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Record W2608489403 · doi:10.1002/oby.21840

Dietary lactalbumin and lactoferrin interact with inulin to modulate energy balance in obese rats

2017· article· en· W2608489403 on OpenAlexafffund
Arashdeep Singh, Rizaldy C. Zapata, Adel Pezeshki, Prasanth K. Chelikani

Bibliographic record

VenueObesity · 2017
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat AgencyCanada Foundation for InnovationMinistry of Advanced Education, Government of Alberta
KeywordsLactoferrinInulinEndocrinologyLactalbuminChemistryLeptinInternal medicineEnergy balanceLean body massPostprandialEnergy expenditureFood scienceInsulinBody weightObesityMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether diets enriched with the whey protein components lactalbumin and lactoferrin interact additively with inulin to improve energy balance by decreasing food intake and body weight (BW). METHODS: In four experiments, diet-induced obese rats were randomized to diets containing either lactalbumin or lactoferrin at low (20% kcal) or high (40% kcal) doses, and inulin at low (7.5% w/w) or high (15% w/w) doses, alone or in combination. Energy intake (EI), energy expenditure (EE), respiratory quotient (RQ), BW, body composition, plasma insulin, and leptin concentrations were measured. RESULTS: Lactalbumin and inulin at low doses were ineffective, whereas high doses additively decreased EI and RQ. Low doses of lactoferrin and inulin additively decreased EI, BW, fat and lean mass, and RQ. High doses of lactoferrin and inulin additively decreased EI, supra-additively decreased BW, fat, and lean mass, and also decreased RQ and plasma leptin concentrations. CONCLUSIONS: High doses of lactalbumin and inulin additively decreased EI. Importantly, lactoferrin and inulin at both low and high dose combinations, additively or supra-additively, decreased EI, BW, and adiposity.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.581

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 head0.018
GPT teacher head0.292
Teacher spread0.273 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

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