Competitive Effects from an Artificial Tear Solution to Protein Adsorption
Bibliographic record
Abstract
PURPOSE: To compare the adsorption of lysozyme, lactoferrin, and albumin to various contact lens materials, between single-protein solutions and a multicomponent artificial tear solution (ATS). Additionally, extra steps were taken to distinguish loosely and tightly bound protein, the latter of which may be fully or partially denatured. METHODS: Using a previously described ATS, we measured the time-dependent adsorption of lys, lac, and alb onto one conventional hydrogel and four silicone hydrogel contact lens materials between the first minute and up to 1 week of protein interaction with the material surface. Proteins were quantified using I radiolabeling of each protein individually in ATS and buffered saline. Extra steps were taken to limit the amount of unbound I and to quantify the amount of reversibly bound protein. RESULTS: Comfilcon A, balafilcon A, and etafilcon A did not show any relevant competitive adsorption between the ATS components and lys, lac, or alb until after 1 week. Competitive adsorption effects for lys, lac, and alb were observed in as little as 1 minute on lotrafilcon B. Lotrafilcon B had no reversibly bound protein at any time points. The ionic materials balafilcon A and etafilcon A deposited significant amounts of reversibly bound lysozyme and lactoferrin in just 10 minutes. Senofilcon A apparent deposition was below our thresholds of confidence for this protein quantification method. CONCLUSIONS: Both the competition between lys, lac, and alb and ATS components and the reversibility of these bound proteins is material specific. Coadsorption of lys, lac, and alb with ATS components can increase the reversibility of their adsorption.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".