Extraction Efficiency of an Extraction Buffer Used to Quantify Lysozyme Deposition on Conventional and Silicone Hydrogel Contact Lens Materials
Bibliographic record
Abstract
PURPOSE: Extracting lysozyme from Food and Drug Administration group IV etafilcon lenses by using 0.2% trifluoroacetic acid and acetonitrile (TFA/ACN) is a well-established procedure. TFA/ACN has been the extraction buffer of choice for extracting proteins from silicone hydrogel contact lenses. The purpose of this study was to determine the efficiency of TFA/ACN in extracting lysozyme from silicone hydrogel and etafilcon lenses by using an in vitro model. METHODS: ACUVUE 2, Focus NIGHT & DAY, O2 Optix, PureVision, and ACUVUE Advance lenses were incubated in simple lysozyme solution and a complex artificial tear solution consisting of multiple tear components containing lysozyme labeled with iodine 125. All the silicone hydrogel lenses were incubated for 28 days, whereas the ACUVUE 2 lenses were incubated for 7 days at 37 degrees C with constant rotation. After the incubation period, radioactive counts were determined, and the lenses were placed in an appropriate volume of the buffer for 24 hours in darkness. The lenses were removed from the buffer, and radioactive counts were determined again. RESULTS: Extraction efficiencies for lysozyme from the artificial tear solution were 97.2% +/- 1.2% for ACUVUE 2, 64.3% +/- 6.2% for Focus NIGHT & DAY, 62.5% +/- 5.6% for O2 Optix, 53.5% +/- 5.8% for PureVision, and 89.2% +/- 3.4% for ACUVUE Advance. Results were similar for the lysozyme extracted after incubating in the simple lysozyme solution. CONCLUSIONS: TFA/ACN is extremely efficient at extracting lysozyme deposited on etafilcon lenses. However, it does not extract all the lysozyme deposited on silicone hydrogel lenses, and alternative extraction procedures should be sought.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".