<i>N</i>‐Methyl‐<scp>d</scp>‐aspartate receptor subunit‐ and neuronal‐type dependence of excitotoxic signaling through post‐synaptic density 95
Bibliographic record
Abstract
NMDA receptors (NMDARs) mediate excitatory synaptic transmission during repetitive or prolonged glutamate release, playing a critical role in synaptic plasticity or cell death, respectively. Evidence indicates that a major pathway of NMDAR signaling to cell death in cortical and hippocampal neurons requires the scaffolding protein post-synaptic density 95 (PSD-95) and activation of neuronal nitric oxide synthase. However, it is not known if this PSD-95-dependent pathway contributes to excitotoxicity in other brain regions. It is also unclear whether the neuroprotective effects of Tat-NR2B9c, a membrane-permeant peptide that disrupts PSD-95/NMDAR binding, correlate with uncoupling NR2B- and/or NR2A-type NMDARs from PSD-95. In this study, we used cultured hippocampal and striatal neurons to test the potency of Tat-NR2B9c on uncoupling NR2 subunits from PSD-95 and protecting against NMDA-induced excitotoxicity. We found that the concentration of Tat-NR2B9c required to dissociate 50% of PSD-95 was fourfold lower for NR2B than NR2A in cultured hippocampal and striatal neurons, and that this concentration correlated tightly with protection against NMDA-induced toxicity in hippocampal neurons without altering NMDAR current. In contrast, NMDAR signaling to cell death in cultured striatal neurons occurred independently of the NR2B/PSD-95 interaction or neuronal nitric oxide synthase activation. These results will facilitate development of neuronal type-specific protective therapies.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".