Impact of Tear Film Components on Lysozyme Deposition to Contact Lenses
Bibliographic record
Abstract
PURPOSE: To investigate the impact of lactoferrin and lipids on the kinetic deposition of lysozyme on silicone and conventional hydrogel lenses, using a complex artificial tear solution (ATS). METHODS: Two silicone hydrogel lenses (AIR OPTIX AQUA; lotrafilcon B and ACUVUE OASYS; senofilcon A) and two conventional hydrogel lenses (ACUVUE 2; etafilcon A and PROCLEAR; omafilcon A) were investigated. Lenses were incubated in four different solutions: a complex ATS consisting of various salts, lipids, proteins, and mucins, an ATS without lactoferrin (ATS w/o Lac), an ATS without lipids (ATS w/o Lip), and an ATS without lactoferrin and lipids (ATS w/o Lac & Lip), each containing 2% radiolabeled (125I) lysozyme (1.9 mg/ml). After each time point (4, 12 h and 1, 2, 3, 5, 7, 14, 21, 28 days), the amount of lysozyme per lens was quantified. RESULTS: After 28 days, lotrafilcon B lenses incubated in ATS deposited significantly less lysozyme (9.7 ± 1.4 μg) than when incubated in solutions not containing lactoferrin and lipids (more than 11.8 μg) (p < 0.001). Lysozyme uptake to senofilcon A lenses was higher in ATS w/o Lip (5.3 ± 0.1 μg) compared with other solutions (less than 3.9 μg) (p < 0.001). Etafilcon A lenses deposited the most lysozyme in all four solutions compared with the rest of the lens types (p < 0.001). For etafilcon A lenses, less lysozyme was deposited when incubated in ATS w/o Lip (588.6 ± 0.4 μg) compared with the other solutions (more than 642.6 μg) (p < 0.001). Omafilcon A lenses in ATS w/o Lac accumulated significantly less lysozyme (12.8 ± 1.0 μg) compared with the other solutions (more than 14.2 μg) (p < 0.001). CONCLUSIONS: An ATS containing lactoferrin and lipids impacts lysozyme deposition on both silicone and conventional hydrogel contact lenses. When performing in vitro experiments to study protein deposition on contact lenses, more complex models should be used to better mimic the human tear film.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".