Effects of NMDA receptor antagonists with different subtype selectivities on retinal spreading depression
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Spreading depression (SD) is a local, temporary disruption of cellular ionic homeostasis that propagates slowly across the cerebral cortex and other neural tissues such as the retina. Spreading depolarization associated with SD occurs in different types of stroke, and this phenomenon correlates also with the initiation of classical migraine aura. The aim of this study was to investigate how NMDA receptor antagonists with different subtype selectivity alter SD. EXPERIMENTAL APPROACH: Immunoblotting was applied to the chick retina for NMDA receptor subunit protein analysis, and an efficient in vitro chick retinal model used with SD imaging for NMDA receptor pharmacology. KEY RESULTS: The prominent NMDA receptor subtypes GluN1, GluN2A and GluN2B were found highly expressed in the chick retina. Nanomolar concentrations of NVP-AAM077 (GluN2A-preferring receptor antagonist) markedly suppressed high K(+) -induced SD; that is, ∼30 times more effectively than MK801. At sub-micromolar concentrations, Ro 25-6981 (GluN2B-preferring receptor antagonist) produced a moderate SD inhibition, whereas CP-101,606 (also GluN2B-preferring receptor antagonist) and UBP141 (GluN2C/2D-preferring receptor antagonist) had no effect. CONCLUSIONS AND IMPLICATIONS: The expression of major NMDA receptor subtypes, GluN1, GluN2A and GluN2B in the chick retina makes them pertinent targets for pharmacological inhibition of SD. The high efficacy of NVP-AAM077 on SD inhibition suggests a critical role of GluN2A-containing receptors in SD genesis. Such high anti-SD potency suggests that NVP-AAM077, and other GluN2A-selective drug-like candidates, could be potential anti-migraine agents.
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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) | 0.001 | 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".