Bibliographic record
Abstract
Nanozymes, characterized by their nanoscale size and enzyme-like catalytic activities, exhibit diverse therapeutic potentials, including anti-oxidative, anti-inflammatory, anti-microbial, and anti-angiogenic effects. These properties make them highly valuable in nanomedicine, particularly ocular therapy, bypassing the need for systemic delivery. Nanozymes show significant promise in tackling multi-factored ocular diseases, particularly those influenced by oxidation and inflammation, like dry eye disease, and age-related macular degeneration. Their small size, coupled with their ease of modification and integration into soft materials, facilitates the effective penetration of ocular barriers, thereby enabling targeted or prolonged therapy within the eye. This review is dedicated to exploring ocular diseases that are intricately linked to oxidation and inflammation, shedding light on the role of nanozymes in managing these conditions. Additionally, recent studies elucidating advanced applications of nanozymes in ocular therapeutics, along with their integration with soft materials for disease management, are discussed. Finally, this review outlines directions for future investigations aimed at bridging the gap between nanozyme research and clinical applications.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".