Enzyme-Assisted Deep Anterior Lamellar Keratoplasty—A New Method of Lamellar Dissection—A Wetlab-Based Pilot Study
Bibliographic record
Abstract
PURPOSE: To explore the safety of a new technique of lamellar dissection, using enzymatic digestion of the corneal stroma and extracellular matrix. METHODS: This was a wetlab-based pilot study of hyaluronidase and trypsin-assisted deep anterior lamellar keratoplasty (DALK) in cadaveric human corneal tissue. Enzyme-assisted DALK was performed on 17 tissues. These underwent histologic analysis using a pneumatic dissection specimen as control. Rates of perforation and Descemet membrane (DM) exposure were recorded by clinical observation and by optical coherence tomography in selected cases. Where possible, pre- and postsurgical endothelial cell counts were obtained via specular microscopy. Two tissues from the same donor were halved, with each half soaked in a different solution (Optisol, balanced salt solution, hyaluronidase, and trypsin) for 13.5 hours to observe maximal effect. RESULTS: Successful exposure of DM was achieved in 8 specimens. In the remaining 9, manual dissection was possible to a residual depth of 25 to 90 μm where measured with optical coherence tomography. Three tissues had perforation of DM, all via manual maneuvers. No deleterious effects on residual host tissue were observed by light microscopy with no significant rates of endothelial cell loss in 8 tissues in which a predissection cell count was obtainable. The 2 enzymes had differing effects on soaked specimens that were reflected intraoperatively. CONCLUSION: Preliminary results of this ex vivo study are encouraging that enzymolysis may represent an effective innovation in DALK surgery with an acceptable safety profile. Further studies are required to refine the technique and application of the enzymes in vivo.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".