Chronic pain selectively reduces the motivation to work for remifentanil but not food reward
Bibliographic record
Abstract
ABSTRACT: Currently, preclinical research has reported conflicting evidence as to whether chronic pain imparts resilience or vulnerability to opioid drug seeking. Here, we investigated the impact of chronic pain on the intravenous self-administration (IVSA) profile of the short-acting opioid analgesic remifentanil in a mouse model. Using a chronic constriction injury model of chronic neuropathic pain, 7 days after injury, male and female C57Bl/6J mice began remifentanil IVSA. During the acquisition phase, there were no differences in the total number of reinforcers earned but an increase in the number of active nose pokes in pain mice. An increase in the rate of acquisition within sessions was observed in male but not female mice. When work effort increased (fixed ratio 3 and progressive ratio), pain mice unexpectedly showed a reduction in the number of reinforcers earned and their breakpoint. This change in motivational state was specific to the willingness to work for remifentanil, as these changes were not observed with higher effort for a food reward. We hypothesized that chronic pain altered the dopaminergic state of the striatum, which would impact the motivation to work for a reward. We found that pain mice had significantly decreased phasic dopamine release assessed via fast-scan cyclic voltammetry and reduced potassium-evoked extracellular dopamine measured by microdialysis. Future studies will investigate the causal relationship between this hypo-dopaminergic state and decreased behavioral motivation associated with a chronic pain state.
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".