Cannabinoid CB1 Receptor Expression and Localization in the Dorsal Horn of Male and Female Rat and Human Spinal Cord
Bibliographic record
Abstract
Background Preclinical and clinical evidence suggests that cannabis has potential analgesic properties. However, cannabinoid receptor expression and localization within spinal cord pain processing circuits remains to be characterized across sex and species.Aims We aimed to investigate the differential expression of the cannabinoid type 1 (CB1) receptor across dorsal horn laminae and cell populations in male and female adult rats and humans.Methods To investigate and quantify CB1 receptor expression in the spinal dorsal horn across species, we refined immunohistochemical procedures for successful rat and human fixed tissue staining and confocal imaging. Immunohistochemical results were complemented with analysis of CB1 gene (CNR1) expression within rodent and human dorsal horn using single-cell/nuclei RNA sequencing datasets.Results We found that CB1 was preferentially localized to the neuropil within the superficial dorsal horn of both rats and humans, with CB1 somatic staining across dorsal horn laminae. CB1 receptor immunoreactivity was significantly higher in the superficial dorsal horn compared to the deeper dorsal horn laminae for both rats and humans, which was conserved across sex. Interestingly, we found that CB1 immunoreactivity was not primarily localized to peptidergic afferents in rats and humans, and that CNR1 (CB1) but not CNR2 (CB2) was robustly expressed in dorsal horn neuron subpopulations of both rodents and humans.Conclusions The conserved preferential expression of CB1 receptors in the superficial dorsal horn in male and female rodents and humans has significant implications for understanding the roles of this cannabinoid receptor in spinal mechanisms of nociception and analgesia.
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.
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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".