Impacts of endocrine-disrupting chemicals on prostate function and cancer
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
Because of their historical mode of action, endocrine-disrupting chemicals (EDCs) are associated with sex-steroid receptors, namely the two estrogen receptors (ERα and ERβ) and the androgen receptor (AR). Broadly, EDCs can modulate sex-steroid receptor functions. They can also indirectly impact the androgen and estrogen pathways by influencing steroidogenesis, expression of AR or ERs, and their respective activity as transcription factors. Additionally, many of these chemicals have multiple cellular targets other than sex-steroid receptors, which results in a myriad of potential effects in humans. The current article reviews the association between prostate cancer and the endocrine-disrupting functions of four prominent EDC families: bisphenols, phthalates, phytoestrogens, and mycoestrogens. Results from both in vitro and in vivo models are included and discussed to better assess the molecular mechanisms by which EDCs can modify prostate biology. To overcome the heterogeneity of results published, we established common guidelines to properly study EDCs in the context of endocrine diseases. Firstly, the expression of sex-steroid receptors in the models used must be determined before testing. Then, in parallel to EDCs, pharmacological compounds acting as positive (agonists) and negative controls (antagonists) have to be employed. Finally, EDCs need to be used in a precise range of concentrations to modulate sex-steroid receptors and avoid off-target effects. By adequately integrating molecular endocrinology aspects in EDC studies and identifying their underlying molecular mechanisms, we will truly understand their impact on prostate cancer and distinguish those that favor the progression of the disease from those that slow down tumor development.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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