Kinetics of Competitive Adsorption between Lysozyme and Lactoferrin on Silicone Hydrogel Contact Lenses and the Effect on Lysozyme Activity
Bibliographic record
Abstract
PURPOSE: To determine the effect of competitive adsorption between lysozyme and lactoferrin on silicone hydrogel contact lenses and the effect on lysozyme activity. METHODS: Three commercially available silicone hydrogel contact lens materials (senofilcon A, lotrafilcon B and balafilcon A) were examined, for time points ranging from 10 s to 2 h. Total protein deposition was determined by I(125) radiolabeling of lysozyme and lactoferrin, while the activity of lysozyme was determined by a micrococcal activity assay. RESULTS: Senofilcon A and balafilcon A did not show any relevant competitive adsorption between lysozyme and lactoferrin. Lotrafilcon B showed reduced protein deposition due to competitive adsorption for lactoferrin at all time points and lysozyme after 7.5 min. Co-adsorption of lactoferrin and lysozyme decreased the activity of lysozyme in solution for senofilcon A and lotrafilcon B, but co-adsorption had no effect on the surface activity of lysozyme for all lens types investigated. CONCLUSIONS: Competition between lysozyme and lactoferrin is material specific. Co-adsorption of lysozyme and lactoferrin does not affect the activity of surface-bound lysozyme but can reduce the activity of subsequently desorbed lysozyme.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".