Surface modification of model hydrogel contact lenses with hyaluronic acid via thiol-ene “click” chemistry for enhancing surface characteristics
Bibliographic record
Abstract
Discontinuation of contact lens wear as a result of ocular dryness and discomfort is extremely common; as many as 26% of contact lens wearers discontinue use within the first year. While patients are generally satisfied with conventional hydrogel lenses, improving on-eye comfort continues to remain a goal. Surface modification with a biomimetic, ocular friendly hydrophilic layer of a wetting agent is hypothesized to improve the interfacial interactions of the contact lens with the ocular surface. In this work, the synthesis and characterization of poly(2-hydroxyethyl methacrylate) surfaces grafted with a hydrophilic layer of hyaluronic acid are described. The immobilization reaction involved the covalent attachment of thiolated hyaluronic acid (20 kDa) on acrylated poly(2-hydroxyethyl methacrylate) via nucleophile-initiated Michael addition thiol-ene "click" chemistry. The surface chemistry of the modified surfaces was analyzed by Fourier transform infrared spectroscopy-attenuated total reflectance and X-ray photoelectron spectroscopy. The appearance of N (1s) and S (2p) peaks on the low resolution X-ray photoelectron spectroscopy spectra confirmed successful immobilization of hyaluronic acid. Grafting hyaluronic acid to the poly(2-hydroxyethyl methacrylate) surfaces decreased the contact angle, the dehydration rate, and the amount of nonspecific sorption of lysozyme and albumin in comparison to pristine hydrogel materials, suggesting the development of more wettable surfaces with improved water-retentive and antifouling properties, while maintaining optical transparency (>92%). In vitro testing also showed excellent viability of human corneal epithelial cells with the hyaluronic acid-grafted poly(2-hydroxyethyl methacrylate) surfaces. Hence, surface modification with hyaluronic acid via thiol-ene "click" chemistry could be useful in improving contact lens surface properties, potentially alleviating symptoms of contact lens related dryness and discomfort during wear.
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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".