In situ Tissue Regeneration in the Cornea from Bench to Bedside
Bibliographic record
Abstract
Corneal blindness accounts for 5.1% of visual deficiency and is the fourth leading cause of blindness globally. An additional 1.5-2 million people develop corneal blindness each year, including many children born with or who later develop corneal infections. Over 90% of corneal blind people globally live in low- and middle-income regions (LMIRs), where corneal ulcers are approximately 10-fold higher compared to high-income countries. While corneal transplantation is an effective option for patients in high-income countries, there is a considerable global shortage of corneal graft tissue and limited corneal transplant programs in many LMIRs. In situ tissue regeneration aims to restore diseases or damaged tissues by inducing organ regeneration. This can be achieved in the cornea using biomaterials based on extracellular matrix (ECM) components like collagen, hyaluronic acid, and silk. Solid corneal implants based on recombinant human collagen type III were successfully implanted into patients resulting in regeneration of the corneal epithelium, stroma, and sub-basal nerve plexus. As ECM crosslinking and manufacturing methods improve, the focus of biomaterial development has shifted to injectable, in situ gelling formulations. Collagen, collagen-mimetic, and gelatin-based in situ gelling formulas have shown the ability to repair corneal wounds, surgical incisions, and perforations in in-vivo models. Biomaterial approaches may not be sufficient to treat inflammatory conditions, so other cell-free therapies such as treatment with tolerogenic exosomes and extracellular vesicles may improve treatment outcomes. Overall, many of the technologies described here show promise as future medical devices or combination products with cell or drug-based therapies. In situ tissue regeneration, particularly with liquid formulas, offers the ability to triage and treat corneal injuries and disease with a single regenerative solution, providing alternatives to organ transplantation and improving patient outcomes.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".