Dose-Dependent and Synergistic Effects of Proteoglycan 4 on Boundary Lubrication at a Human Cornea–Polydimethylsiloxane Biointerface
Bibliographic record
Abstract
OBJECTIVES: Proteoglycan 4 (PRG4), also known as lubricin, is a boundary lubricating mucin-like glycoprotein present on several tissue surfaces in the body. The objectives of this study were to (1) implement and characterize an in vitro boundary lubrication test at a human cornea-polydimethylsiloxane (PDMS) biointerface and (2) determine the dose-dependent and synergistic effects of PRG4, with hyaluronan (HA), on ocular surface boundary lubrication using this test. METHODS: Human corneas and model PDMS material were articulated against each other, at effective sliding velocities v(eff) between 0.3 and 30 mm/sec under physiologic loads of approximately 8 to 25 kPa. Samples were tested serially in (1) saline, PRG4 at 30, 100, 300 μg/mL resuspended in saline, then saline again or (2) saline, AQuify Comfort Eye Drops (containing 0.1% HA), 300 μg/mL PRG4 in saline, 300 μg/mL PRG4 in AQuify, then saline again. Both static and kinetic friction coefficients were calculated. RESULTS: PRG4 effectively lowered friction at the cornea-PDMS biointerface, both alone in a dose-dependent manner and in combination with HA. PRG4 reduced kinetic friction coefficients, <μ(kinetic, Neq)>, from approximately 0.30 in saline, to approximately 0.30, 0.24, and 0.17 in 30, 100, and 300 μg/mL PRG4, respectively. Values of <μ(kinetic, Neq)> in AQuify, approximately 0.32, were similar to those in saline; however, when combined with 300 μg/mL PRG4, values of <μ(kinetic, Neq)> were reduced to approximately 0.15. CONCLUSIONS: PRG4 functions as an effective ocular surface boundary lubricant, both alone in a dose-dependent manner and in combination with HA.
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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".