Role of delta‐opioid and adenosine receptors in preventing NMDA receptor dependent excitotoxicity in anoxic turlte cortex
Bibliographic record
Abstract
Delta‐opioid (DOR) and adenosine A1 (A1R) receptor activation are neuroprotective against short‐term anoxic insults in mammalian brain. Protection may be conferred by inhibition of N‐methyl‐D‐aspartate receptors (NMDARs); activation of which leads to excitotoxic cell death (ECD). In anoxic turtle cortex NMDAR activity decreases 50% and ECD is avoided. DORs and A1Rs are expressed in turtle brain but their roles in anoxic turtle brain NMDAR regulation have not been investigated. DOR blockade with naltrindole potentiated normoxic NMDAR currents by 78%, and increased [Ca 2+ ] c 13%. Anoxic neurons treated with naltrindole were strongly depolarized, NMDAR currents were potentiated 70%, and [Ca 2+ ] c increased 5‐fold above anoxic controls. The naltrindole‐mediated depolarization and increased [Ca 2+ ] c were prevented by NMDAR antagonism or by perfusion of the G i protein agonist mastoparan, which also reversed the naltrindole‐mediated potentiation. Normoxic agonism of A 1 Rs with CPA decreased NMDAR currents by 58% but antagonism during anoxia with DPCPX did not prevent the anoxia‐mediated decrease. The G i protein inhibitor pertusis toxin (PTX) prevented both the CPA and anoxia‐mediated decreases in NMDAR currents. Our results suggest that the long‐term anoxic decrease in NMDAR activity is activated by a PTX‐sensitive mechanism that maybe be related by DORs but is independent of A 1 Rs.
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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".