Distinct Roles of Synaptic and Extrasynaptic NMDA Receptors in Excitotoxicity
Bibliographic record
Abstract
Excitatory synaptic activity governs excitotoxicity and modulates the distribution of NMDA receptors (NMDARs) among synaptic and extrasynaptic sites of central neurons. We investigated whether NMDAR localization was functionally linked to excitotoxicity by perturbing F-actin, a cytoskeletal protein that participates in targeting synaptic NMDARs in dendritic spines. Depolymerizing F-actin did not affect NMDA-evoked whole-cell currents. However, the number of dendritic NMDAR clusters and the NMDAR-mediated component of miniature spontaneous EPSCs were reduced, whereas the number of AMPA receptor clusters and AMPA receptor-mediated component of EPSCs was unchanged. This selective perturbation of synaptically activated NMDARs had no effect on neuronal death or the accumulation of (45)Ca(2+) evoked by applying exogenous NMDA or L-glutamate, which reach both synaptic and extrasynaptic receptors. However, it increased survival and decreased (45)Ca(2+) accumulation in neurons exposed to oxygen glucose deprivation, which causes excitotoxicity by glutamate release at synapses. Thus, synaptically and extrasynaptically activated NMDARs are equally capable of excitotoxicity. However, their relative contributions vary with the location of extracellular excitotoxin accumulation, a factor governed by the mechanism of extracellular neurotransmitter accumulation, not the synaptic activation of NMDARs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".