Efficacy of an Extraction Solvent Used to Quantify Albumin Deposition on Hydrogel Contact Lens Materials
Bibliographic record
Abstract
OBJECTIVES: Extracting proteins from conventional hydrogel (CH) and silicone hydrogel (SH) contact lens materials using a mixture of trifluoroacetic acid/acetonitrile (TFA/ACN) is a well-established procedure for quantifying individual and total protein deposited on contact lenses. The purpose of this study was to determine the efficacy of TFA/ACN in extracting albumin from SH and a CH group IV lens material using an in vitro model. METHODS: One CH group IV lens material (etafilcon A) and five different SH lens materials (lotrafilcon A, lotrafilcon B, balafilcon A, galyfilcon A, and senofilcon A) were incubated in both simple albumin solution and a complex artificial tear protein solution containing 125I-labeled albumin. All the lens materials were incubated for 14 days at 37 degrees C with constant rotations. Following the incubation period, radioactive counts were determined and the lenses were placed in an appropriate volume of the extraction solvent. After the specified time, the lenses were removed and radioactive counts were determined again to calculate the amount of albumin remaining on the lenses post-extraction. RESULTS: Extraction efficiencies for albumin from the artificial tear protein solution were 97.2% +/- 2 for etafilcon A, 77.3% +/- 6.2 for lotrafilcon A, 73.5% +/- 5.6 for lotrafilcon B, 81.5% +/- 5.8 for balafilcon, 91.2% +/- 3.4 for galyfilcon A, and 89.2% +/- 3.4 for senofilcon A. Results were similar for the albumin extracted after incubating in the simple albumin solution. CONCLUSIONS: Although TFA/ACN is efficient at extracting albumin deposited on etafilcon lenses, it does not extract all the albumin that is deposited on SH lenses and alternative extraction procedures should be sought.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 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".