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Record W2018362021 · doi:10.3177/jnsv.53.345

Royal Jelly Supplementation Improves Lipoprotein Metabolism in Humans

2007· article· en· W2018362021 on OpenAlexaff
Hang Guo, Ai Saiga, Mikako Sato, Izumi Miyazawa, Makoto Shibata, Yoshihisa Takahata, Fumiki Morimatsu

Bibliographic record

VenueJournal of Nutritional Science and Vitaminology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsConestoga Meat Packers (Canada)
Fundersnot available
KeywordsVery low-density lipoproteinLipoproteinTriglycerideEndocrinologyCholesterolHigh-density lipoproteinInternal medicineLow-density lipoproteinMetabolismLipoprotein(a)Lipid metabolismBiologyChemistryMedicine

Abstract

fetched live from OpenAlex

Royal jelly (RJ) has several physiological effects and is widely used in commercial medical products and health foods. We examined the effects of RJ supplementation on serum lipoprotein metabolism in humans. Fifteen volunteers were divided into an RJ intake group (n=7) and a control group (n=8). The RJ group took 6 g per day for 4 wk. Their serum total cholesterol (TC) and serum low-density lipoprotein (LDL) decreased significantly compared with those of the control group (p<0.05). There were no significant differences in serum high-density lipoprotein (HDL) or triglyceride concentrations. Moreover, the relationship between the serum cholesterol and lipoprotein levels was investigated. Among the lipoprotein fractions, small very-low-density lipoprotein was decreased (p<0.05) after RJ intake. Our results suggest that dietary RJ decreases TC and LDL by lowering small VLDL levels.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.015
GPT teacher head0.248
Teacher spread0.234 · 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

Citations109
Published2007
Admission routes1
Has abstractyes

Explore more

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