Transplacental benzene exposure increases tumor incidence in mouse offspring: possible role of fetal benzene metabolism
Bibliographic record
Abstract
Childhood cancer is the leading cause of disease-related death in children aged 1-14 years in Canada and the USA and it has been hypothesized that transplacental exposure to environmental carcinogens such as benzene may contribute to the etiology of these cancers. Our objectives were to determine if transplacental benzene exposure increased tumor incidence in mouse offspring and assess fetal benzene metabolism capability. Pregnant CD-1 and C57Bl/6N mice were given intraperitoneal injections of corn oil, 200 mg/kg, or 400 mg/kg benzene on gestational days 8, 10, 12 and 14. A significant increase in tumor incidence was observed in CD-1, but not C57BL/6N, 1-year-old offspring exposed transplacentally to 200 mg/kg benzene. Hepatic and hematopoietic tumors were predominantly observed in male and female CD-1 offspring, respectively. Female CD-1 offspring exposed transplacentally to 200 mg/kg benzene had significantly suppressed bone marrow CD11b(+) cells 1 year after birth, correlating with reduced colony-forming unit granulocyte/macrophage numbers in 2-day-old pups. CD-1 and C57Bl/6N maternal blood benzene levels and fetal liver benzene, t, t-muconic acid, hydroquinone and catechol levels were analyzed by gas chromatography/mass spectrometry. Significant strain-, gender- and dose-related differences were observed. Male CD-1 fetuses had high hydroquinone levels, whereas females had high catechol levels after maternal exposure to 200 mg/kg benzene. This is the first demonstration that transplacental benzene exposure can induce hepatic and hematopoietic tumors in mice, which may be dependent on fetal benzene metabolism capability.
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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 | 0.001 | 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 | 0.001 |
| 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".