A Systematic Review of the Effects of Perinatal Alcohol Exposure and Perinatal Marijuana Exposure on Adult Neurogenesis in the Dentate Gyrus
Bibliographic record
Abstract
BACKGROUND: Marijuana and alcohol are both substances that, when used during pregnancy, may have profound effects on the developing fetus. There is evidence to suggest that both drugs have the capacity to affect working memory, one function of the hippocampal formation; however, there is a paucity of data on how perinatal exposure to alcohol or cannabis impacts the process of adult neurogenesis. METHODS: This systematic review examines immunohistochemical data from adult rat and mouse models that assess perinatal alcohol or perinatal marijuana exposure. A comprehensive list of search terms was designed and used to search 3 separate databases. All results were imported to Mendeley and screened by 2 authors. Consensus was reached on a set of final papers that met the inclusion criteria, and their results were summarized. RESULTS: Twelve papers were identified as relevant, 10 of which pertained to the effects of perinatal alcohol on the adult hippocampus, and 2 pertained to the effects of perinatal marijuana on the adult hippocampus. Cellular proliferation in the dentate gyrus was not affected in adult rats and mice exposed to alcohol perinatally. In general, perinatal alcohol exposure did not have a significant and reliable effect on the maturation and survival of adult born granule neurons in the dentate gyrus. In contrast, interneuron numbers appear to be reduced in the dentate gyrus of adult rats and mice exposed perinatally to alcohol. Perinatal marijuana exposure was also found to reduce inhibitory interneuron numbers in the dentate gyrus. CONCLUSIONS: Perinatal alcohol exposure and perinatal marijuana exposure both act on inhibitory interneurons in the hippocampal formation of adult rats. These findings suggest simultaneous perinatal alcohol and marijuana exposure (SAM) may have a dramatic impact on inhibitory processes in the dentate gyrus.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".