CB₁ receptor antagonism in the bed nucleus of the stria terminalis interferes with affective opioid withdrawal in rats.
Bibliographic record
Abstract
The bed nucleus of the stria terminalis (BNST) is a region of the extended amygdala that is implicated in addiction, anxiety, and stress related behaviors. This region has been identified in mediating the aversive state of naloxone-precipitated morphine withdrawal (MWD) and cannabinoid Type I (CB1) receptors have been found to modulate neurotransmission within this region. Previous findings suggest that the CB1 antagonist/inverse agonist, AM251, administered systemically or by infusion into the central nucleus of the amygdala (CeA) prevented the aversive affective properties of MWD as measured by conditioned place aversion learning. As well, when administered systemically or by infusion into the basolateral nucleus of the amygdala (BLA) or the interoceptive insular cortex, the monoaclyglycerol lipase (MAGL) inhibitor, MJN110 (which elevates 2-arachidonlyglycerol), also prevented a naloxone-precipitated MWD induced place aversion. Given the connectivity of these regions and the BNST, the present study sought to determine whether cannabinoid modulation of the BNST would also prevent the affective properties of naloxone precipitated MWD-induced place aversion learning. Prior to conditioning trials, rats received intra-BNST infusions of AM251, in Experiment 1, or MJN110 in Experiment 2. AM251, but not MJN110, prevented the establishment of the MWD-induced place aversion. The current findings emphasize an important role for the BNST in opioid withdrawal and suggest that the ameliorative effects of systemically administered CB1 antagonists are mediated, in part, by their actions within this region. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".