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Record W4409512557 · doi:10.1080/19336950.2025.2490308

The GluN3-containing NMDA receptors

2025· review· en· W4409512557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChannels · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of NingboChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsNMDA receptorReceptorChemistryNeuroscienceCell biologyBiochemistryBiology

Abstract

fetched live from OpenAlex

N-methyl-D-aspartate receptors (NMDARs) are heterotetrameric ion channels that play crucial roles in brain function. Among all the NMDAR subtypes, GluN1-N3 receptors exhibit unique agonist binding and gating properties. Unlike “conventional” GluN1-N2 receptors, which require both glycine and glutamate for activation, GluN1-N3 receptors are activated solely by glycine. Furthermore, GluN1-N3 receptors display faster desensitization, reduced Ca2+ permeability, and lower sensitivity to Mg2+ blockage compared to GluN1-N2 receptors. Due to these characteristics, GluN1-N3 receptors are thought to play critical roles in eliminating redundant synapses and pruning spines in early stages of brain development. Recent studies have advanced pharmacological tools for specifically targeting GluN1-N3 receptors and provided direct evidence of these glycine-activated excitatory receptors in native brain tissue. The structural basis of GluN1-N3 receptors has also been elucidated through cryo-EM and artificial intelligence. These findings highlight that GluN1-N3 receptors are not only involved in essential brain functions but also present potential targets for drug development.

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.170
GPT teacher head0.439
Teacher spread0.269 · 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