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Record W4281713925 · doi:10.1007/s11130-022-00984-1

Hypolipidemic Effects of Soy Protein and Isoflavones in the Prevention of Non-Alcoholic Fatty Liver Disease- A Review

2022· review· en· W4281713925 on OpenAlexafffund
Chao-Wu Xiao, Amy Hendry

Bibliographic record

VenuePlant Foods for Human Nutrition · 2022
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCarleton UniversityHealth Canada
FundersHealth Canada
KeywordsLipogenesisFatty liverIsoflavonesMetabolic syndromeSoy proteinEndocrinologyInternal medicineMedicineObesityType 2 diabetesLipolysisDiseasePopulationDiabetes mellitusBiologyLipid metabolismAdipose tissuePathology

Abstract

fetched live from OpenAlex

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease and affects about 25% of the population globally. Obesity and diabetes are the main causes of the disease characterized by excessive accumulation of lipids in the liver. There is currently no direct pharmacological treatments for NAFLD. Dietary intervention and lifestyle modification are the key strategies in the prevention and treatment of the disease. Soy consumption is associated with many health benefits such as decreased incidence of coronary heart disease, type-2 diabetes, atherosclerosis and obesity. The hypolipidemic functions of soy components have been shown in both animal studies and human clinical trials. Dietary soy proteins and associated isoflavones suppressed the formation and accumulation of lipid droplets in the liver and improved NAFLD-associated metabolic syndrome. The molecular mechanism(s) underlying the effects of soy components are mainly through modulation of transcription factors, sterol regulatory element-binding protein-1 and peroxisome proliferator-activated receptor-γ2, and expressions of their target genes involved in lipogenesis and lipolysis as well as lipid droplet-promoting protein, fat-specific protein-27. Inclusion of appropriate amounts of soy protein and isoflavones in the diets might be a useful approach to decrease the prevalence of NAFLD and mitigate disease burden.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.294
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.074
GPT teacher head0.361
Teacher spread0.287 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2022
Admission routes2
Has abstractyes

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