Molecular regulation of neuroinflammation in glaucoma: Current knowledge and the ongoing search for new treatment targets
Bibliographic record
Abstract
Neuroinflammation relying on the inflammatory responses of glial cells has emerged as an impactful component of the multifactorial etiology of neurodegeneration in glaucoma. It has become increasingly evident that despite early adaptive and reparative features of glial responses, prolonged reactivity of the resident glia, along with the peripheral immune cells, create widespread toxicity to retinal ganglion cell (RGC) axons, somas, and synapses. As much as the synchronized responses of astrocytes and microglia to glaucoma-related stress or neuron injury, their bi-directional interactions are critical to build and amplify neuroinflammation and to dictate the neurodegenerative outcome. Although distinct molecular programs regulate somatic and axonal degeneration in glaucoma, inhibition of neurodegenerative inflammation can provide a broadly beneficial treatment strategy to rescue RGC integrity and function. Since inflammatory toxicity and mitochondrial dysfunction are converging etiological paths that can boost each other and feed into a vicious cycle, anti-inflammatory treatments may also offer a multi-target potential. This review presents an overview of the current knowledge on neuroinflammation in glaucoma with particular emphasis on the cell-intrinsic and cell-extrinsic factors involved in the reciprocal regulation of glial responses, the interdependence between inflammatory and mitochondrial routes of neurodegeneration, and the research aspects inspiring for prospective immunomodulatory treatments. With the advent of powerful technologies, ongoing research on molecular and functional characteristics of glial responses is expected to accumulate more comprehensive and complementary information and to rapidly move the field forward to safe and effective modulation of the glial pro-inflammatory activities, while restoring or augmenting the glial immune-regulatory and neurosupport functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".