<p>In vitro Evaluation of the Location of Cholesteryl Ester Deposits on Monthly Replacement Silicone Hydrogel Contact Lens Materials</p>
Bibliographic record
Abstract
PURPOSE: The deposition profile of cholesteryl ester on the surface and throughout the matrix of silicone hydrogel contact lens (CL) materials was determined under conditions that mimic a daily wear regimen. METHODS: In this in vitro study, four SiHy CL materials (senofilcon C, lotrafilcon B, comfilcon A and samfilcon A) were incubated in an artificial tear solution (ATS) for up to 30 days. CL incubation was alternated between the ATS (16 hours) and a multipurpose care regimen (8 hours). The ATS included fluorescently tagged cholesteryl ester (5-cholesten-3ß-ol 6-[(7-nitro-2-1,3-benzoxadiazol-4-yl)amino]caproate; CE-NBD) and confocal laser scanning microscopy visualized the distribution of the lipid through the CLs. RESULTS: The distribution of CE-NBD was homogenous from the anterior to posterior surface in senofilcon C and comfilcon A, at all time points. For lotrafilcon B and samfilcon A, CE-NBD localization was heterogeneous, with greater amounts on the surfaces on Day 1 and Day 14 compared to the lens matrix; however, differences in concentration between the surface and bulk diminished by Day 30. CONCLUSION: The distribution of the non-polar lipid CE-NBD varied with lens material chemistry. While some lens materials deposited the lipid primarily on the surface after 16 hours of exposure, all materials exhibited a homogenous distribution after one month.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".