Effects of advanced maternal age and acute prenatal alcohol exposure on mouse offspring growth and craniofacial phenotype
Bibliographic record
Abstract
BACKGROUND: Prenatal alcohol exposure (PAE) can result in developmental defects that include growth restriction, craniofacial anomalies, and cognitive behavioral deficits, though the presence and severity of these adverse outcomes can vary dramatically among exposed individuals. Preclinical animal models have demonstrated that the dose and timing of PAE account for much, but not all, of this phenotypic variation, suggesting that additional factors mitigate the effects of PAE. Here, we used a mouse model to investigate whether maternal age modulates the effects of PAE on the severity and variation in offspring growth and craniofacial outcomes. METHODS: Nulliparous C57BL/6N dams received either an intraperitoneal injection of ethanol (EtOH) or vehicle solution on gestational day 7.5. Dams were divided into four groups: (1) EtOH-treated young dams (6 to 10 weeks); (2) control young dams; (3) EtOH-treated old dams (6 to 7 months); and (4) old control dams. Neonate offspring growth restriction was measured through body mass and organ-to-body mass ratios, while skeletal craniofacial features were imaged using micro-CT and analyzed for size, shape, and variation. RESULTS: PAE and advanced maternal age each increased the risk of low birthweight and growth restriction in offspring, but these factors in combination changed the nature of the growth restriction. Similarly, both PAE and advanced maternal age individually caused changes to craniofacial morphology such as smaller skull size, dysmorphic skull shape, and greater skull shape variation and asymmetry. Interestingly, while the combination of PAE and advanced maternal age did not affect mean skull shape or size, it significantly increased the variation and asymmetry of those measures. CONCLUSION: Our results indicate that maternal age modulates the effects of PAE, but that the effects of this combination on offspring outcomes are more complex than simply scaling the effects of either factor.
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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) | 0.001 | 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) | 0.002 | 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".