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A preliminary report on the feeding of cynomolgus monkeys (Macaca fascicularis) with a high-sugar high-fat diet for 33 weeks

2011· article· en· W2118964248 on OpenAlexfundno aff
James N. Mubiru, Magdalena Garcia‐Forey, Paul B. Higgins, Peggah Hemmat, Nicole Cavazos, Edward J. Dick, Michael Owston, Cassondra A. Bauer, Robert E. Shade, Anthony G. Comuzzie, Jeffrey Rogers

Bibliographic record

VenueJournal of Medical Primatology · 2011
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsnot available
FundersNational Institutes of HealthNational Center for Research ResourcesMcGill University
KeywordsEndocrinologyInternal medicineCholesterolBiologyObesityTrunkMetabolic syndromeBlood sugarHigh-density lipoproteinBody weightMedicinePhysiologyDiabetes mellitus

Abstract

fetched live from OpenAlex

BACKGROUND: The metabolic syndrome is common in populations exposed to a typical Western diet. There is a lack of an animal model that mimics this condition. METHODS: We fed 15 cynomolgus monkeys ad libitum a high-sugar high-fat (HSHF) diet for 33 weeks. Body weight, body composition, serum lipids, and insulin were measured at baseline and at 33 weeks. RESULTS: The animals tolerated the HSHF diet very well. In the intervention group, total serum cholesterol and low-density lipoprotein cholesterol were 3- and 5-fold higher, respectively, at 33 weeks as compared with their baseline levels. Serum high-density lipoprotein cholesterol and triglycerides were not significantly affected. Dual-energy X-ray absorptiometry (DXA) analysis of the intervention group indicated that the trunk fat mass increased by 187% during this period. CONCLUSIONS: Cynomolgus monkeys should be a useful model for investigating the interactions of diet and other factors such as genetics in the development of the metabolic syndrome.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.318
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2011
Admission routes1
Has abstractyes

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