Neuroprotection After Status Epilepticus by Targeting Protein Interactions With Postsynaptic Density Protein 95
Bibliographic record
Abstract
N-methyl-D-aspartate receptors (NMDARs) mediate essential neuronal excitation, but overactivation of NMDARs results in excitotoxic cell death in a variety of pathologic conditions, including status epilepticus (SE). Although NMDAR antagonists attenuate SE-induced brain injury, undesirable side effects have limited their clinical efficacy. Tat-NR2B9c was designed to disrupt protein interactions involving postsynaptic density protein 95 in the NMDAR signaling complex while not interfering with function of the NMDAR ion channel. We examined the ability of Tat-NR2B9c to provide neuroprotection in the hippocampus of rats after 60 minutes of SE induced by the repeated injection of low doses of pilocarpine (10 mg/kg). Tat-NR2B9c was administered 3hours after the termination of SE, and neuronal densities were assessed 14 days later by stereologic analysis of NeuN-positive cells. After SE, pyramidal cell densities were reduced by 70% in CA1, 34% in CA3, 58% in CA4, and 88% in the piriform cortex. In Tat-NR2B9c-treated rats, neuronal densities in CA1, a subregion of CA3, and CA4 were decreased by only 38%, 4%, and 26%, respectively. Tat-NR2B9c did not reduce cell loss in the posterior piriform cortex. The results indicate that targeted disruption of the NMDAR signaling complex represents a potential therapeutic approach for limiting neuronal cell loss after SE.
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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 | 0.000 |
| 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".