Validation of preloaded DMEK donor tissues: a laboratory-based study on endothelial cell viability and comparison of two F-mark inks
Bibliographic record
Abstract
OBJECTIVE: To investigate endothelial cell loss (ECL) associated with Descemet membrane endothelial keratoplasty (DMEK) donor tissues preloaded in the DMEK RAPID transport system after 1 and 5 days and to compare prestamping with 2 different F-mark inks. METHODS: DMEK donor tissues were stripped, marked with gentian violet dye applied as an F-mark, trephined, stained with trypan blue, and then preloaded into the DMEK RAPID transport system by an eye bank technician. Preloaded DMEK tissues were then unfolded and stained with calcein AM after 1 or 5 days of storage. Tissues were imaged, analyzed for total tissue ECL, and immunostained for corneal endothelium markers zonular occludens-1 and xCD166. Additionally, ECL and the intensity of an F-mark caused by 2 different inks were quantified. RESULTS: Preloaded DMEK tissues displayed an average ECL of 11.9% ± 4.5% (n = 8) at 1 day and 9.9% ± 4.2% (n = 9) at 5 days. No difference was found between the 2 groups. Zonular occludens-1 and activated leukocyte cell adhesion molecule (ALCAM; also know as CD166) staining showed that the corneal endothelial monolayer remained intact on preloaded tissues. On 5-day preloaded DMEK tissues, the average ECL and mean grayscale caused by the Keir Surgical ink F-mark and the Cardinal Health ink F-mark were 4.3% ± 0.8% and 158.5 ± 13.9% and 5.0% ± 1.1% and 142.9% ± 20.0%, respectively. No difference was found between the F-mark inks. CONCLUSION: Preloaded DMEK donor tissues resulted in an acceptable ECL range after 1 and 5 days of storage and were deemed suitable for transplantation. Both F-mark inks are acceptable for prestamping preloaded DMEK tissues prior to surgical transplantation with comparable ECL and intensities.
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 | 0.001 | 0.001 |
| 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".