Nuclear Receptors: Potential Biomarkers for Assessing Physiological Functions of Soy Proteins and Phytoestrogens
Bibliographic record
Abstract
Soy consumption is associated with decreased incidence of chronic diseases, including cardiovascular diseases, atherosclerosis, diabetes, osteoporosis, and certain types of cancers. However, consumption of high amounts of soy isoflavones may adversely influence endocrine functions, such as thyroid function and reproductive performance, because of their structural similarity to endogenous estrogens. Nuclear receptors are a group of transcription factors that play critical roles in the regulation of gene expression and physiological functions through direct interaction with target genes. Modulation of the abundance of these receptors, such as changing their gene expression, alters the sensitivity of the target cells or tissues to the stimulation of ligands, and eventually affects the relevant physiological functions, such as growth, development, osteogenesis, immune response, lipogenesis, reproductive process, and anticarcinogenesis. A number of studies have shown that the bioactive components in soy can modify the expression of these receptors in various tissues and cancer cells, which is believed to be a key intracellular mechanism by which soy components affect physiological functions. This review summarizes the current understanding of the modulation of nuclear receptors by soy proteins and isoflavones, and focuses especially on the receptors for estrogens, progesterone, androgen, vitamin D, retinoic acid, and thyroid hormones as well as the potential impact on physiological functions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".