In utero benzene exposure in CD-1 mice results in increased fetal and placental size at gestational day 19, which is dependent upon intra-litter variables
Bibliographic record
Abstract
Benzene is a common environmental pollutant and carcinogen. In a previous mouse study, we found that transplacental benzene exposure increased tumor incidence in offspring, with apparent sex differences. Mouse models are widely used in developmental toxicity studies; however, they are litter-bearing and show intra-litter variability. This study sought to determine if in utero benzene exposure affects fetal and placental growth with consideration for intra-litter variables. Pregnant CD-1 mice were exposed to corn oil (vehicle control) or 200 mg/kg benzene from gestational days (GD) 8-14 and sacrificed on GD 19. Fetal-placental units were grouped by exposure, sex, and uterine horn location. Fetus and placenta growth measurements were normalized to maternal weight gain and total live litter size or the number of live fetuses in the uterine horn. In utero benzene exposure did not affect fetal survival or the distribution of fetuses between sexes or uterine horns, but did significantly increase fetal and placental size, particularly in the left uterine horn. Both male and female fetuses showed increased weight, head-rump length, head circumference, and abdominal circumference, with the left horn most affected. Placental weight and diameter also increased, with larger placentas in the left horn. Fetal heart weight increased, especially in female fetuses. Placental efficiency was not significantly altered, though benzene-exposed females had higher efficiency than males. These findings suggest that benzene induces placental toxicity, leading to altered growth, and emphasize the need to account for intra-litter variability in xenobiotic-induced developmental toxicity studies.
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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) | 0.003 | 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".