Hyaluronic acid as an internal wetting agent in model DMAA/TRIS contact lenses
Bibliographic record
Abstract
Model silicone hydrogel contact lenses, comprised of N,N-dimethylacrylamide and methacryloxypropyltris (trimethylsiloxy) silane, were fabricated and hyaluronic acid (HA) was incorporated as an internal wetting agent using a dendrimer-based method. HA and dendrimers were loaded into the silicone hydrogels and cross-linked using 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide chemistry. The presence and location of HA in the hydrogels was confirmed using X-ray photoelectron spectroscopy and confocal laser scanning microscopy, respectively. The effects of the presence of HA on the silicone hydrogels on hydrophilicity, swelling behavior, transparency, and lysozyme sorption and denaturation were evaluated. The results showed that HA increased the hydrophilicity and the equilibrium water content of the hydrogels without affecting transparency. HA also significantly decreased the amount of lysozyme sorption (p < 0.002). HA had no effect on lysozyme denaturation in hydrogels containing 0% and 1.7% methacrylic acid (MAA) (by weight) but when the amount of MAA was increased to 5%, the level of lysozyme denaturation was significantly lower compared to control materials. These results suggest that HA has great potential to be used as a wetting agent in silicone hydrogel contact lenses to improve wettability and to decrease lysozyme sorption and denaturation.
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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) | 0.000 | 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".