D-Serine differently modulates NMDA receptor function in rat CA1 hippocampal pyramidal cells and interneurons
Bibliographic record
Abstract
The organization of the neuronal hippocampal network depends on the tightly regulated interaction between pyramidal cells (PCs) and interneurons (Ints). NMDA receptor (NMDAR) activation requires the binding of glutamate and co-activation of the 'glycine site'. It has been reported that D-serine is a more potent endogenous agonist than glycine for that site. While many studies have focused on NMDAR function in PCs, little is known regarding the modulation of NMDARs in Ints. We studied the modulatory effect of D-serine on NMDAR EPSCs in PCs and in stratum radiatum Ints using whole-cell patch-clamp recording in rat acute hippocampal slices. We found that D-serine enhances NMDAR function and differently modulates NMDAR currents in both cell types. The augmentation of NMDAR currents by D-serine was significantly larger in PCs compared with Ints. Moreover, we found differences in the kinetics of NMDAR currents in PCs and Ints. Our findings indicate that regulation of NMDAR through the 'glycine site' depends on the cell types. We speculate that the observed differences arise from assemblies of diverse NMDAR subunits. Overall, our data suggest that D-serine may be involved in regulation of the excitation-inhibition balance in the CA1 hippocampal region.
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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) | 0.000 | 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".