Cannabinoid-Mediated Modulation of Neuropathic Pain and Microglial Accumulation in a Model of Murine Type I Diabetic Peripheral Neuropathic Pain
Bibliographic record
Abstract
BACKGROUND: Despite the frequency of diabetes mellitus and its relationship to diabetic peripheral neuropathy (DPN) and neuropathic pain (NeP), our understanding of underlying mechanisms leading to chronic pain in diabetes remains poor. Recent evidence has demonstated a prominent role of microglial cells in neuropathic pain states. One potential therapeutic option gaining clinical acceptance is the cannabinoids, for which cannabinoid receptors (CB) are expressed on neurons and microglia. We studied the accumulation and activation of spinal and thalamic microglia in streptozotocin (STZ)-diabetic CD1 mice and the impact of cannabinoid receptor agonism/antagonism during the development of a chronic NeP state. We provided either intranasal or intraperitoneal cannabinoid agonists/antagonists at multiple doses both at the initiation of diabetes as well as after establishment of diabetes and its related NeP state. RESULTS: Tactile allodynia and thermal hypersensitivity were observed over 8 months in diabetic mice without intervention. Microglial density increases were seen in the dorsal spinal cord and in thalamic nuclei and were accompanied by elevation of phosphorylated p38 MAPK, a marker of microglial activation. When initiated coincidentally with diabetes, moderate-high doses of intranasal cannabidiol (cannaboid receptor 2 agonist) and intraperitoneal cannabidiol attenuated the development of an NeP state, even after their discontinuation and without modification of the diabetic state. Cannabidiol was also associated with restriction in elevation of microglial density in the dorsal spinal cord and elevation in phosphorylated p38 MAPK. When initiated in an established DPN NeP state, both CB1 and CB2 agonists demonstrated an antinociceptive effect until their discontinuation. There were no pronociceptive effects demonstated for either CB1 or CB2 antagonists. CONCLUSIONS: The prevention of microglial accumulation and activation in the dorsal spinal cord was associated with limited development of a neuropathic pain state. Cannabinoids demonstrated antinociceptive effects in this mouse model of DPN. These results suggest that such interventions may also benefit humans with DPN, and their early introduction may also modify the development of the NeP state.
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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) | 0.000 | 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 | 0.001 |
| 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".