A Biosynthetic Alternative to Human Donor Tissue for Inducing Corneal Regeneration: 24-Month Follow-Up of a Phase 1 Clinical Study
Bibliographic record
Abstract
Corneas from human donors are used to replace damaged tissue and treat corneal blindness, but there is a severe worldwide shortage of donor corneas. We conducted a phase 1 clinical study in which biosynthetic mimics of corneal extracellular matrix were implanted to replace the pathologic anterior cornea of 10 patients who had significant vision loss, with the aim of facilitating endogenous tissue regeneration without the use of human donor tissue. The biosynthetic implants remained stably integrated and avascular for 24 months after surgery, without the need for long-term use of the steroid immunosuppression that is required for traditional allotransplantation. Corneal reepithelialization occurred in all patients, although a delay in epithelial closure as a result of the overlying retaining sutures led to early, localized implant thinning and fibrosis in some patients. The tear film was restored, and stromal cells were recruited into the implant in all patients. Nerve regeneration was also observed and touch sensitivity was restored, both to an equal or to a greater degree than is seen with human donor tissue. Vision at 24 months improved from preoperative values in six patients. With further optimization, biosynthetic corneal implants could offer a safe and effective alternative to the implantation of human tissue to help address the current donor cornea shortage.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".