Cingulate NMDA NR2B receptors contribute to morphine-induced analgesic tolerance
Bibliographic record
Abstract
Morphine is widely used to treat chronic pain, however its utility is hindered by the development of tolerance to its analgesic effects. While N-methyl-D-aspartate (NMDA) receptors are known to play roles in morphine tolerance and dependence, less is known about the roles of individual NMDA receptor subtypes. In this study, Ro 256981, an antagonist of the NMDA receptor subunit NR2B, was used to reduce the expression of analgesic tolerance to morphine. The mechanisms altered with chronic drug use share similarities with those underlying the establishment of long-tem potentiation (LTP) and behavioral memory. Since NMDA NR2B receptors in the anterior cingulate cortex (ACC) play roles in the establishment of LTP and fear memory, we explored their role in changes that occur in this region after chronic morphine. Both systemic and intra-ACC inhibition of NR2B in morphine-tolerant animals inhibited the expression of analgesic tolerance. Electrophysiological recordings revealed a significant increase in the NR2B component of NMDA receptor mediated excitatory postsynaptic currents (EPSCs), at both synaptic and extra-synaptic sites. However, there was no change in alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor mediated EPSCs. This study suggests that selective inhibition of NMDA NR2B receptors may prove useful in combating the development of analgesic tolerance to morphine and proposes a novel role for the ACC in opioid tolerance and morphine induced changes in synaptic plasticity.
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) | 0.000 | 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 | 0.001 |
| 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".