Cannabinoids affect the mouse visual acuity via the cannabinoid receptor type 2
Bibliographic record
Abstract
Recently, there have been increasing indications that the endocannabinoid (eCB) system is involved in vision. Multiple research teams studied the cannabinoid receptor type 2 (CB2R) expression and function in the mouse retina. Here, we examined the consequence of CB2R modulation on visual acuity using genetic and pharmacologic tools. We found that Cnr2 knockout mice show an enhanced visual acuity, CB2R activation decreased visual acuity while CB2R blockade with the inverse agonist AM630 increased it. The inhibition of 2-arachidonylglycerol (2-AG) synthesis and degradation also greatly increased and decreased visual acuity, respectively. No differences were seen when the cannabinoid receptor type 1 (CB1R) was deleted, blocked or activated implying that CB2R exclusively mediates cannabinoid modulation of the visual acuity. We also investigated the role of cannabinoids in retinal function using electroretinography (ERG). We found that modulating 2-AG levels affected many ERG components, such as the a-wave and oscillatory potentials (OPs), suggesting an impact on cones and amacrine cells. Taken together, these results reveal that CB2R modulates visual acuity and that eCBs such as 2-AG can modulate both visual acuity and retinal sensitivity. Finally, these findings establish that CB2R is present in visual areas and regulates vision-related functions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".