Neuroprotection in glaucoma: Mechanisms beyond intraocular pressure lowering
Bibliographic record
Abstract
Glaucoma is a common, complex, multifactorial neurodegenerative disease characterized by progressive dysfunction and then loss of retinal ganglion cells, the output neurons of the retina. Glaucoma is the most common cause of irreversible blindness and affects ∼80 million people worldwide with many more undiagnosed. The major risk factors for glaucoma are genetics, age, and elevated intraocular pressure. Current strategies only target intraocular pressure management and do not directly target the neurodegenerative processes occurring at the level of the retinal ganglion cell. Despite strategies to manage intraocular pressure, as many as 40% of glaucoma patients progress to blindness in at least one eye during their lifetime. As such, neuroprotective strategies that target the retinal ganglion cell and these neurodegenerative processes directly are of great therapeutic need. This review will cover the recent advances from basic biology to on-going clinical trials for neuroprotection in glaucoma covering degenerative mechanisms, metabolism, insulin signaling, mTOR, axon transport, apoptosis, autophagy, and neuroinflammation. With an increased understanding of both the basic and clinical mechanisms of the disease, we are closer than ever to a neuroprotective strategy for glaucoma.
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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 | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".