A nanocomposite contact lens for the delivery of hydrophilic protein drugs
Bibliographic record
Abstract
To improve the efficiency of topical ocular drug administration, we developed a nanocomposite contact lens to deliver hydrophilic protein drugs over a prolonged period of time. Here, an in situ route was used to encapsulate the hydrophilic protein drug bovine serum albumin (BSA) within gelatin nanoparticles (NPs), 180 ± 20 nm in diameter, which were then grafted onto the lens material, a copolymer of 2-hydroxyethyl methacrylate and 2-aminoethyl methacrylate p(HEMA-co-AEMA), through photopolymerization. The thickness of the nanocomposite lens was controlled at 150 μm. The release kinetics of BSA from plain p(HEMA-co-AEMA), gelatin NPs, and gelatin NP-grafted p(HEMA-co-AEMA) in phosphate buffer saline (PBS) at pH = 7.4 were studied. The release profile of BSA encapsulated within gelatin NPs could be monitored for 7 days, three times longer than that of BSA soaked in p(HEMA-co-AEMA). Our findings indicate that use of the nanocomposite contact lens, i.e. BSA-loaded gelatin NPs incorporated into p(HEMA-co-AMEA), can prolong the release profile of BSA to 12 days. The swelling behavior and interior strain of p(HEMA-co-AEMA) with and without grafted NPs (1000 : 1 w/w) were further investigated. The nanocomposite lens shows higher swelling behavior than the plain p(HEMA-co-AEMA) lens does. The addition of gelatin NPs to hydrogels leads to a relatively uniform interior strain with lower stiffness. Thus, the prolonged release might be due to a combination of effects. Internal diffusion of the nanocomposite lens materials may significantly contribute to the prolonged release of protein drugs. Furthermore, the nanocomposite lens materials had no cytotoxicity. This new biocompatible nanocomposite might be further developed as an alternative tool for continuous topical ocular drug delivery over a prolonged period of time.
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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".