Turning Down the Thermostat: Modulating the Endocannabinoid System in Ocular Inflammation and Pain
Bibliographic record
Abstract
The endocannabinoid system (ECS) has emerged as an important regulator of both physiological and pathological processes. Notably, this endogenous system plays a key role in the modulation of pain and inflammation in a number of tissues. The components of the ECS, including endocannabinoids, their cognate enzymes and cannabinoid receptors, are localized in the eye, and evidence indicates that ECS modulation plays a role in ocular disease states. Of these diseases, ocular inflammation presents a significant medical problem, given that current clinical treatments can be ineffective or are associated with intolerable side-effects. Furthermore, a prominent comorbidity of ocular inflammation is pain, including neuropathic pain, for which therapeutic options remain limited. Recent evidence supports the use of drugs targeting the ECS for the treatment of ocular inflammation and pain in animal models; however, the potential for therapeutic use of cannabinoid drugs in the eye has not been thoroughly investigated at this time. This review will highlight evidence from experimental studies identifying components of the ocular ECS and discuss the functional role of the ECS during different ocular inflammatory disease states, including uveitis and corneal keratitis. Candidate ECS targeted therapies will be discussed, drawing on experimental results obtained from both ocular and non-ocular tissue(s), together with their potential application for the treatment of ocular inflammation and pain.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".