Lactobacillus helveticus R0052. Immunomodulatory bioactivity was assessed by testing selected SB and MB ferments on tumor necrosis factor alpha (TNFalpha)-treated IEC and measuring effects on Interleukin-8 (IL-8) production. Impact of timing of ferment administration relative to this pro-inflammatory challenge was investigated. The most pronounced reductions in IEC IL-8 production were observed when IEC were treated with either SB or MB ferment preparations prior to TNFalpha challenge. These results indicate that freezing-stable MB and SB ferments prepared with selected strains can modulate IEC IL-8 production in vitro, and suggest that yogurt-like fermented soy formulations could provide a functional food alternative to milk-based fermented 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.118

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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations73
Published2009
Admission routes1
Has abstractyes

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