Non-Competitive AMPA Receptor Antagonist Perampanel Inhibits Ischemia-Induced Neurodegeneration and Behavioral Deficits in Focal Cortical Pial Vessel Disruption Stroke Model
Bibliographic record
Abstract
Glutamate receptors represent a potential target for neuroprotection in neurodegener-ative neurological conditions. Perampanel, a non-competitive α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptor (AMPAR) antagonist, is clinically approved for the management of epilepsy. Perampanel’s neuroprotective ef-fects have been reported in global and focal cerebral ischemia models, but the cellular mechanisms remain incompletely understood. Here we investigated the potential neuroprotective effects of perampanel in rats subjected to pial vessel disruption (PVD), which mimics a focal cortical non-reperfusion ischemic stroke model. Perampanel was given intraperitoneally (3mg/kg body weight) 1 hr after PVD surgery and once daily for two days post-surgery. On the fourth day post PVD, animal behavioral assays and imaging, biochemical and electrophysiological analyses were performed. Compared to vehicle control, perampanel in PVD-treated rats significantly inhibited hippocampal neurodegeneration and long-term potentiation deficits. Perampanel also attenuated PVD-induced motor deficits, depressive/anxiety-like behaviors, and hippocam-pal-dependent cognitive impairment. In addition, perampanel prevented the PVD-induced downregulation of surface-expressed GluA1 and GluA2 AMPARs and increased phosphorylation of GluA1 at S831 and S845. Molecular docking analysis revealed perampanel binding to transmembrane regions M1, M3 and M4 of GluA1 and GluA2 subunits. Together, our results show that perampanel attenuated PVD-induced neurodegeneration and behavioral deficits by blocking AMPARs and decreasing GluA1 and GluA2 internalization. In addition, this study shows the neuroprotective potential of perampanel through the inhibition of neuroinflammation mediated by ac-tivated microglia and astrocytes following cerebral ischemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".