Hyaluronan incorporation into model contact lens hydrogels as a built‐in lubricant: Effect of hydrogel composition and proteoglycan 4 as a lubricant in solution
Bibliographic record
Abstract
Abstract Contact lens friction significantly correlates with subjective comfort. Hyaluronan (HA) and proteoglycan 4 (PRG4) are natural boundary lubricants present in the body. The objective of this study was to assess the effect of crosslinked HA into the bulk of model contact lens materials pHEMA, pHEMA/TRIS, and DMAA/TRIS on surface wettability, protein sorption, and boundary lubricating properties at a material–cornea biointerface, both alone and synergistically with PRG4 in solution. Surface wettability was assessed by water contact angle measurement, protein sorption by lysozyme sorption assay, and boundary lubricating properties using an in vitro friction test method. HA incorporation (HA inc ) increased the surface wettability of all materials, and reduced protein sorption for pHEMA and DMAA/TRIS. HA inc increased friction for pHEMA, and DMAA/TRIS, whereas a decrease was observed for pHEMA/TRIS. A combination of HA inc and PRG4 sol had a synergistic effect of reducing friction only for pHEMA/TRIS. This combination had similar friction reduction compared with PRG4 sol alone for DMAA/TRIS. These results indicate HA incorporation could be an effective internal wetting agent, antiadhesive, and boundary lubricant for pHEMA/TRIS silicone hydrogels. In conclusion, HA incorporation can reduce friction of hydrogels alone and in combination with PRG4 in solution, though in a hydrogel composition‐dependent (e.g., TRIS) manner. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1818–1826, 2018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".