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 (HAinc) increased the surface wettability of all materials, and reduced protein sorption for pHEMA and DMAA/TRIS. HAinc increased friction for pHEMA, and DMAA/TRIS, whereas a decrease was observed for pHEMA/TRIS. A combination of HAinc and PRG4sol had a synergistic effect of reducing friction only for pHEMA/TRIS. This combination had similar friction reduction compared with PRG4sol 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".