<i>In Vitro</i> Effect of Lysozyme on Albumin Deposition to Hydrogel Contact Lens Materials
Bibliographic record
Abstract
SIGNIFICANCE: Albumin deposition on contact lenses could be detrimental to contact lens (CL) wear because this may increase the risk of bacterial binding and reduce comfort. Lysozyme deposition on selected lens materials would reduce albumin deposition on lenses. PURPOSE: This study aims to determine if lysozyme deposition on CLs could act as a barrier against subsequent albumin adsorption, using an in vitro model. METHODS: Six hydrogel CL materials (etafilcon A, polymacon, nelfilcon A, omafilcon A, ocufilcon B, and nesofilcon A) were evaluated. Four CLs of each type were soaked in lysozyme solution for 16 hours at 37°C. Lysozyme-coated lenses were then placed in vials with 1.5 mL of artificial tear solution containing I-labeled albumin for 16 hours at 37°C with shaking. Four uncoated lenses of each type were used as controls. Lenses soaked in radiolabeled albumin were rinsed in a phosphate-buffered saline solution, and radioactive counts were measured directly on lenses using a gamma counter. Albumin uptake on lenses was measured using a calibration curve by plotting radioactive counts versus protein concentration. RESULTS: Results are reported as mean ± SD. Lysozyme-coated etafilcon A lenses exhibited lower levels of deposited albumin than uncoated etafilcon A lenses (58 ± 12 vs. 84 ± 5 ng/lens; P < .05). There were no differences in albumin adsorption between control (uncoated) and lysozyme-coated polymacon (105 ± 10 vs. 110 ± 34 ng/lens), nelfilcon A (51 ± 7 vs. 42 ± 20 ng/lens), omafilcon A (90 ± 20 vs. 80 ± 38 ng/lens), ocufilcon B (87 ± 20 vs. 115 ± 50 ng/lens), and nesofilcon A (170 ± 29 vs. 161 ± 10 ng/lens) lens materials (P > .05). Uncoated nesofilcon A lenses deposited the highest amount of albumin when compared with other uncoated lenses (P < .05). CONCLUSIONS: This study demonstrates that lysozyme deposited onto etafilcon A resists the deposition of albumin, which may potentially be beneficial to CL wearers.
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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 | 0.001 | 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) | 0.000 | 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".