The efficiency of contact lens care regimens on protein removal from hydrogel and silicone hydrogel lenses.
Bibliographic record
Abstract
PURPOSE: To investigate the efficiency of lysozyme and albumin removal from silicone hydrogel and conventional contact lenses, using a polyhexamethylene biguanide multipurpose solution (MPS) in a soaking or rubbing/soaking application and a hydrogen peroxide system (H(2)O(2)). METHODS: Etafilcon A, lotrafilcon B and balafilcon A materials were incubated in protein solutions for up to 14 days. Lenses were either placed in radiolabeled protein to quantify the amount deposited or in fluorescent-conjugated protein to identify its location, using confocal laser scanning microscopy (CLSM). Lenses were either rinsed with PBS or soaked overnight in H(2)O(2) or MPS with and without lens rubbing. RESULTS: After 14 days lysozyme was highest on etafilcon A (2,200 mug) >balafilcon A (50 microg) >lotrafilcon B (9.7 microg) and albumin was highest on balafilcon A (1.9 microg) =lotrafilcon B (1.8 microg) >etafilcon A (0.2 microg). Lysozyme removal was greatest for balafilcon A >etafilcon A >lotrafilcon B, with etafilcon A showing the most change in protein distribution. Albumin removal was highest from etafilcon A >balafilcon A >lotrafilcon B. H(2)O(2) exhibited greater lysozyme removal from etafilcon A compared to both MPS procedures (p<0.001) but performed similarly for lotrafilcon B and balafilcon A lenses (p>0.62). Albumin removal was solely material specific, while all care regimens performed to a similar degree (p>0.69). CONCLUSIONS: Protein removal efficiency for the regimens evaluated depended on the lens material and protein type. Overall, lens rubbing with MPS before soaking did not reduce the protein content on the lenses compared to nonrubbed lenses (p=0.89).
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".