A Review of Techniques to Measure Protein Sorption to Soft Contact Lenses
Bibliographic record
Abstract
PURPOSE: To compare and critically evaluate a variety of techniques to measure the quantity and biological activity of protein sorption to contact lenses over short time periods. METHODS: A literature review was undertaken investigating the major techniques to measure protein sorption to soft contact lens materials, with specific reference to measuring protein directly on lenses using in situ, ex situ, protein structural, and biological activity techniques. RESULTS: The use of in situ techniques to measure protein quantity provides excellent sensitivity, but many are not directly applicable to contact lenses. Many ex situ techniques struggle to measure all sorbed proteins, and these measurements can have significant signal interference from the lens materials themselves. Techniques measuring the secondary and tertiary structures of sorbed proteins have exhibited only limited success. CONCLUSIONS: There are a wide variety of techniques to measure both the amount of protein and the biological activity of protein sorbed to soft contact lens materials. To measure the mass of protein sorbed to soft contact lenses (not just thin films) over short time periods, the method of choice should be I radiolabeling. This technique is sensitive enough to measure small amounts of deposited protein, provided steps are taken to limit and measure any interaction of the iodine tracer with the materials. To measure the protein activity over short time periods, the method of choice should be to measure the biological function of sorbed proteins. This may require new methods or adaptations of existing ones.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".