The Friction Coefficient of Commercially Available Contact Lenses at a Cornea-Contact Lens Biointerface
Bibliographic record
Abstract
Background: At the ocular surface, epithelial cells are subject to shear and frictionforces during blinking. This can lead to wear damage, especially when there isinadequate lubrication in the presence of a compromised tear film1. Contact lenses areassociated with increased lid wiper damage, epitheliopathy and tear film instability2caused by increased friction due to the lens material3. This may lead to dry eye disease.Due to this, lenses often include agents that attempt to improve surface properties.These properties, particularly friction, are therefore paramount to preventing ocularsurface damage and discomfort.Methods: A custom cornea-contact lens biomechanical friction test was used totest commercially available contact lenses (Air Optix Aqua, Acuvue Oasys, Acuvue2,and Acuvue TruEye Dailies) The contact lenses and human corneas (obtained fromLions Eye Bank, n=5) were articulated against each other in a saline bath at effectivesliding velocities between 0.3-30 mm/sec and under loads of approximately 12-32 kPa.Friction coefficients were calculated from the axial load and torque measured duringarticulation of the test surfaces.Results: Kinetic friction coefficients, , in saline for each lens wasapproximately 0.080.02, 0.120.04, 0.150.05 and 0.090.02 for Acuvue2, Air Optix,TruEye and Oasys respectively (meansem). Values of in TruEye weresignificantly greater than those in both AC2 and Oasys (p 0.05).Conclusions: TruEye, a silicone hydrogel daily wear lens, had higher friction thanboth Oasys and Acuvue2, which were not significantly different from each other. Theseresults suggest that the unique wetting agent contained in Oasys and TruEye do notsignificantly affect in vitro friction measurements. Future experiments will examine ifadding ocular lubricants, such as hyaluronan and/or lubricin, can further reduce thefriction of these lenses and ultimately improve in vivo wear.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".