Prenatal alcohol and cannabis exposure can have opposing and region‐specific effects on parvalbumin interneuron numbers in the hippocampus
Bibliographic record
Abstract
BACKGROUND: We recently showed that alcohol and cannabis can interact prenatally, and in a recent review paper, we identified parvalbumin-positive (PV) interneurons in the hippocampus as a potential point of convergence for these teratogens. METHODS: A 2 (Ethanol [EtOH], Air) × 2 (tetrahydrocannabinol [THC], Vehicle) design was used to expose pregnant Sprague-Dawley rats to either EtOH or air, in addition to either THC or the inhalant vehicle solution, during gestational days 5-20. Immunohistochemistry was performed to detect PV interneurons in 1 male and 1 female pup from each litter at postnatal day 70. RESULTS: Significant between-group and subregion-specific effects were found in the dorsal cornu ammonis 1 (CA1) subfield and the ventral dentate gyrus (DG). In the dorsal CA1 subfield, there was an increase in the number of PV interneurons in both the EtOH and EtOH +THC groups, but a decrease with THC alone. There were fewer changes in interneuron numbers overall in the DG, though there was a sex difference, with a decrease in the number of PV interneurons in the THC-exposed group in males. There was also a greater cell layer volume in the DG in the EtOH +THC group than the control group, and in the CA1 region in the EtOH group compared to the control and THC groups. CONCLUSIONS: Prenatal exposure to alcohol and THC differentially affects parvalbumin-positive interneuron numbers in the hippocampus, indicating that both individual and combined exposure can impact the balance of excitation and inhibition in a structure critically involved in learning and memory processes.
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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".