Biological and Clinical Implications of Lysozyme Deposition on Soft Contact Lenses
Bibliographic record
Abstract
Within a few minutes of wear, contact lenses become rapidly coated with a variety of tear film components, including proteins, lipids, and mucins. Tears have a rich and complex composition, allowing a wide range of interactions and competitive processes, with the first event observed at the interface between a contact lens and tear fluid being protein adsorption. Protein adsorption on hydrogel contact lenses is a complex process involving a variety of factors relating to both the protein in question and the lens material. Among tear proteins, lysozyme is a major protein that has both antibacterial and anti-inflammatory functions. Contact lens materials that have high ionicity and high water content have an increased affinity to accumulate lysozyme during wear, when compared with other soft lens materials, notably silicone hydrogel lenses. This review provides an overview of tear film proteins, with a specific focus on lysozyme, and examines various factors that influence protein deposition on contact lenses. In addition, the impact of lysozyme deposition on various ocular physiological responses and bacterial adhesion to lenses and the interaction of lysozyme with other tear proteins are reviewed. This comprehensive review suggests that deposition of lysozyme on contact lens materials may provide a number of beneficial effects during contact lens wear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".