Memantine Protects Neurons From Shrinkage in the Lateral Geniculate Nucleus in Experimental Glaucoma
Bibliographic record
Abstract
OBJECTIVE: To determine whether memantine as a treatment for glaucoma prevents neuron shrinkage in the lateral geniculate nucleus, the major target for retinal ganglion cells. METHODS: Sixteen monkeys with right-eye unilateral experimental glaucoma for 14 months were studied and treated with memantine (n = 9) or vehicle only (n = 7). Left lateral geniculate nucleus relay neurons (layers 1, 4, and 6) were examined following parvalbumin immunolabeling. Cell body cross-sectional areas and neuron numbers were assessed using unbiased methods. Memantine- and vehicle-treated glaucoma groups were compared using t tests and analysis of covariance. RESULTS: Compared with vehicle-treated animals, memantine-treated animals showed significantly less mean +/- SD neuron shrinkage in layers 1 (-4.0% +/- 13.9% vs 28.2% +/- 17.4%; P = .001) and 4 (24.9% +/- 10.0% vs 37.2% +/- 12.3%; P = .04). For layer 6, the difference was not statistically significant (34.2% +/- 10.1% vs 45.3% +/- 14.5%; P = .10). Analysis of covariance results showed significantly less neuron shrinkage in the memantine-treated group for layers 1, 4, and 6 (P < .001; P < .02; and P < .04, respectively). This difference was greatest in layer 1. In each of these layers, neuron numbers did not differ significantly between groups. CONCLUSION: Monkeys with glaucoma that were treated with memantine showed significantly less neuron shrinkage in the lateral geniculate nucleus than the vehicle-treated glaucoma group. CLINICAL RELEVANCE: The finding that memantine protects adult visual neurons from transsynaptic atrophy in experimental glaucoma could have therapeutic value. Currently, memantine is being tested in an ongoing clinical trial as a treatment 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".