Glial TLR4 signaling does not contribute to opioid-induced depression of respiration
Bibliographic record
Abstract
Opioids activate glia in the central nervous system in part by activating the toll-like receptor 4 (TLR4)/myeloid differentiation 2 (MD2) complex. TLR4/MD2-mediated activation of glia by opioids compromises their analgesic actions. Glial activation is also hypothesized as pivotal in opioid-mediated reward and tolerance and as a contributor to opioid-mediated respiratory depression. We tested the contribution of TLR4 to opioid-induced respiratory depression using rhythmically active medullary slices that contain the pre-Bötzinger Complex (preBötC, an important site of respiratory rhythm generation) and adult rats in vivo. Injection with DAMGO (μ-opioid receptor agonist; 50 μM) or bath application of DAMGO (500 nM) or fentanyl (1 μM) slowed frequency recorded from XII nerves to 40%, 40%, or 50% of control, respectively. This DAMGO-mediated frequency inhibition was unaffected by preapplication of lipopolysaccharides from Rhodobacter sphaeroides (a TLR4 antagonist, 2,000 ng/ml) or (+)naloxone (1-10 μM, a TLR4-antagonist). Bath application of (-)naloxone (500 nM; a TLR4 and μ-opioid antagonist), however, rapidly reversed the opioid-mediated frequency decrease. We also compared the opioid-induced respiratory depression in slices in vitro in the absence and presence of bath-applied minocycline (an inhibitor of microglial activation) and in slices prepared from mice injected (ip) 18 h earlier with minocycline or saline. Minocycline had no effect on respiratory depression in vitro. Finally, the respiratory depression evoked in anesthetized rats by tail vein infusion of fentanyl was unaffected by subsequent injection of (+)naloxone, but completely reversed by (-)naloxone. These data indicate that neither activation of microglia in preBötC nor TLR4/MD2-activation contribute to opioid-induced respiratory depression.
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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) | 0.001 | 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".