Trace elements alone or in mixtures associated with unconventional natural gas exploitation affect rat fetal steroidogenesis and testicular development in vitro
Bibliographic record
Abstract
Biomonitoring studies have shown that pregnant women living in regions of unconventional natural gas (UNG) exploitation have higher levels of trace elements. Whether developmental endocrine disruption can be expected at these exposure levels during pregnancy is unclear. In this study, we aimed to test the impact of five trace elements alone or in mixtures using in vitro cell- and tissue-based assays relevant to endocrine disruption and development. Manganese, aluminum, strontium, barium, and cobalt were tested at concentrations including those representatives of human fetal exposure. Using transactivation assays, none of the tested elements nor their mixture altered the human estrogen receptor 1 or androgen receptor genomic signalling. In the rat fetal testis assay, an organ culture system, cobalt (5 μg/l), barium (500 μg/l) and strontium (500 μg/l) significantly increased testosterone secretion. Cobalt and strontium were associated with hyperplasia and/or hypertrophy of fetal Leydig cells. Mixing the five elements at concentrations where none had an effect individually stimulated testosterone secretion by the rat fetal testis paralleled by the significant increase of 3β-hydroxysteroid dehydrogenase protein level in comparison to the vehicle control. The mechanisms involved may be specific to the fetal testis as no effect was observed in the steroidogenic H295R cells. Our data suggest that some trace elements in mixture at concentrations representative of human fetal exposure can impact testis development and function. This study highlights the potential risk posed by UNG operations, especially for the most vulnerable populations, pregnant individuals, and their fetus.
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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) | 0.001 | 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".