Increased Prostaglandin E2Release and Activated Akt/β-Catenin Signaling Pathway Occur after Opioid Withdrawal in Rat Spinal Cord
Bibliographic record
Abstract
BACKGROUND: Prostaglandin E(2) is an important spinal modulator of nociception. However, the effects of chronic opioid administration and withdrawal on prostaglandin E(2) release and associated signaling pathways in the spinal cord are generally unknown. METHODS: This study sought to examine these effects using a spinal microdialysis technique in a model of chronic morphine administration and withdrawal in the rat. RESULTS: The authors found that spinal prostaglandin E(2) release was unaffected by chronic morphine treatment but was significantly increased during withdrawal. Recurrent withdrawal did not further enhance this release. The authors also found up-regulation of cyclooxygenase-2 expression and phosphorylation of protein kinase Akt at Ser-473 in response to opioid withdrawal. In addition, they demonstrated that beta-catenin, a transcription factor downstream of Akt, was induced during morphine withdrawal, particularly during recurrent withdrawal. CONCLUSIONS: These results suggest that opioid withdrawal activates signaling pathways associated with neuronal survival and transcriptional control, two processes implicated in neuronal development and 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".