Proteoglycan 4 and hyaluronan as boundary lubricants for model contact lens hydrogels
Bibliographic record
Abstract
Clinical data show that in vitro contact lens friction is related to in vivo comfort. Solutions of biological lubricants hyaluronan (HA) and proteoglycan 4 (PRG4, also known as lubricin) reduce friction at a cornea-polydimethylsiloxane (PDMS) interface. The purpose of this study was to (1) determine if PRG4 can sorb to and lubricate model contact lens materials and (2) assess the boundary lubricating ability of PRG4 and HA compared to saline on model contact lens materials. PRG4 was obtained from bovine cartilage culture and suspended in saline at 300 µg/mL. N,N-Dimethylacrylamidetris (trimethylsiloxy) silane, (DMAA/TRIS) and methacryloxypropyltris (trimethylsiloxy) silane (pHEMA/TRIS) silicone hydrogels were prepared. A previously described in vitro eyelid-hydrogel and cornea-hydrogel biomechanical friction test was used to determine boundary lubricant effect. PRG4 sorption to the hydrogels was assessed using a soak-rinse protocol and western blotting. PRG4 effectively lubricated both silicone hydrogel materials and HA effectively lubricated pHEMA/TRIS, as indicated by a statistically significant reduction in friction compared to the saline control lubricant. An HA and PRG4 combination showed a synergistic effect for pHEMA/TRIS and effectively lubricated DMAA/TRIS. Biological boundary lubricants HA and PRG4 were shown to effectively lubricate silicone hydrogels when in solution. Additionally, HA and PRG4 showed synergistic lubrication for pHEMA/TRIS. The purpose of this study was not to replicate the friction coefficients of contact lenses, but rather to investigate lubricant-surface interactions for common contact lens constituents. These findings contribute to the potential development of biomolecule based lubricant drops for contact lens wearers. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1329-1338, 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".