Modification of Timolol Release From Silicone Hydrogel Model Contact Lens Materials Using Hyaluronic Acid
Bibliographic record
Abstract
OBJECTIVES: The ability of hyaluronic acid (HA) to act as a functional additive in model silicone hydrogel contact lenses to alter the uptake and release characteristics of timolol was investigated. METHODS: Model contact lenses were prepared using 2 primary formulations: 2-hydroxyethyl methacrylate (HEMA) with 3-methacryloxypropyltris (trimethylsiloxy) silane (TRIS) in a 9:1 (wt:wt) ratio or N,N-dimethylacrylamide (DMA) with TRIS in a 1:1 (wt:wt) ratio. Ethylene glycol dimethacrylate (EGDMA) was used as the cross-linker. Four different model lens compositions were explored: unmodified controls, lenses containing HA, lenses that were molecularly imprinted with timolol maleate, and those that were both imprinted and contained HA. Model lenses were then used in subsequent materials characterization, drug loading, and drug release studies. RESULTS: Hyaluronic acid was shown to have the ability to act as a functional additive in these model contact lenses, significantly increasing the drug loading and release mass. This ability seemed to be independent of molecular imprinting, but its efficacy was related to the concentration of HA contained within model lenses and the concentration of drug loading solution used to facilitate uptake. Timolol release was sustained for a duration of approximately 2 days, and the dose of drug was shown to be controlled by both HA-drug interactions and molecular imprinting within the silicone hydrogels. CONCLUSIONS: Hyaluronic acid, although different than typical functional monomers used in molecular imprinting, can be a useful additive to modify the mass of drug release from model silicone hydrogel lenses.
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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".