Nanotechnology for the Prevention and Treatment of Cataract
Bibliographic record
Abstract
PURPOSE: The purpose of this article was to review recent advances in the applications of nanotechnology in cataract treatment and prevention strategies. DESIGN: A literature review on the use of nanotechnology for the prevention and treatment of cataract was done. METHODS: Research articles about nanotechnology-based treatments and prevention technologies for cataract were searched on Web of Science, and the most recent advances were reported. RESULTS: Nonsteroid anti-inflammatory drugs, natural antioxidants, biologic and chemical chaperones, and chaperones such as molecules have found great application in preventing and treating cataracts. Current scientific research on new treatment strategies, which focuses on the biochemical basis of the disease, will likely result in new anticataract agents. However, none of the drug formulations will be approved for use unless efficient delivery is promised. Nanoparticle engineering together with biomimetic strategies enable the development of next-generation, more efficient, less complex, and personalized treatments. CONCLUSIONS: The only currently available treatment for cataracts, surgical replacement of the opacified lens, is not an easily accessible option in developing countries. New treatment strategies based on topical drugs would enable treatment to reach massive populations facing the threat of blindness and more effectively deal with the postsurgical complications. Nanotechnology plays a key role in improving drug delivery systems with enhanced controlled release, targeted delivery, and bioavailability to overcome diffusion limitations in the eye.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".