Countering by methylxanthines of opioid‐evoked depression of spontaneous network oscillations in locus coeruleus of newborn rat brain slices
Bibliographic record
Abstract
Treatment of (preterm) infants with opioids for analgesia can inhibit medullary respiratory networks controling breathing. Countering such opioid‐evoked and spontaneous apneas of prematurity with theophylline or caffeine can elicit seizures. Here, we studied in 400 μm thick horizontal brain slices from 0–4 days‐old rats whether these methylxanthines also counter opioid depression and evoke hyperexcitability in neural networks of locus coeruleus. Suction electrode recording revealed synchronized regular neuronal population bursting in locus coeruleus at 0.5–5 Hz which was disrupted neither by 10 mM theophylline, 10 mM caffeine nor GABAA receptor blockade (bicuculline, 25 μM). At 25 nM, the μ‐opioid receptor agonist DAMGO transformed fast oscillations into slower group bursts whereas 100–250 nM DAMGO abolished rhythm. At 1 mM, theophylline in 250 nM DAMGO reactivated slow rhythm whereas 2.5–10 mM restored faster oscillations. Block of oscillations by 250 nM DAMGO led to concomitant 10–25 mV hyperpolarization and a fall of Fluo‐4‐AM imaged cytosolic Ca2+ baseline in locus coeruleus neurons which were (partially) reversed by 10 mM theophylline. Findings indicate that methylxanthines do not cause seizure in locus coeruleus but counter opioid‐evoked depression of these networks with involvement of postsynaptic mechanisms.
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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 | 0.000 |
| 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".