Impact of a Hyaluronic Acid-Grafted Layer on the Surface Properties of Model Silicone Hydrogel Contact Lenses
Bibliographic record
Abstract
The introduction of high oxygen transmissibility silicone hydrogel lenses ameliorated hypoxia-related complications, making them the most prescribed type of contact lens (CL). Despite the progress made over the last 2 decades to improve their clinical performance, symptoms of ocular dryness and discomfort and a variety of adverse clinical events are still reported. Consequently, the rate of CL wear discontinuation has not been appreciably diminished by their introduction. Aiming to improve the interfacial interactions of silicone hydrogel CLs with the ocular surface, a biomimetic layer of hydrophilic glycosaminoglycan hyaluronic acid (HA) (100 kDa) was covalently attached to the surface of model poly(2-hydroxyethyl methacrylate- co-3-methacryloxypropyl-tris-(trimethylsiloxy)silane) (pHEMA- co-TRIS) silicone hydrogel materials via UV-induced thiol-ene "click" chemistry. The surface structural changes after each modification step were studied by Fourier transform infrared spectroscopy-attenuated total reflectance and X-ray photoelectron spectroscopy (XPS). Successful grafting of a homogeneous HA layer to the surface of the model silicone hydrogels was confirmed by the consistent appearance of N (1s) and the significant decrease of the Si (2p) peaks, as determined by low-resolution angle-resolved XPS. The HA-grafted surfaces demonstrated reduced contact angles, dehydration rate, and nonspecific deposition of lysozyme and albumin, while maintaining their optical transparency (>90%). In vitro studies demonstrated that the HA-grafted pHEMA- co-TRIS materials did not show any toxicity to human corneal epithelial cells. These results suggest that surface immobilization of HA via thiol-ene "click" chemistry can be used as a promising surface treatment for silicone hydrogel CLs.
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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".