Deposition of Fluorescently Tagged Lysozyme on Contact Lenses in a Physiological Blink Model
Bibliographic record
Abstract
PURPOSE: To visualize the deposition of fluorescein isothiocyanate (FITC) lysozyme on daily disposable contact lenses (CLs) using a novel blink model. METHODS: Three daily disposable conventional hydrogel CLs (etafilcon A, omafilcon A, and nelfilcon A) and three silicone hydrogel CLs (delefilcon A, senofilcon A, and somofilcon A) were evaluated in the study. The CLs were mounted onto a novel blink model and exposed to an artificial tear solution containing FITC lysozyme for 2 and 10 hr. The flow rate and blink speed were set to 1 μL/min and 6 blinks/min, respectively. After the incubation period, a 5-mm-diameter disc was punched out from the center of the lens and mounted on a microscope slide. The slides were imaged using the Zeiss 510 Meta confocal laser scanning microscope, which scanned the lens from the front to the back surface at 5-μm increments. RESULTS: There was an increase in deposition of FITC lysozyme for all lens types with increasing incubation time (P<0.05), with the exception of somofilcon A, which did not show statistical significance between 2 and 10 hr (P>0.05). The conventional hydrogel CLs deposited higher amounts of FITC lysozyme than the silicone hydrogel CLs (P<0.001), with etafilcon A depositing the highest at all time points (P<0.05). Interestingly, at the 2-hr incubation time, most CLs showed a higher amount of deposition at the front surface than the back surface of the lens. In particular, etafilcon A showed preferred deposition at the front surface at all time points. CONCLUSION: The results suggest that there is differential deposition at the front surface of the CL, which is exposed to the prelens tear film, compared with the back surface of the CL, which is exposed to the postlens tear film. Therefore, it may be beneficial to design CL materials with differing surface properties for the front and back surfaces of the CL to enhance interactions with the tear film and ocular surface.
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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) | 0.001 | 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".