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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".