Prenatal ethanol exposure has sex-specific effects on hippocampal long-term potentiation
Bibliographic record
Abstract
Alcohol consumption during pregnancy is deleterious to the developing brain of the fetus and leads to persistent deficits in adulthood. Long-term potentiation (LTP) is a biological model for learning and memory processes and previous evidence has shown that prenatal ethanol exposure (PNEE) affects LTP in a sex specific manner during adolescence. The objective of this study was to determine if there are sex specific differences in adult animals and to elucidate the underlying molecular mechanisms that contribute to these differences. Pregnant Sprague-Dawley dams were assigned to either; liquid ethanol, pair-fed or standard chow diet. In vivo electrophysiology was performed in the hippocampal dentate gyrus (DG) of adult offspring. LTP was induced by administering 400 Hz stimuli. Western blot analysis for glutamine synthetase (GS) and glutamate decarboxylase from tissue of the DG indicated that GS expression was increased following PNEE. Surprisingly, adult females did not show any deficit in N-methyl-D-aspartate (NMDA)-dependent LTP after PNEE. In contrast, males showed a 40% reduction in LTP. It was indicated that glutamine synthetase expression was increased in PNEE females, suggesting that altered excitatory neurotransmitter replenishment may serve as a compensatory mechanism. Ovariectomizing females did not influence LTP in control or PNEE animals, suggesting that circulating estradiol levels do not play a major role in maintaining LTP levels in PNEE females. These results demonstrate the sexually dimorphic effects of PNEE on the ability for the adult brain to elicit LTP in the DG. The mechanisms for these effects are not fully understood, but an increase in glutamine synthetase in females may underlie this phenomenon.
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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".