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Record W2135955217 · doi:10.1017/s1751731111001194

Regulation of soy isoflavones on weight gain and fat percentage: evaluation in a Chinese Guangxi minipig model

2011· article· en· W2135955217 on OpenAlexaff
F. N. Li, L. L. Li, Huansheng Yang, Xiaoxue Yuan, B. Zhang, Mingyang Geng, Chao-Wu Xiao, Yulong Yin

Bibliographic record

Venueanimal · 2011
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsHealth Canada
Fundersnot available
KeywordsIsoflavonesAdipose tissueEndocrinologyInternal medicineSOY ISOFLAVONESSoy proteinWeight gainBiologyBody weightMedicineFood science

Abstract

fetched live from OpenAlex

This study was conducted to determine the effects of soy isoflavones on changes in body and tissue weight and on insulin-like factor I (IGF-I) and peroxisome proliferator-activated receptor-γ (PPARγ) gene and protein expression in muscle and adipose tissues in Chinese Guangxi minipig, as a model for studying human nutrition. A total of 72 male Chinese Guangxi minipigs were fed basal diet (control, Con), low dose of soy isoflavones and high dose of soy isoflavones (HSI). The results showed that HSI increased the body weight (BW) gain and fat percentage of minipigs (P < 0.05). In addition, the serum concentrations of IGF-I and interleukin-6 were increased by high levels of soy isoflavones (P < 0.05). Furthermore, a diet containing soy isoflavones enhanced IGF-I mRNA expression levels in longissimus muscle, but decreased these levels in perirenal fat. However, the mRNA and protein expression levels of PPARγ in longissimus muscle and subcutaneous adipose tissue were both increased when compared with the Con. The data indicated that soy isoflavones regulated the BW gain and fat percentage of Chinese Guangxi minipigs, which also showed changes in IGF-I system and PPARγ. However, further research is required to clarify the causative relationship.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.230

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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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

Citations23
Published2011
Admission routes1
Has abstractyes

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