Biomaterials-enabled cornea regeneration in patients at high risk for rejection of donor tissue transplantation
Bibliographic record
Abstract
The severe worldwide shortage of donor organs, and severe pathologies placing patients at high risk for rejecting conventional cornea transplantation, have left many corneal blind patients untreated. Following successful pre-clinical evaluation in mini-pigs, we tested a biomaterials-enabled pro-regeneration strategy to restore corneal integrity in an open-label observational study of six patients. Cell-free corneal implants comprising recombinant human collagen and phosphorylcholine were grafted by anterior lamellar keratoplasty into corneas of unilaterally blind patients diagnosed at high-risk for rejecting donor allografts. They were followed-up for a mean of 24 months. Patients with acute disease (ulceration) were relieved of pain and discomfort within 1-2 weeks post-operation. Patients with scarred or ulcerated corneas from severe infection showed better vision improvement, followed by corneas with burns. Corneas with immune or degenerative conditions transplanted for symptom relief only showed no vision improvement overall. However, grafting promoted nerve regeneration as observed by improved touch sensitivity to near normal levels in all patients tested, even for those with little/no sensitivity before treatment. Overall, three out of six patients showed significant vision improvement. Others were sufficiently stabilized to allow follow-on surgery to restore vision. Grafting outcomes in mini-pig corneas were superior to those in human subjects, emphasizing that animal models are only predictive for patients with non-severely pathological corneas; however, for establishing parameters such as stable corneal tissue and nerve regeneration, our pig model is satisfactory. While further testing is merited, we have nevertheless shown that cell-free implants are potentially safe, efficacious options for treating high-risk patients.
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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 | 0.000 | 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".