Using an<i>In Vitro</i>Model of Lipid Deposition to Assess the Efficiency of Hydrogen Peroxide Solutions to Remove Lipid from Various Contact Lens Materials
Bibliographic record
Abstract
PURPOSE: To test the ability of two commercially available hydrogen peroxide disinfection solutions, one containing a surfactant and one without, to remove lipid from various contact lens materials using in vitro radiochemical experiments. METHODS: Etafilcon A, senofilcon A and balafilcon A contact lens materials were incubated in an artificial tear solution (ATS) containing a mixture of lipids, proteins, mucin and either (14)C-cholesterol or (14)C-phosphatidylcholine for 8 h. Following incubation, the lenses were removed, rinsed, and placed for 16 h in either a surfactant-containing peroxide solution (ClearCare®), a peroxide solution devoid of a surfactant (AOSept®) or stored without solution (control). This process was repeated every day for 1 week. The lenses were extracted with a previously optimized extraction protocol, evaporated, re-suspended, fluor added and counted for their radioactive signals. Masses of lipids deposited were calculated based on standard calibration curves, the disinfection solutions were compared and repeated measures ANOVA and post hoc statistical analysis was completed using Statistica 9. RESULTS: The results of this experiment found that daily disinfection with hydrogen peroxide solutions reduced the amount of cholesterol and phosphatidylcholine deposited on the three contact lens materials examined, however in many cases the reduction in deposition was less than 15% when compared to the control. Disinfection with the solution containing the surfactant (ClearCare), resulted in the least deposited cholesterol and phosphatidylcholine for all materials, however not all of the comparisons were statistically significant. CONCLUSIONS: Overall, ClearCare hydrogen peroxide disinfection solution containing Pluronic 17R4 removed the most lipid from lenses when compared to the non-surfactant containing AOSept or the control, for both lipids and all lens materials. However, the differences found were quite small at times and whether these differences are clinically significant are yet to be determined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".