Human-Based Exposure Levels of Perfluoroalkyl Acids May Induce Harmful Effects to Health by Disrupting Major Components of Androgen Receptor Signalling In Vitro
Bibliographic record
Abstract
Abstract Perfluoroalkyl acids (PFAAs) are detectable in human blood. PFAA exposure may contribute to androgen receptor (AR)-related health effects such as prostate cancer (PCa). In Norway and Sweden, exposures to PFAAs and PCa are very real concerns. In vitro studies conventionally do not investigate PFAA-induced AR disruption at human blood-based concentrations, thus limiting application to human health. We aim to determine the endocrine disrupting activity of PFAAs based upon human exposure levels, on AR transactivation and translocation. PFAAs (PFOS, PFOA, PFNA, PFDA, PFHxS, and PFUnDA) were tested at concentrations ranging from 1/10 × to 500 × relative to human blood based upon the exposure levels observed in a Scandinavian population. Translocation was measured by high content analysis (HCA) and transactivation was measured by reporter gene assay (RGA). No agonist activity (translocation or transactivation) was detected for any PFAAs. In the presence of testosterone, AR translocation increased following exposure to PFOS 1/10 × and 100 ×, PFOA 1/10 ×, and PFNA 1 × and 500 × (P < 0.05). In the presence of testosterone, PFOS 500 × antagonised AR transactivation, whereas PFDA 500 × increased AR transactivation (P < 0.05). PFAAs may contribute to AR-related adverse health effects such as PCa. PFAAs can disrupt AR signalling via two major components: translocation and transactivation. PFAAs which disrupt one signalling component do not necessarily disrupt both. Therefore, to fully investigate the disruptive effect of human exposure-based contaminants on AR signalling, it is imperative to analyse multiple molecular components as not all compounds induce a disruptive effect at the same level of receptor signalling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".