The heterogeneity of astrocytes in glaucoma
Bibliographic record
Abstract
Glaucoma is a leading cause of blindness with progressive degeneration of retinal ganglion cells. Aging and increased intraocular pressure (IOP) are major risk factors. Lowering IOP does not always stop the disease progression. Alternative ways of protecting the optic nerve are intensively studied in glaucoma. Astrocytes are macroglia residing in the retina, optic nerve head (ONH), and visual brain, which keep neuronal homeostasis, regulate neuronal activities and are part of the immune responses to the retina and brain insults. In this brief review, we discuss the activation and heterogeneity of astrocytes in the retina, optic nerve head, and visual brain of glaucoma patients and animal models. We also discuss some recent transgenic and gene knockout studies using glaucoma mouse models to clarify the role of astrocytes in the pathogenesis of glaucoma. Astrocytes are heterogeneous and play crucial roles in the pathogenesis of glaucoma, especially in the process of neuroinflammation and mitochondrial dysfunction. In astrocytes, overexpression of Stat3 or knockdown of IκKβ/p65, caspase-8, and mitochondrial uncoupling proteins (Ucp2) can reduce ganglion cell loss in glaucoma mouse models. Based on these studies, therapeutic strategies targeting the heterogeneity of reactive astrocytes by enhancing their beneficial reactivity or suppressing their detrimental reactivity are alternative options for glaucoma treatment in the future.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".