Prenatal ethanol and cannabis exposure have sex‐ and region‐specific effects on somatostatin and neuropeptide Y interneurons in the rat hippocampus
Bibliographic record
Abstract
BACKGROUND: Cannabis is increasingly being legalized and socially accepted around the world and is often used with alcohol in social settings. We recently showed that in utero exposure to both substances can alter the density of parvalbumin-expressing interneurons in the hippocampus. Here we investigate the effects of in utero alcohol and cannabis exposure, alone or in combination, on somatostatin- and neuropeptide Y-positive (NPY) interneurons. These are separate classes of interneurons important for network synchrony and inhibition in the hippocampus. METHODS: A 2 (Ethanol, Air) × 2 (tetrahydrocannabinol [THC], Vehicle) design was used to expose pregnant Sprague-Dawley rats to either ethanol or air, in addition to either THC or the inhalant vehicle solution, during gestational days 5-20. Immunohistochemistry for somatostatin- and NPY-positive interneurons was performed in 50 μm tissue sections obtained at postnatal day 70. RESULTS: Exposure to THC in utero had region-specific and sex-specific effects on the density of somatostatin-positive interneurons in the adult rat hippocampus. A female-specific decrease in NPY interneuron cell density was observed in the CA1 region following THC exposure. Combined exposure to alcohol and THC reduced NPY neurons selectively in the ventral dentate gyrus hippocampal subfield. However, overall, co-exposure to alcohol and cannabis had neither additive nor synergistic effects on interneuron populations in other areas of the hippocampus. CONCLUSIONS: These results illustrate how alcohol and cannabis exposure in utero may affect hippocampal function by altering inhibitory processes in a sex-specific manner.
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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 | 0.001 |
| 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".