Conserved contributions of NMDA receptor subtypes to synaptic responses in lamina II spinal neurons across early postnatal development
Bibliographic record
Abstract
NMDA receptors are heteromeric complexes that contribute to excitatory synaptic transmission and plasticity. The presence of specific variants of GluN2 subunits in these complexes enables diversity in NMDA receptor function and regulation. At brain synapses, there is a switch from slow GluN2B-mediated NMDA receptors to faster GluN2A-dominated NMDA receptors as well as an increase in the ratio of AMPA to NMDA receptors during early postnatal development. This glutamate receptor switch is observed across brain regions and is critical for synaptic maturation, circuit development, and associative learning. However, whether a similar receptor subunit switch occurs within pain processing neurons in the developing spinal cord remains untested. To investigate this, we performed whole-cell patch clamp recordings of excitatory synaptic responses from lamina II dorsal horn neurons of one to three week-old rats. We found that GluN2B and GluN2A both prominently contribute to NMDA receptor responses at neonatal lamina II synapses, with a small contribution from GluN2D as well. Surprisingly, we found that this molecular identity of NMDA receptor responses as well as the relative contribution of AMPA receptors versus NMDA receptors did not change at lamina II synapses across early postnatal development (P7 to P21). The lack of a developmental switch and persistence of slow-decaying GluN2B- and GluN2D-mediated synaptic responses throughout neuronal maturation in the dorsal horn has implications for understanding both the regulation of synaptic glutamatergic receptors as well as spinal mechanisms of pain processing.
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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".