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Record W3049490081 · doi:10.3390/nu12082465

Synergistic Effects of Heat-Killed Kefir Paraprobiotics and Flavonoid-Rich Prebiotics on Western Diet-Induced Obesity

2020· article· en· W3049490081 on OpenAlexaff
Kun‐Ho Seo, Jaewoon Jeong, Hyunsook Kim

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Prince Edward Island
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsPrebioticObesityFood scienceAdipose tissueWeight gainBiologyChemistryInternal medicineEndocrinologyMedicineBody weight

Abstract

fetched live from OpenAlex

The synergistic anti-obesity effect of paraprobiotic heat-killed lactic acid bacteria (HLAB) and prebiotics has not been studied. To determine the anti-obesity properties of prebiotic polyphenol-rich wine grape seed flour (GSF) and paraprobiotic HLAB, C57BL/6J mice were administered a high-fat and high-fructose diet (HFFrD) with 5% microcrystalline cellulose (CON), HFFrD supplemented with 2.5% GSF, HFFrD with orally administered HLAB, or HFFrD with a combination of GSF and orally administered HLAB (GSF+HLAB) for 8 weeks. Compared with the CON group, the GSF and HLAB groups both showed significant reductions in HFFrD-induced body weight gain and adipose tissue weights (p < 0.05). Interestingly, combined supplementation with GSF and HLAB revealed statistically significant synergistic effects on body weight gain, visceral adiposity, and plasma triacylglycerol concentrations (p < 0.05). The synergistic action was significantly related to a decreased adipocyte gene expression in fatty acid synthesis and inflammation metabolism. In conclusion, the combination of prebiotic GSF and paraprobiotic kefir HLAB is potentially useful, as natural food ingredients, in the prevention of obesity and obesity-related diseases, especially for immunocompromised individuals.

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 score0.033
Threshold uncertainty score0.785

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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