Bovine Posterior Limbus: An Evaluation of an Alternative Source for Corneal Endothelial and Trabecular Meshwork Stem/Progenitor Cells
Bibliographic record
Abstract
A growing body of evidence has revealed that stem-like cells in the posterior limbus of the eye between the corneal endothelium (CE) and trabecular meshwork (TM) may be able to rejuvenate these tissues in disease. However, these cells have not been clearly defined and we have named them PET cells (progenitor cells of the endothelium and trabeculum). A good and inexpensive animal model for PET cells is lacking, so we investigated bovine eyes as an effective large tissue source. We showed the presence of stem/progenitor cells in the bovine CE, transition zone, and TM in situ. Floating spheres cultured from the CE and TM showed similar stem cell marker expression patterns. Both the CE and TM spheres were bipotent and highly proliferative, but with limited secondary sphere-forming capability. They were highly prone to differentiate back into the cell type of their tissue of origin. It is speculated that the PET cells become more tissue-specific as they migrate away from their niche. Here, we showed that PET cells are present in the posterior limbus of bovine eyes and that they can be successfully cultured and expanded. PET cells represent an attractive target for developing new treatments to regenerate both the CE and TM, thereby reducing the requirement for donor tissue for corneal transplant and invasive treatments for glaucomatous patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".