Biodegradable Nanoparticle for Cornea Drug Delivery: Focus Review
Bibliographic record
Abstract
During recent decades, researchers all around the world have focused on the characteristic pros and cons of the different drug delivery systems for cornea tissue change for sense organs. The delivery of various drugs for cornea tissue is one of the most attractive and challenging activities for researchers in biomaterials, pharmacology, and ophthalmology. This method is so important for cornea wound healing because of the controllable release rate and enhancement in drug bioavailability. It should be noted that the delivery of various kinds of drugs into the different parts of the eye, especially the cornea, is so difficult because of the unique anatomy and various barriers in the eye. Nanoparticles are investigated to improve drug delivery systems for corneal disease. Biodegradable nanocarriers for repeated corneal drug delivery is one of the most attractive and challenging methods for corneal drug delivery because they have shown acceptable ability for this purpose. On the other hand, by using these kinds of nanoparticles, a drug could reside in various part of the cornea for longer. In this review, we summarized all approaches for corneal drug delivery with emphasis on the biodegradable nanoparticles, such as liposomes, dendrimers, polymeric nanoparticles, niosomes, microemulsions, nanosuspensions, and hydrogels. Moreover, we discuss the anatomy of the cornea at first and gene therapy at the end.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".