Nanotechnology-Based Approaches for Ophthalmology Applications
Bibliographic record
Abstract
PURPOSE: The purpose of this article was to review recent advances in applications of nanotechnology in ophthalmology. DESIGN: Literature review. METHODS: Research articles about nanotechnology-based treatments for particular eye diseases and diagnostic technologies were searched through Web of Science, and the most recent advances were reported. RESULTS: Nanotechnology enabled to improve drug and gene delivery systems, medicine solubility and short half-life in biological systems, controlled release, targeted delivery, bioavailability, diffusion limitations, and biocompatibility so far. These promising achievements are the assurance of next-generation treatment technologies. As well as treatment, nanofabrications systems such as microelectromechanical manufacturing systems removed the limitations of nanodevice generations and led the development of diagnostic tools such as intraocular pressure monitors and biosensors. CONCLUSIONS: The pursuit of personalized medicine approaches for combating ocular diseases may be possible only through the development of nanotechnology platforms that include molecular-level engineering. Nanoparticle engineering is a common thread; herein, we attempt to show unmodified nanoparticles as well as interesting and representative biomimetic strategies can be used for specific diseases. Finally, through combining microelectromechanical and nanoelectromechanical manufacturing system strategies, interesting manufacturing and sensor development can be accomplished for early detection and, in some cases, treatment of ocular diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.007 | 0.003 |
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".