GluN2B and GluN2D NMDARs dominate synaptic responses in the adult spinal cord
Bibliographic record
Abstract
The composition of the postsynaptic ionotropic receptors that receive presynaptically released transmitter is critical not only for transducing and integrating electrical signals but also for coordinating downstream biochemical signaling pathways. At glutamatergic synapses in the adult CNS an overwhelming body of evidence indicates that the NMDA receptor (NMDAR) component of synaptic responses is dominated by NMDARs containing the GluN2A subunit, while NMDARs containing GluN2B, GluN2C, or GluN2D play minor roles in synaptic transmission. Here, we discovered NMDAR-mediated synaptic responses with characteristics not described elsewhere in the adult CNS. We found that GluN2A-containing receptors contribute little to synaptic NMDAR responses while GluN2B dominates at synapses of lamina I neurons in the adult spinal cord. In addition, we provide evidence for a GluN2D-mediated synaptic NMDAR component in adult lamina I neurons. Strikingly, the charge transfer mediated by GluN2D far exceeds that of GluN2A and is comparable to that of GluN2B. Lamina I forms a distinct output pathway from the spinal pain processing network to the pain networks in the brain. The GluN2D-mediated synaptic responses we have discovered in lamina I neurons provide the molecular underpinning for slow, prolonged and feedforward amplification that is a fundamental characteristic of pain.
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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".