Multiple roles of GluN2D-containing NMDA receptors in short-term potentiation and long-term potentiation in mouse hippocampal slices
Bibliographic record
Abstract
The GluN2 subunits of N-methyl-d-aspartate receptors (NMDARs) are key drivers of synaptic plasticity in the brain, where the particular GluN2 composition endows the NMDAR complex with distinct pharmacological and physiological properties. Compared to GluN2A and GluN2B subunits, far less is known about the role of the GluN2D subunit in synaptic plasticity. In this study, we have used a GluN2C/2D selective competitive antagonist, UBP145, in combination with a GluN2D global knockout (GluN2D KO) mouse line to study the contribution of GluN2D-containing NMDARs to short-term potentiation (STP) and long-term potentiation (LTP) in the CA1 region of mouse hippocampal slices. We made several distinct observations: First, GluN2D KO mice have higher levels of LTP compared to wild-type (WT) mice, an effect that was occluded by blockade of GABA receptor-mediated inhibition or by using a strong LTP induction protocol. Second, UBP145 partially inhibited LTP in WT but not GluN2D KO mice. Third, UBP145 inhibited a component of STP, termed STP2, in WT but not GluN2D KO mice. Taken together, these findings suggest an involvement for GluN2D-containing NMDARs in both STP and LTP in mouse hippocampus.
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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) | 0.001 | 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 | 0.000 |
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".