Genetic deletion of microglial Panx1 attenuates morphine withdrawal, but not analgesic tolerance or hyperalgesia in mice
Bibliographic record
Abstract
Opioids are among the most powerful analgesics for managing pain, yet their repeated use can lead to the development of severe adverse effects. In a recent study, we identified the microglial pannexin-1 channel (Panx1) as a critical substrate for opioid withdrawal. Here, we investigated whether microglial Panx1 contributes to opioid-induced hyperalgesia (OIH) and opioid analgesic tolerance using mice with a tamoxifen-inducible deletion of microglial Panx1. We determined that escalating doses of morphine resulted in thermal pain hypersensitivity in both Panx1-expressing and microglial Panx1-deficient mice. In microglial Panx1-deficient mice, we also found that acute morphine antinociception remained intact, and repeated morphine treatment at a constant dose resulted in a progressive decline in morphine antinociception and a reduction in morphine potency. This reduction in morphine antinociceptive potency was indistinguishable from that observed in Panx1-expressing mice. Notably, morphine tolerant animals displayed increased spinal microglial reactivity, but no change of microglial Panx1 expression. Collectively, our findings indicate microglial Panx1 differentially contributes to opioid withdrawal, but not the development of opioid-induced hyperalgesia or tolerance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".