Differential regulation of STP, LTP and LTD by structurally diverse NMDA receptor subunit-specific positive allosteric modulators
Bibliographic record
Abstract
Different types of memory are thought to rely on different types of synaptic plasticity, many of which depend on the activation of the N-Methyl-D Aspartate (NMDA) subtype of glutamate receptors. Accordingly, there is considerable interest in the possibility of using positive allosteric modulators (PAMs) of NMDA receptors (NMDARs) as cognitive enhancers. Here we firstly review the evidence that NMDA receptor-dependent forms of synaptic plasticity: short-term potentiation (STP), long-term potentiation (LTP) and long-term depression (LTD) can be pharmacologically differentiated by using NMDAR ligands. These observations suggest that PAMs of NMDAR function, depending on their subtype selectivity, might differentially regulate STP, LTP and LTD. To test this hypothesis, we secondly performed experiments in rodent hippocampal slices with UBP714 (a GluN2A/2B preferring PAM), CIQ (a GluN2C/D selective PAM) and UBP709 (a pan-PAM that potentiates all GluN2 subunits). We report here, for the first time, that: (i) UBP714 potentiates sub-maximal LTP and reduces LTD; (ii) CIQ potentiates STP without affecting LTP; (iii) UBP709 enhances LTD and decreases LTP. We conclude that PAMs can differentially regulate distinct forms of NMDAR-dependent synaptic plasticity due to their subtype selectivity.
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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) | 0.000 | 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 | 0.000 |
| 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".