Microglial Cannabinoid Type 1 Receptor Regulates Brain Inflammation in a Sex-Specific Manner
Bibliographic record
Abstract
Background: Neuroinflammation is a key feature shared by most, if not all, neuropathologies. It involves complex biological processes that act as a protective mechanism to fight against the injurious stimuli, but it can lead to tissue damage if self-perpetuating. In this context, microglia, the main cellular actor of neuroinflammation in the brain, are seen as a double-edged sword. By phagocyting neuronal debris, these cells can not only provide tissue repair but can also contribute to neuronal damage by releasing harmful substances, including inflammatory cytokines. The mechanisms guiding these apparent opposing actions are poorly known. The endocannabinoid system modulates the release of inflammatory factors such as cytokines and could represent a functional link between microglia and neuroinflammatory processes. According to transcriptomic databases and in vitro studies, microglia, the main source of cytokines in pathological conditions, express the cannabinoid type 1 receptor (CB1R). Methods: We thus developed a conditional mouse model of CB1R deletion specifically in microglia, which was subjected to an immune challenge (peripheral lipopolysaccharide injection). Results: Our results reveal that microglial CB1R differentially controls sickness behavior in males and females. Conclusion: These findings add to the comprehension of neuroinflammatory processes and might be of great interest for future studies aimed at developing therapeutic strategies for brain disorders with higher prevalence in men.
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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) | 0.003 | 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".