Investigating the Synergistic Interactions of Surface Immobilized and Free Natural Ocular Lubricants for Contact Lens Applications: A Comparative Study between Hyaluronic Acid and Proteoglycan 4 (Lubricin)
Bibliographic record
Abstract
The main reasons for the discontinuation of contact lens wear are ocular dryness and discomfort. Proteoglycan 4 (PRG4), a mucinous glycoprotein, and hyaluronic acid (HA), a nonsulfated linear glycosaminoglycan, are naturally present in the eye and contribute to ocular hydration and lubrication. This study aimed to investigate the impact of the structure of the recombinant human PRG4 (rhPRG4)/HA complex on contact lens properties, when one agent is grafted and the counterpart is physisorbed on the surface of model conventional or silicone contact lens materials. Investigation of the wettability, water retention, antifouling, and boundary lubricant properties of the prepared hydrogels showed that the rhPRG4/HA interactions varied with the rhPRG/HA configuration on the hydrogel surface as well as the composition of the underlying substrate used. The rhPRG4-physisorbed/HA-grafted sample was characterized by better antifouling and boundary lubricant properties on the model conventional hydrogels, while the HA-physisorbed/rhPRG4-grafted sample exhibited improved surface wettability, antifouling, and water-retentive properties on the model silicone hydrogels. The results of this study contribute to the design of biomimetic contact lens surfaces that work synergistically with ocular fluid-phase biological agents to enhance compatibility between the contact lens and the ocular environment, alleviating dry eye symptoms and improving comfort.
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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".