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D-Serine differently modulates NMDA receptor function in rat CA1 hippocampal pyramidal cells and interneurons

2003· article· en· W1983251249 on OpenAlexfundno aff
Marzia Martina, Nicholas V. Krasteniakov, Richard Bergeron

Bibliographic record

VenueThe Journal of Physiology · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsHippocampal formationNMDA receptorNeurosciencePyramidal cellChemistryHippocampusExcitatory postsynaptic potentialSerineReceptorBiologyCell biologyPhosphorylationBiochemistryInhibitory postsynaptic potential

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.209
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2003
Admission routes1
Has abstractyes

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