Impact of Cosmetics on the Surface Properties of Silicone Hydrogel Contact Lenses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This study evaluated the impact of various cosmetics on the surface properties of silicone hydrogel (SiHy) contact lens materials. METHODS: In this in vitro experiment, 7 SiHy contact lens materials were coated with 1 of 9 cosmetics, including common hand creams (3), eye makeup removers (3), and mascaras (3). Dark-field microscopy images were taken to determine pixel brightness (PB) after cosmetic exposure, which describes the visible surface deposition (n=6 for each lens type), with a higher PB indicating increased deposition. The sessile drop technique was used to determine the advancing contact angle (CA). Measurements were repeated for both methods after a single peroxide-based cleaning cycle. RESULTS: Pixel brightness was significantly higher for mascara-coated lenses compared with the other cosmetic products (P<0.01). The peroxide-based lens care solution removed most deposits from the nonwaterproof mascara for 4 lens types, whereas deposits remained relatively unchanged for 1 waterproof mascara (P>0.05). Hand creams and makeup remover had minimal impact on PB. Changes in CA measurements after cosmetic application were highly lens dependent. Hand creams caused primarily a decrease in CA for 5 of the 7 lens types, whereas 1 of the waterproof mascaras caused a significant increase of 30 to 50° for 3 lens types. CONCLUSION: Some mascara-lens combinations resulted in increased CA and PB, which could have an impact on in vivo lens performance. Nonwaterproof mascara was mostly removed after a cleaning cycle. Further research is needed to understand the clinical implications for SiHy lens wearers using cosmetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it