Heterogeneity of synaptic NMDA receptor responses within individual lamina I pain processing neurons across sex in rats and humans
Bibliographic record
Abstract
Abstract Excitatory glutamatergic NMDA receptors (NMDARs) are key regulators of spinal pain processing, and yet the biophysical properties of NMDARs in dorsal horn nociceptive neurons remain poorly understood. Despite the clinical implications, it is unknown whether the molecular and functional properties of NMDAR synaptic responses are conserved between males and females as well as from rodents to humans. To address these translational gaps, we systematically compared individual and averaged excitatory synaptic responses from lamina I pain-processing neurons of adult Sprague Dawley rats and human organ donors, including both sexes. By combining patch-clamp recordings of outward miniature excitatory postsynaptic currents with non-biased data analyses, we uncovered a wide range of decay constants of excitatory synaptic events within individual lamina I neurons. Decay constants of quantal synaptic responses were distributed in a continuum from 1-20 ms to greater than 1000 ms, suggesting that individual lamina I neurons contain AMPA receptor (AMPAR)-only as well as GluN2A-, GluN2B-, and GluN2D-NMDAR-dominated synaptic events. This intraneuronal heterogeneity in AMPAR– and NMDAR-mediated decay kinetics was observed across sex and species. However, we discovered a decreased relative contribution of GluN2B-dominated NMDAR responses as well as larger amplitude GluN2D-like events at human lamina I synapses compared to rodent synapses, suggesting species differences relevant to NMDAR subunit-targeting therapeutic approaches. The conserved heterogeneity in decay rates of excitatory synaptic events within individual lamina I pain-processing neurons may enable synapse-specific forms of plasticity and sensory integration within dorsal horn nociceptive networks.
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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 | 0.001 |
| 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".