Multiplex analysis platform for evaluation of endocrine disruption of emerging contaminants against human steroid hormone receptors using autobioluminescent yeast bioassay: Application to bisphenols
Bibliographic record
Abstract
Emerging synthetic or derivative chemicals have drawn global concern due to their potential endocrine-disrupting effects, either as agonists or antagonists or through indirect effects on enzymes of signal transduction pathways. Currently, in vitro screening models are limited to poor tolerance, robustness, high cost and need for sphisticated equipment or complex procedures that require high levels of expertise. Herein, a multiplex analysis platform based on autobioluminescent yeast strains, including BLYhERαS, BLYhERβS, BLYhPRS, BLYhARS, BLYhMRS, and BLYhGRS, was developed for use in easy, rapid, robust, and sensitive screening for potential modulation of endocrine systems. Methods were applied to assess endocrine-disrupting effects of Bisphenol A (BPA) and its alternatives. Disrupting activities of bisphenols (BPs) varied greatly, and some BPA substitutes exerted more potent activities than those of BPA. Most BPs were primarily agonists of ERα and ERβ, while others were antagonists to AR, PR, GR, and MR. BPP and BPM exhibited non-monotonic dose-response relationships toward ERα and ERβ, and BHPF and BPG displayed converse disrupting activities to ERα and ERβ. Overall, the bioassay system provides an efficient tool for comprehensive evaluation of endocrine-disrupting activities of emerging contaminants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".