Advancements in hydrogel technology for ocular drug delivery
Bibliographic record
Abstract
Hydrogel-based drug delivery systems (DDS) offer promising alternatives for treating ocular diseases by overcoming the limitations of traditional therapies, such as low bioavailability, frequent administration, and invasiveness. Hydrogels, with their high biocompatibility and ability to respond to external stimuli, can provide sustained and targeted drug delivery. This review highlights the unique properties of hydrogels, including their swelling behavior, porosity, and mechanical strength, making them suitable for various ocular applications. The classification of hydrogels based on cross-linking methods, origins, and stimuli responsiveness is discussed, emphasizing their potential in drug delivery for dry eye disease (DED), glaucoma, corneal alkali burns, and neovascularization. Notable advances include thermosensitive and pH-responsive hydrogels, which have shown promising results in preclinical studies. Despite these advances, most studies are still in preclinical stages, highlighting the need for rigorous human trials to validate the safety and efficacy of hydrogel DDS. Collaborative efforts among researchers, pharmacologists, and ophthalmologists are essential to translating these innovations into clinical practice, ultimately improving patient outcomes in ocular disease management.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".