Cryopreservation of human umbilical vein and porcine corneal endothelial cell monolayers
Bibliographic record
Abstract
Cryopreservation of endothelium is one of the major challenges in the cryopreservation of complex tissues. Human umbilical vein endothelial cells (HUVECs) in suspension are available commercially and recently their post-thaw cell membrane integrity was significantly improved by cryopreservation in 5% dimethyl sulfoxide (Me2SO) and 6% hydroxyethyl starch (HES). However, cryopreservation of cells in monolayers has been elusive. The exact mechanisms of damage during cell monolayer cryopreservation are still under investigation. Here, we show that a combination of different factors contribute to significant progress in cryopreservation of endothelial monolayers. The addition of 2% chondroitin sulfate to 5% Me2SO and 6% HES and cooling at 0.2 or 1 °C/min led to high membrane integrity (97.3 ± 3.2%) immediately after thaw when HUVECs were cultured on a substrate with a coefficient of thermal expansion similar to that of ice. The optimized cryopreservation protocol was applied to monolayers of primary porcine corneal endothelial cells, and resulted in high post-thaw viability (95.9 ± 3.7% membrane integrity) with metabolic activity 12 h post-thaw comparable to unfrozen control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 0.000 |
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".