Effect of soy proteins and isoflavones on lipid metabolism and involved gene expression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it