An inducible rodent glaucoma model that exhibits gradual sustained increase in intraocular pressure with distinct inner retina and optic nerve inflammation
Bibliographic record
Abstract
Glaucoma is a chronic and progressive neurodegenerative disease of the optic nerve resulting in loss of retinal ganglion cells (RGCs) and vision. The most prominent glaucoma risk factor is increased intraocular pressure (IOP), and most models focus on reproducing this aspect to study disease mechanisms and targets. Yet, current models result in IOP profiles that often do not resemble clinical glaucoma. Here we introduce a new model that results in a gradual and sustained IOP increase over time. This approach modifies a circumlimbal suture method, taking care to make the sutures 'snug' instead of tight, without inducing an initial IOP spike. This approach did not immediately affect IOPs, but generated gradual ocular hypertension (gOHT) as the sutures tighten over time, in comparison to loosely sutured control eyes (CON), resulting in an average 12.6 mmHg increase in IOP at 17 weeks (p < 0.001). Corresponding characterization revealed relevant retinal and optic nerve pathology, such as thinning of the retinal nerve fiber layer, decreased optokinetic response, RGC loss, and optic nerve head remodeling. Yet, angles remained open, with no evidence of inflammation. Corresponding biochemical profiling indicated significant increases in TGF-β2 and 3, and IL-1 family cytokines in gOHT optic nerve tissues compared to CON, with accompanying microglial reactivity, consistent with active tissue injury and repair mechanisms. Remarkably, this signature was absent from optic nerves following acute ocular hypertension (aOHT) associated with intentionally tightened sutures, although the resulting RGC loss was similar in both methods. These results suggest that the pattern of IOP change has an important impact on underlying pathophysiology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".