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Record W2011699502 · doi:10.5625/lar.2012.28.4.265

Anti-obesity effects of Rapha diet® preparation in mice fed a high-fat diet

2012· article· en· W2011699502 on OpenAlexaff
Jihyun Kim, Jangbeen Kyung, Dajeong Kim, Ehn‐Kyoung Choi, Paul Bang, Dongsun Park, Yun-Bae Kim

Bibliographic record

VenueLaboratory Animal Research · 2012
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsWelichem Biotech (Canada)
FundersMinistry of Education, Science and TechnologyNational Research Foundation of KoreaNational Research Foundation
KeywordsInternal medicineAdipose tissueEndocrinologyObesitySteatosisWhite adipose tissueAdipose capsule of kidneyBiologyChemistryMedicineKidney

Abstract

fetched live from OpenAlex

The anti-obesity activities of Rapha diet® preparation containing silkworm pupa peptide, Garcinia cambogia, white bean extract, mango extract, raspberry extract, cocoa extract, and green tea extract were investigated in mice with dietary obesity. Male C57BL/6 mice were fed a high-fat diet (HFD) containing 3% Rapha diet® preparation for 8 weeks, and blood and tissue parameters of obesity were analyzed. The HFD markedly enhanced body weight gain by increasing the weights of epididymal, perirenal, and mesenteric adipose tissues. The increased body weight gain induced by HFD was significantly reduced by feeding Rapha diet® preparation, in which decreases in the weight of abdominal adipose tissue and the size of abdominal adipocytes were confirmed by microscopic examination. Long-term feeding of HFD increased blood triglycerides and cholesterol levels, leading to hepatic lipid accumulation. However, Rapha diet® preparation not only reversed the blood lipid levels, but also attenuated hepatic steatosis. The results indicate that Rapha diet® preparation could improve HFD-induced obesity by reducing both lipid accumulation and the size of adipocytes.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.424
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.355
Teacher spread0.328 · 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

Citations17
Published2012
Admission routes1
Has abstractyes

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