Structural and functional neuroprotection in glaucoma: role of galantamine-mediated activation of muscarinic acetylcholine receptors
Bibliographic record
Abstract
Glaucoma is the leading cause of irreversible blindness worldwide. Loss of vision due to glaucoma is caused by the selective death of retinal ganglion cells (RGCs). Treatments for glaucoma, limited to drugs or surgery to lower intraocular pressure (IOP), are insufficient. Therefore, a pressing medical need exists for more effective therapies to prevent vision loss in glaucoma patients. In this in vivo study, we demonstrate that systemic administration of galantamine, an acetylcholinesterase inhibitor, promotes protection of RGC soma and axons in a rat glaucoma model. Functional deficits caused by high IOP, assessed by recording visual evoked potentials from the superior colliculus, were improved by galantamine. These effects were not related to a reduction in IOP because galantamine did not change the pressure in glaucomatous eyes and it promoted neuronal survival after optic nerve axotomy, a pressure-independent model of RGC death. Importantly, we demonstrate that galantamine-induced ganglion cell survival occurred by activation of types M1 and M4 muscarinic acetylcholine receptors, while nicotinic receptors were not involved. These data provide the first evidence of the clinical potential of galantamine as neuroprotectant for glaucoma and other optic neuropathies, and identify muscarinic receptors as potential therapeutic targets for preventing vision loss in these blinding diseases.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".