The Impact of Lipid on Contact Angle Wettability
Bibliographic record
Abstract
PURPOSE: To analyze the effect of in vitro lipid doping on conventional hydrogel (CH) and silicone hydrogel (SH) lens wettability, assessed by sessile drop contact angle (CA) measurement. METHODS: Nine contact lens materials, five SHs and four CH, were incubated with two different lipid tear solutions (LTS) containing cholesterol, cholesteryl oleate, oleic acid, oleic acid methyl ester, and triolein. The first LTS was a "low" concentration solution, which was close to human values, and the second was a "high" concentration. Lenses were soaked in the two LTS types for 2 or 5 days and compared with lenses soaked in phosphate buffered saline (PBS) only. After soaking, advancing CAs were measured on a customized computerized device using a sessile drop method. RESULTS: Compared with PBS, CAs for untreated SHs were unaffected by soaking in the LTS, with typical CA values of >95 degrees (p > 0.05). The surface-treated SH materials exhibited markedly reduced CAs after lipid exposure, with the high concentration LTS reducing the CA to <5 degrees (p < 0.01). The CH materials all exhibited lower CAs after soaking, with values typically decreasing to 35 degrees , which was significantly lower than that seen with PBS (p < 0.01). CONCLUSION: Exposure to lipid may improve the wettability of certain SH and CH materials, particularly those SH materials that are surface treated. This may help to explain why certain SH materials appear to improve in comfort for some patients during the first few hours or days of wear.
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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) | 0.000 | 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".