Differential expression of GluN2 NMDA receptor subunits in the dorsal horn of male and female rats
Bibliographic record
Abstract
N-methyl-D-aspartate receptors (NMDARs) are excitatory ionotropic glutamate receptors expressed throughout the CNS, including in the spinal dorsal horn. The GluN2 subtypes of NMDAR subunit, which include GluN2A, GluN2B, and GluN2D in the dorsal horn, confer NMDARs with structural and functional variability, enabling heterogeneity in synaptic transmission and plasticity. Despite essential roles for NMDARs in physiological and pathological pain processing, the distribution and function of these specific GluN2 isoforms across dorsal horn laminae remain poorly understood. Surprisingly, there is a complete lack of knowledge of GluN2 expression in female rodents. We, therefore, investigated the relative expression of specific GluN2 variants in the dorsal horn of lumbar (L4/L5) spinal cord from both male and female rats. In order to detect synaptic GluN2 isoforms, we used pepsin antigen-retrieval to unmask these highly cross-linked protein complexes. We found that GluN2B and GluN2D are preferentially localized to the pain-processing superficial regions of the dorsal horn in males, while only GluN2B is predominantly localized to the superficial dorsal horn of female rats. The GluN2A subunit is diffusely localized to neuropil throughout the dorsal horn of both males and females, while GluN2B and GluN2D immunolabelling are found both in the neuropil and on the soma of dorsal horn neurons. Finally, we identified an unexpected enhanced expression of GluN2B in the medial division of the superficial dorsal horn, but in males only. These sex-specific localization patterns of GluN2-NMDAR subunits across dorsal horn laminae have significant implications for the understanding of divergent 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".