Advancements in Hydrogels for Corneal Healing and Tissue Engineering
Bibliographic record
Abstract
Hydrogels have garnered significant attention for their versatile applications across various fields, including biomedical engineering. This review delves into the fundamentals of hydrogels, exploring their definition, properties, and classification. Hydrogels, as three-dimensional networks of crosslinked polymers, possess tunable properties such as biocompatibility, mechanical strength, and hydrophilicity, making them ideal for medical applications. Uniquely, this article offers original insights into the application of hydrogels specifically for corneal tissue engineering, bridging a gap in current research. The review further examines the anatomical and functional complexities of the cornea, highlighting the challenges associated with corneal pathologies and the current reliance on donor corneas for transplantation. Considering the global shortage of donor corneas, this review discusses the potential of hydrogel-based materials in corneal tissue engineering. Emphasis is placed on the synthesis processes, including physical and chemical crosslinking, and the integration of bioactive molecules. Stimuli-responsive hydrogels, which react to environmental triggers, are identified as promising tools for drug delivery and tissue repair. Additionally, clinical applications of hydrogels in corneal pathologies are explored, showcasing their efficacy in various trials. Finally, the review addresses the challenges of regulatory approval and the need for further research to fully realize the potential of hydrogels in corneal tissue engineering, offering a promising outlook for future developments in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".