Prenatal cannabis exposure alters the firing properties of adolescent ventral tegmental area dopamine neurons
Bibliographic record
Abstract
Though cannabis use during pregnancy has increased, our understanding of its effects on offspring is incomplete. Gestational THC injection studies found altered goal-directed behaviour and mesolimbic dopamine reward circuitry in offspring of various ages. To investigate whether oral cannabis exposure during pregnancy alters ventral tegmental area (VTA) dopamine neuron activity, mice were exposed to 5 mg/kg THC cannabis extract in peanut butter or peanut butter alone from GD0-PD10. Whole cell patch clamp electrophysiology was performed in VTA slices from adolescent (P42-P46) offspring. Maternal weight gain, food intake, litter size, frequency of miscarriages, or maternal behaviour in a pup retrieval test were not different between groups, suggesting the effects of cannabis are not secondary to poor maternal care or health. VTA dopamine neurons of male mice exposed to cannabis from GD0 to PD10 had a more depolarised resting membrane potential, increased spontaneous firing, decreased latency to fire, and increased after-hyperpolarisation potential height, but not width. In contrast, dopamine neurons from female mice had shorter after-hyperpolarisation potentials, without changes in resting membrane potential, spontaneous firing frequency or other firing properties. Consistent with previous injection studies, prenatal exposure to oral cannabis differently alters the firing properties of male and female VTA dopamine neurons. Future research will examine synaptic changes and the behavioural implications of cannabis-induced differences in VTA dopamine neuronal activity.
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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".