Analysis of Using I<sup>125</sup>Radiolabeling for Quantifying Protein on Contact Lenses
Bibliographic record
Abstract
PURPOSE: To investigate the accuracy of I(125) radiolabeling to quantitatively determine the deposition of protein onto various commercially available contact lens (CL) materials. METHODS: Commercially available silicone hydrogel and conventional hydrogel CL materials were examined for times ranging from 10 s to 1 week. Adsorption of free I(125) was measured directly for the CL. The use of dialyzing labeled proteins and/or using NaI to compete with free I(125) uptake was investigated as ways to minimize effects due to free I(125). RESULTS: At all time points and with all lens materials, there was 0.3 μg/lens or greater of apparent mass attributable to free I(125) uptake. Dialyzing labeled proteins significantly reduced free I(125) uptake for all materials investigated. The benefit of using dialyzed protein was most prominent at shorter times, as free I(125) is continuously generated over time. Using NaI can reduce free I(125) uptake for some lens materials, but this is shown to directly affect protein deposition on some materials. CONCLUSIONS: Periodic replenishment of incubation solutions with freshly dialyzed labeled protein to limit free I(125) generation is recommended, but the incorporation of NaI onto the buffer solution is not. Irrespective of the exact procedure to limit free I(125) uptake, extra steps must be performed to quantify the amount of I(125) adsorbed onto contact lens materials, to determine thresholds of confidence with respect to the actual protein deposition that occurs.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".