Comparative Study of Lens Solutions' Ability to Remove Tear Constituents
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to use atomic force microscopy to compare and characterize the cleaning abilities of a hydrogen peroxide-based system (HPS) and a polyhexamethylene biguanide-containing multipurpose solution (MPS) at removing in vitro deposited tear film constituents, as well as to determine deposition patterns on various silicone hydrogel contact lenses. METHODS: Silicone hydrogel materials-balafilcon A (BA), lotrafilcon B (LB), and senofilcon A (SA)-were incubated for 1 week in an artificial tear solution (ATS) containing representative lipids, proteins, and salts from the tear film. Atomic force microscopy was used to resolve each lens before and after being cleaned overnight in HPS or MPS. Atomic force microscopy was used again to resolve HPS/MPS-cleaned lenses, which were reincubated in fresh ATS for 1 week, before and after an overnight clean in their respective cleaning solution. RESULTS: Atomic force microscopy imaging was able to characterize lens deposits with high resolution. Lenses incubated in ATS revealed distinct differences in their deposition pattern across lens materials. The surface of BA contained about 20-nm-high deposits, whereas deposit heights up to 150 nm completely occluded the surface of SA. Lotrafilcon B lenses revealed clusters of deposits up to 90 nm. The use of either lens solution left trace amounts of tear film constituents, although components from the MPS were seen adsorbed onto the surface after cleaning. Surface roughness (Ra) measurements revealed a significant difference between ATS-incubated and HPS/MPS-cleaned SA and LB lenses (p < 0.05). Ra between first incubated and HPS/MPS-cleaned reincubated SA and LB was also significant (p < 0.05). CONCLUSIONS: Unique variations in ATS deposition patterns were seen between lenses with atomic force microscopy. The application of both HPS and MPS removed most visible surface deposits.
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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 | 0.000 | 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".