Cannabinoids in Chronic Pain: Therapeutic Potential Through Microglia Modulation
Bibliographic record
Abstract
Chronic pain is a complex sensory, cognitive, and emotional experience that imposes a great personal, psychological, and socioeconomic burden on patients. An estimated 1.5 billion people worldwide are afflicted with chronic pain, which is often difficult to treat and may be resistant to the potent pain-relieving effects of opioid analgesics. Attention has therefore focused on advancing new pain therapies directed at the cannabinoid system because of its key role in pain modulation. Endocannabinoids and exogenous cannabinoids exert their actions primarily through Gi/o-protein coupled cannabinoid CB1 and CB2 receptors expressed throughout the nervous system. CB1 receptors are found at key nodes along the pain pathway and their activity gates both the sensory and affective components of pain. CB2 receptors are typically expressed at low levels on microglia, astrocytes, and peripheral immune cells. In chronic pain states, there is a marked increase in CB2 expression which modulates the activity of these central and peripheral immune cells with important consequences for the surrounding pain circuitry. Growing evidence indicate that interventions targeting CB1 or CB2 receptors improve pain outcomes in a variety of preclinical pain models. In this mini-review, we will highlight recent advances in understanding how cannabinoids modulate microglia function and its implications for cannabinoid-mediated analgesia, focusing on microglia-neuron interactions within the spinal nociceptive circuitry.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".