Enantiomeric Agonists of the Type 2 Cannabinoid Receptor Reduce Retinal Damage during Proliferative Vitreoretinopathy and Inhibit Hyperactive Microglia <i>In Vitro</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microglia are resident immune cells of the central nervous system (CNS) and propagate inflammation following damage to the CNS, including the retina. Proliferative vitreoretinopathy (PVR) is a condition that can emerge following retinal detachment and is characterized by severe inflammation and microglial proliferation. The type 2 cannabinoid receptor (CB 2 ) is an emerging pharmacological target to suppress microglial-mediated inflammation when the eyes or brain are damaged. CB 2 -knockout mice have exacerbated inflammation and retinal pathology during experimental PVR. We aimed to assess the anti-inflammatory effects of CB 2 stimulation in the context of retinal damage and also explore the mechanistic roles of CB 2 in microglia function. To target CB 2, we used a highly selective agonist, HU-308, as well as its enantiomer, HU-433, which is a putative selective agonist. First, β-arrestin2 and Gα i recruitment was measured to compare activation of human CB 2 in an in vitro heterologous expression system. Both agonists were then utilized in a mouse model of PVR, and the effects on retinal damage, inflammation, and cell death were assessed. Finally, we used an in vitro model of microglia to determine the effects of HU-308 and HU-433 on phagocytosis, cytokine release, migration, and intracellular signaling. We observed that HU-308 more strongly recruited both β-arrestin2 and Gα i compared to HU-433. Stimulation of CB 2 with either drug effectively blunted LPS- and IFNγ-mediated signaling as well as NO and TNF release from microglia. Furthermore, both drugs reduced IL-6 accumulation, total caspase-3 cleavage, and retinal pathology following the induction of PVR. Ultimately, this work supports that CB 2 is a valuable target for drugs to suppress inflammation and cell death associated with infection or sterile retinopathy, although the magnitude of effector recruitment may not be predictive of anti-inflammatory capacity.
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 | 0.000 | 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 | 0.001 |
| 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".