Microglia attenuate the opioid‐induced depression of preBötzinger Complex (preBötC) inspiratory rhythm in vitro via a TLR4‐independent pathway
Bibliographic record
Abstract
Opioids activate neurons via opioid receptors (Rs) but also activate microglia via toll like receptors (TLR). Activation of microglial TLR4 impairs the ability of opioids to suppress pain and is hypothesized as pivotal in opioid‐mediated reward and tolerance. Here we test the contribution of microglia and TLR4 to the opioid‐induced respiratory depression. Using rhythmic medullary slices from neonatal rats we compared the duration of apneas evoked by locally injecting DAMGO (μ‐opiate R agonist; 50μM) into the preBötC before and after 40 min incubation in minocycline (inhibits microglial activation, 500 nM). Minocycline increased the duration of DAMGO‐evoked apnea 9.3±4.7‐fold from 33±7 to 295±76 s (n=3). In time‐matched controls, the second DAMGO‐evoked apnea was only 0.5±0.4‐fold greater than the first. The TLR4 antagonist LPS‐RS (90sec; 2000 ng/mL) had no effect on the frequency (f) depression evoked by DAMGO in the preBötC. Similarly, bath application of (+) naloxone (10μM; TLR4selective antagonist) had no effect on the f depression evoked by DAMGO (500nM) or fentanyl (1μM) in the bath. In contrast, (−) naloxone (500nM; TLR4 and μ‐opioid antagonist) reversed the depression. These data suggest that microglia attenuate the opioid‐induced respiratory depression via a mechanism that does not involve TLR4 activation. Supported by CIHR, WCHRI, AIHS, NIDA & NIAAA.
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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) | 0.000 | 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".