Localization of fluorescent gold nanoparticles throughout the eye after topical administration
Bibliographic record
Abstract
The human eye is a highly intricate sensory organ. When a condition requiring treatment occurs, eyedrops, which represent 90% of all ophthalmic treatments, are most frequently used. However, eyedrops are associated with low bioavailability, with less than 0.02% of therapeutic molecules reaching the anterior chamber. Thus, new delivery systems are required to ensure sufficient drug concentration over time at the target site. Gold nanoparticles are a promising avenue for drug delivery; however, they can be difficult to track in biological systems. Fluorescent gold nanoparticles, which have the same ultrastability and biocompatibility as their nonfluorescent counterpart, could act as an effective imaging tool to study their localization throughout the eye after administration. Thus, this study (1) synthesized and characterized fluorescent gold nanoparticles, (2) validated similar properties between nonfluorescent and fluorescent gold nanoparticles, and (3) determined their localization in the eye after topical application on ex vivo rabbit eyes. The fluorescent gold nanoparticles were synthesized, characterized, and identified in the cornea, iris, lens, and posterior segment of rabbit eyeballs, demonstrating tremendous potential for future drug delivery research.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".