The Role of Decorin in the Ocular Surface: Structure, Function, and Therapeutic Potential
Bibliographic record
Abstract
The ocular surface, comprising the cornea, conjunctiva, and sclera, relies on a complex extracellular matrix (ECM) for structural support and functional integrity. Decorin (DCN), a small leucine-rich proteoglycan, a key ECM component involved in collagen fibrillogenesis, growth factor modulation, and cellular regulation, is essential for maintaining corneal transparency, wound healing, and preventing pathological fibrosis. DCN interacts with various target molecules, including transforming growth factor-beta (TGF-β), vascular endothelial growth factor (VEGF), and epidermal growth factor receptor (EGFR), influencing critical processes, such as cell proliferation, inflammation, and angiogenesis. These interactions underscore DCN's therapeutic potential in ocular disorders like corneal scarring, pterygium, and keratoconus. This review explores the structure, expression, and functions of DCN in the ocular surface, emphasizing its regulatory mechanisms and therapeutic potential. Recent advancements include gene therapy approaches, sustained release systems, and topical applications, each demonstrating significant promise in treating ocular surface diseases. Additionally, the review highlights the importance of DCN in ocular disorders, including congenital stromal corneal dystrophy (CSCD), KC, and pterygium. This review aims to elucidate the multifaceted roles of DCN, paving the way for innovative therapeutic strategies in ocular surface therapy. Future directions for DCN research involve advanced delivery mechanisms, gene editing technologies, and expanded studies on other ocular surfaces.
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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 | 0.001 | 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".