Expansion and cryopreservation of porcine and human corneal endothelial cells
Bibliographic record
Abstract
Impairment of the corneal endothelium causes blindness that afflicts millions worldwide and constitutes the most often cited indication for corneal transplants. The scarcity of donor corneas has prompted the alternative use of tissue-engineered grafts which requires the ex vivo expansion and cryopreservation of corneal endothelial cells. The aims of this study are to culture and identify the conditions that will yield viable and functional corneal endothelial cells after cryopreservation. Previously, using human umbilical vein endothelial cells (HUVECs), we employed a systematic approach to optimize the post-thaw recovery of cells with high membrane integrity and functionality. Here, we investigated whether improved protocols for HUVECs translate to the cryopreservation of corneal endothelial cells, despite the differences in function and embryonic origin of these cell types. First, we isolated endothelial cells from pig corneas and then applied an interrupted slow cooling protocol in the presence of dimethyl sulfoxide (Me 2 SO), with or without hydroxyethyl starch (HES). Next, we isolated and expanded endothelial cells from human corneas and applied the best protocol verified using porcine cells. We found that slow cooling at 1 °C/min in the presence of 5% Me 2 SO and 6% HES, followed by rapid thawing after liquid nitrogen storage, yields membrane-intact cells that could form monolayers expressing the tight junction marker ZO-1 and cytoskeleton F-actin, and could form tubes in reconstituted basement membrane matrix. Thus, we show that a cryopreservation protocol optimized for HUVECs can be applied successfully to corneal endothelial cells, and this could provide a means to address the need for off-the-shelf cryopreserved cells for corneal tissue engineering and regenerative medicine.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".