<i>In vitro</i>uptake and release of natamycin Dex<i>-b-</i>PLA nanoparticles from model contact lens materials
Bibliographic record
Abstract
PURPOSE: To evaluate the uptake and release of the antifungal agent natamycin encapsulated within poly(D,L-lactide)-dextran nanoparticles (Dex-b-PLA NPs) from model contact lens (CL) materials. METHODS: Six model CL materials (gel 1:poly(hydroxyethyl methacrylate, pHEMA); gel 2:85% pHEMA: 15% [Tris(trimethylsiloxy)silyl]-propyl methacrylate (TRIS); gel 3: 75% pHEMA: 25% TRIS; gel 4: 85% N,N dimethylacrylamide (DMAA): 15% TRIS; gel 5:75% DMAA: 25% TRIS; and gel 6: DMAA) were prepared using a photoinitiation procedure. The gels were incubated in: (1) natamycin dissolved in deionized (DI) water and (2) natamycin encapsulated within Dex-b-PLA NPs in dimethylsulfoxide/DI water. Natamycin release from these materials was monitored using UV-visible spectrophotometry at 304 nm over 7 d. RESULTS: Natamycin uptake by all model CL materials increased between 1 and 7 d (p < 0.001). The uptake of natamycin-NPs was higher than the uptake of the drug alone in DI water (p < 0.05). Drug release was higher in materials containing DMAA than pHEMA (p < 0.05). All gels loaded with natamycin-NPs also released more drug compared to gels soaked with natamycin in DI water (p < 0.001). After 1 h, CL materials loaded with natamycin alone released 28-82% of the total drug release. With the exception of gel 6, this burst released was reduced to 21-54% for CL materials loaded with natamycin-NPs. CONCLUSIONS: Model CL materials loaded with natamycin-Dex-b-PLA NPs were able to release natamycin for up to 12 h under infinite sink conditions. DMAA-TRIS materials may be more suitable for drug delivery of natamycin due to the higher drug release observed with these materials.
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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) | 0.000 | 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) | 0.001 | 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".