Recent Advances in Hydrogels: Ophthalmic Applications in Cell Delivery, Vitreous Substitutes, and Ocular Adhesives
Bibliographic record
Abstract
With the prevalence of eye diseases, such as cataracts, retinal degenerative diseases, and glaucoma, different treatments including lens replacement, vitrectomy, and stem cell transplantation have been developed; however, they are not without their respective shortcomings. For example, current methods to seal corneal incisions induced by cataract surgery, such as suturing and stromal hydration, are less than ideal due to the potential for surgically induced astigmatism or wound leakage. Vitrectomy performed on patients with diabetic retinopathy requires an artificial vitreous substitute, with current offerings having many shortcomings such as retinal toxicity. The use of stem cells has also been investigated in retinal degenerative diseases; however, an optimal delivery system is required for successful transplantation. The incorporation of hydrogels into ocular therapy has been a critical focus in overcoming the limitations of current treatments. Previous reviews have extensively documented the use of hydrogels in drug delivery; thus, the goal of this review is to discuss recent advances in hydrogel technology in surgical applications, including dendrimer and gelatin-based hydrogels for ocular adhesives and a variety of different polymers for vitreous substitutes, as well as recent advances in hydrogel-based retinal pigment epithelium (RPE) and retinal progenitor cell (RPC) delivery to the retina.
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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 | 0.003 |
| 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) | 0.003 | 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".