Surface-Functionalized Model Contact Lenses with a Bioinspired Proteoglycan 4 (PRG4)-Grafted Layer
Bibliographic record
Abstract
Ocular dryness and discomfort are the primary reasons for the discontinuation of contact lens wear. This is mainly due to poorly hydrated contact lens surfaces and increased friction, particularly at the end of the day and can potentially cause reduced vision or even inflammation. Proteoglycan 4 (PRG4) is a mucinous glycoprotein with boundary lubricating properties, naturally found in the eye, able to prevent tear film evaporation and protect the ocular surface during blinking. Aiming to improve the interfacial interactions between the ocular surface and the contact lens, the synthesis and characterization of surface-modified model contact lenses with PRG4 is described. Full-length recombinant human PRG4 (rhPRG4) was successfully grafted onto the surface of model conventional and silicone hydrogel (SiHy) contact lenses via its somatomedin B-like end-domain using N, N ′-carbonyldiimidazole linking chemistry. Grafting was assessed by Fourier transform infrared spectroscopy-attenuated total reflectance, X-ray photoelectron spectroscopy, and radioactive (I 131 ) labeling. Surface immobilization of rhPRG4 led to model conventional and SiHy materials with improved antifouling properties, without impacting optical transparency or causing any toxic effects to human corneal epithelial cells in vitro. The surface wettability and the boundary friction against human corneal tissue were found to be substrate-dependent, with only the rhPRG4-grafted model SiHy exhibiting a reduced contact angle and kinetic friction coefficient compared to the unmodified surfaces. Hence, clinical grade rhPRG4 can be an attractive candidate for the development of novel bioinspired SiHy contact lenses, providing improved comfort and overall lens performance.
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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".