Δ-Tetrahydrocannabinol Increases Dopamine D1-D2 Receptor Heteromer and Elicits Phenotypic Reprogramming in Adult Primate Striatal Neurons
Bibliographic record
Abstract
-tetrahydrocannabinol (THC)-induced neuroadaptive dysfunctional dopamine signaling, similar to those observed upon dopamine D1-D2 heteromer activation. The molecular mechanisms remain largely unknown. We show evolutionary and regional differences in D1-D2 heteromer abundance in mammalian striatum. Importantly, chronic THC increased the number of D1-D2 heteromer-expressing neurons, and the number of heteromers within individual neurons in adult monkey striatum. The majority of these neurons displayed a phenotype co-expressing the characteristic markers of both striatonigral and striatopallidal neurons. Furthermore, THC increased D1-D2-linked calcium signaling markers (pCaMKIIα, pThr75-DARPP-32, BDNF/pTrkB) and inhibited cyclic AMP signaling (pThr34-DARPP-32, pERK1/2, pS845-GluA1, pGSK3). Cannabidiol attenuated most but not all of these THC-induced neuroadaptations. Targeted pathway analyses linked these changes to neurological and psychological disorders. These data underline the importance of the D1-D2 receptor heteromer in cannabis use-related disorders, with THC-induced changes likely responsible for the reported adverse effects observed in heavy long-term users.
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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".