Cannabinoid CB1 Receptor Expression and Localization in the Dorsal Horn of Male and Female Human and Rat Spinal Cord Tissue
Bibliographic record
Abstract
Preclinical and clinical evidence suggests that cannabis, a potent cannabinoid, has potential analgesic properties.However, there is a gap in the literature with respect to cannabinoid receptor expression and localization in the spinal cord across both sex and species, with almost nothing known in humans.We aimed to investigate the differential expression of the cannabinoid type 1 receptor (CB1R) across dorsal horn laminae and cell populations in male and female adult rats and humans.Human spinal cord samples were collected from organ donors 1-3 hours post-aortic cross-clamping.To investigate and quantify CB1R expression in the spinal dorsal horn, we used an immunohistochemistry approach along with confocal imaging.We successfully refined and applied staining procedures from rat to human fixed tissue.Qualitatively, we observed increased neuropil immunostaining in the superficial dorsal horn (SDH) of rats and humans, and somatic staining in the deeper laminae.Quantitative results indicated a significant increase in CB1R immunoreactivity in the SDH when compared to the deeper dorsal horn laminae of both rat and humans.This significant difference in receptor expression across dorsal horn laminae was conserved across sex in both species.The preferential expression of CB1Rs in the SDH across both sex and species has significant implications for both the understanding and treatment of pain.internal examiner for your time and helpful critiques of my thesis.Thank you to microscope specialist Dr. Chloë van Oostende-Triplet and the rest of the team at the Cell Biology and Image Acquisition (CBIA) Core at the University of Ottawa for their expertise.Thank you to the friends I met amidst a global pandemic, who supported me throughout this journey, and to my family for their constant love and support.And finally, this project couldn't be possible without the selfless donation from our organ donors.
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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) | 0.007 | 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".