Timing matters: modeling the effects of gestational cannabis exposure on social behavior and microglia in the developing amygdala
Bibliographic record
Abstract
Cannabis is the most frequently used illicit drug during pregnancy, with use steadily increasing in the United States as legalization and decriminalization expand to more states. Many pregnant individuals use cannabis to reduce adverse symptoms of pregnancy, considering it to be less harmful than other pharmaceuticals or alcohol. The primary psychoactive component of cannabis, delta-9-tetrahydrocannabinol (THC), is a partial agonist of the candidate receptors of the endocannabinoid (eCB) system cannabinoid receptor 1 (CB1R) and 2 (CB2R). However, whether it perturbs neural development of the fetus is poorly understood. Previously we have shown that androgen mediated eCB tone in the developing amygdala promotes microglial phagocytosis of newborn astrocytes which has enduring consequences on the neural circuits regulating sex differences in social behavior. Microglia are the resident immune cells of the brain and express both CB1R and CB2R, making them likely targets of modulation by THC. It is also plausible that exposure to THC at differing gestational timepoints can result in distinct outcomes, as is the case with alcohol exposure. To model human cannabis use during either late or early pregnancy, we exposed rodents to THC either directly during the early postnatal period via intraperitoneal (IP) injection or in utero during the prenatal period via dam subcutaneous (SC) injection respectively. Here we show that postnatal THC exposure results in sex specific changes in microglial phagocytosis during development as well as social behavior during the juvenile period. Interestingly prenatal exposure to THC resulted in inverse changes to phagocytosis and social behavior. These findings highlight the differential effects of THC exposure across gestation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".