Lipid mediators in glaucoma: Unraveling their diverse roles and untapped therapeutic potential
Bibliographic record
Abstract
Glaucoma is a complex neurodegenerative disease characterized by optic nerve damage and visual field loss, and remains a leading cause of irreversible blindness. Elevated intraocular pressure (IOP) is a critical risk factor that requires effective management. Emerging research underscores dual roles of bioactive lipid mediators in both IOP regulation, and the modulation of neurodegeneration and neuroinflammation in glaucoma. Bioactive lipids, encompassing eicosanoids, specialized pro-resolving mediators (SPMs), sphingolipids, and endocannabinoids, have emerged as crucial players in these processes, orchestrating inflammation and diverse effects on aqueous humor dynamics and tissue remodeling. Perturbations in these lipid mediators contribute to retinal ganglion cell loss, vascular dysfunction, oxidative stress, and neuroinflammation. Glaucoma management primarily targets IOP reduction via pharmacological agents and surgical interventions, with prostaglandin analogues at the forefront. Intriguingly, additional lipid mediators offer promise in attenuating inflammation and providing neuroprotection. Here we explore these pathways to shed light on their intricate roles, and to unveil novel therapeutic avenues for glaucoma management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".