3D Printed Hydrogels for Ocular Wound Healing
Bibliographic record
Abstract
Corneal disease is one of the most significant causes of blindness around the world. Presently, corneal transplantation is the only way to treat cornea blindness. It should be noted that the amount of cornea that people donate is so much less than that required (1:70). Therefore, scientists have tried to resolve this problem with tissue engineering and regenerative medicine. Fabricating cornea with traditional methods is difficult due to their unique properties, such as transparency and geometry. Bioprinting is a technology based on additive manufacturing that can use different biomaterials as bioink for tissue engineering, and the emergence of 3D bioprinting presents a clear possibility to overcome this problem. This new technology requires special materials for printing scaffolds with acceptable biocompatibility. Hydrogels have received significant attention in the past 50 years, and they have been distinguished from other materials because of their unique and outstanding properties. Therefore, hydrogels could be a good bioink for the bioprinting of different scaffolds for corneal tissue engineering. In this review, we discuss the use of different types of hydrogel for bioink for corneal tissue engineering and various methods that have been used for bioprinting. Furthermore, the properties of hydrogels and different types of hydrogels are described.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".