Efficacy of Contact Lens Care Solutions in Removing Cholesterol Deposits From Silicone Hydrogel Contact Lenses
Bibliographic record
Abstract
PURPOSE: To determine the efficacy of multipurpose solutions (MPSs) on the removal of cholesterol deposits from silicone hydrogel (SH) contact lens materials using an in vitro model. MATERIALS AND METHODS: Five SH lens materials: senofilcon A, comfilcon A, balafilcon A, lotrafilcon A, and lotrafilcon B were removed from the blister pack (n=4 for each lens type), incubated for 7 days at 37°C in an artificial tear solution containing C radiolabeled cholesterol. Thereafter, lenses were stored in a preserved saline solution control (Sensitive Eyes Saline Plus) or cleaned with 1 of the 5 MPSs incorporating different preservatives (POLYQUAD/ALDOX, polyquaternium-1/alexidine, polyquaternium-1/PHMB, and 2 based on PHMB alone) using a rub and rinse technique, according to the manufacturer's recommendations, and stored in the MPS for a minimum of 6 hr. Lenses were then extracted with 2:1 chloroform:methanol, analyzed in a beta counter, and μg/lens of cholesterol was determined. RESULTS: Balafilcon A and senofilcon A lens materials showed the highest amounts of accumulated cholesterol (0.93±0.02 μg/lens; 0.95±0.01 μg/lens, respectively), whereas lotrafilcon A and lotrafilcon B deposited the lowest amounts (0.37±0.03 μg/lens; 0.47±0.12 μg/lens, respectively). For all lens materials, the MPS preserved with POLYQUAD/ALDOX removed more deposited cholesterol than any other test solution; however, the amount of removed cholesterol contamination from the individual contact lenses was only statistically significant for balafilcon A and senofilcon A (P=0.006 and P=0.042, respectively). Sensitive eyes and the other evaluated MPSs showed no significant effect on cholesterol removal (P>0.05). CONCLUSION: Cholesterol-removal efficacy varies depending on the combination of lens material and solution. Only 1 MPS showed a statistically significant reduction of cholesterol deposit for only 2 of the 5 tested lens materials.
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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) | 0.001 | 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".