Effect of soy proteins and isoflavones on lipid metabolism and involved gene expression
Bibliographic record
Abstract
Clinical trials and animal studies showed that ingestion of soy proteins improves blood lipid profiles including lowering triglyceride, total and LDL cholesterol levels and increasing HDL cholesterol content. However, the effective components in the soy and the mechanisms involved in the hypolipidemic actions are not fully understood. Increasing evidence from animal studies have suggested that soy components may regulate lipid metabolism by modulating the activities of key transcription factors and thereby changing the downstream gene expression involved in lipogenesis or lipolysis. It has been shown that intake of soy proteins alters the expression of genes for sterol regulatory element binding protein, peroxisomal proliferator activated receptor, and liver X receptor. Dietary soy proteins suppress the DNA binding activities of hepatic nuclear receptors for thyroid hormones and retinoic acid, and alter the activities of key enzymes including cholesterol 7alpha hydroxylase and ATPase/ATP synthase through post-translational protein modifications. This paper reviews the current understanding of the cellular and molecular events by which soy components affect lipid levels, especially focusing on modulation of transcription factors and regulation of gene expression involved in lipid metabolism by soy proteins and associated isoflavones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".