Protocol for Conditional Knockout Mouse Model of OHT and Glaucoma
Bibliographic record
Abstract
This chapter describes the protocols used to create a conditional knockout mouse model to study partially closed-angle glaucoma where the intraocular pressure (IOP) is raised beyond normal levels. The Cre/loxP approach was employed as a strategy to conditionally inactivate the AP-2β gene from the developing periocular mesenchyme (POM) that is responsible for creating the trabecular meshwork (TM) (Taiyab et al., 2022). The Mgp-Cre knock-in (Mgp-Cre.KI) mice, in combination with the AP-2β floxed mice, were utilized to create a targeted deletion of AP-2β to the TM area. The AP-2β TMR KO mutants exhibit an absent TM and underdeveloped Schlemm’s canal (SC) and partial adherence of the iris to the cornea. The mutants have significantly higher IOP than their wild-type littermates by one month of age, and this is correlated with a progressive, significant loss of retinal ganglion cells, reduced retinal thickness, and reduced retinal function, as measured by electroretinography. Thus, these mutant mice can serve as a model for understanding and treating progressive human primary angle-closure glaucoma with associated ocular hypertension.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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".