Confocal Microscopy and Albumin Penetration into Contact Lenses
Bibliographic record
Abstract
PURPOSE: To develop a novel in vitro method to detect the depth of penetration of the tear film protein albumin into contact lens materials using confocal laser scanning microscopy (CLSM). METHODS: A poly-HEMA-based hydrogel (etafilcon A) and a silicone hydrogel material (lotrafilcon B) were examined. In vitro, bovine serum albumin (BSA) was labeled with 5-(4,6-dichloro-s-triazin-2-ylamino) fluorescein hydrochloride (DTAF). The lenses were incubated in this protein solution (0.5 mg/ml) at 37 degrees C. After 1 and 7 days incubation, the lenses were examined using CLSM (Zeiss 510, config. META 18) and the location of the fluorescently labeled BSA was identified. RESULTS: BSA adsorption on the surface and penetration into the lens matrix occurred at a higher concentration for etafilcon compared to lotrafilcon (p < 0.001). For both materials, BSA was detected on the surface after 1 day of incubation. Significant levels of BSA were detected within the matrix of etafilcon after as little as 1 day (p < 0.001), but no BSA was detected in the matrix of lotrafilcon at any time (p > 0.05). CONCLUSION: CLSM can be successfully used to examine the depth of penetration of fluorescently labeled proteins into various hydrogel polymers. Our results show that etafilcon lenses both adsorb BSA on the surface and absorb BSA within the matrix, whereas lotrafilcon B adsorbs small amounts of BSA on the surface only.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".