The Impact of the CB2 Cannabinoid Receptor in Inflammatory Diseases: An Update
Bibliographic record
Abstract
The emergence of inflammatory diseases is a heavy burden on modern societies. Cannabis has been used for several millennia to treat inflammatory disorders such as rheumatism or gout. Since the characterization of cannabinoid receptors, CB1 and CB2, the potential of cannabinoid pharmacotherapy in inflammatory conditions has received great interest. Several studies have identified the importance of these receptors in immune cell migration and in the production of inflammatory mediators. As the presence of the CB2 receptor was documented to be more predominant in immune cells, several pharmacological agonists and antagonists have been designed to treat inflammation. To better define the potential of the CB2 receptor, three online databases, PubMed, Google Scholar and clinicaltrial.gov, were searched without language restriction. The full texts of articles presenting data on the endocannabinoid system, the CB2 receptor and its role in modulating inflammation in vitro, in animal models and in the context of clinical trials were reviewed. Finally, we discuss the clinical potential of the latest cannabinoid-based therapies in inflammatory diseases.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".