Advancements in Polymer Biomaterials as Scaffolds for Corneal Endothelium Tissue Engineering
Bibliographic record
Abstract
Corneal endothelial dysfunction is a leading cause of vision loss globally, frequently requiring corneal transplantation. However, the limited availability of donor tissues, particularly in developing countries, has spurred on the exploration of tissue engineering strategies, with a focus on polymer biomaterials as scaffolds for corneal endotlhelium regeneration. This review provides a comprehensive overview of the advancements in polymer biomaterials, focusing on their role in supporting the growth, differentiation, and functional maintenance of human corneal endothelial cells (CECs). Key properties of scaffold materials, including optical clarity, biocompatibility, biodegradability, mechanical stability, permeability, and surface wettability, are discussed in detail. The review also explores the latest innovations in micro- and nano-topological morphologies, fabrication techniques such as electrospinning and 3D/4D bioprinting, and the integration of drug delivery systems into scaffolds. Despite significant progress, challenges remain in translating these technologies to clinical applications. Future directions for research are highlighted, including the need for improved biomaterial combinations, a deeper understanding of CEC biology, and the development of scalable manufacturing processes. This review aims to serve as a resource for researchers and clinician-scientists seeking to advance the field of corneal endothelium tissue engineering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".