Localization of Lysozyme Sorption to Conventional and Silicone Hydrogel Contact Lenses Using Confocal Microscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To investigate the distribution profile of hen egg lysozyme (HEL) through poly-2-hydroxyethyl methacrylate (pHEMA)-based lens materials and silicone hydrogel (SH) lens materials using confocal laser scanning microscopy (CLSM). METHODS: Five silicone SH materials (balafilcon A, lotrafilcon A, lotrafilcon B, galyfilcon A, senofilcon A) and four pHEMA-based materials (alphafilcon A, etafilcon A, omafilcon A, vifilcon A) were incubated in 1.9 mg/ml protein solution for 24 hours. The protein solution consisted of HEL, which was conjugated with either fluorescein isothiocyanate (FITC) or lucifer yellow VS dilithium salt (LY). CLSM (Zeiss LSM 510 META) identified the location of the fluorescently labeled protein by using 1 micro m depth scans through the lens. In a second experiment, lenses were incubated with 2% (125) I labeled HEL to determine the amount of deposited protein on each lens. Both techniques were combined to describe the individual HEL profiles. RESULTS: After the incubation in fluorescently labeled HEL, all pHEMA-based materials and the SH material balafilcon A accumulated protein throughout the entire lens material, while, for the SH lenses lotrafilcon A and lotrafilcon B, HEL was primarily detected on the lens surface alone. Differences in protein uptake pattern due solely to the two conjugated dyes were most apparent for the SH materials galyfilcon A and senofilcon A; HEL was detected throughout these lenses when conjugated with LY but accumulated primarily on the surface when conjugated with FITC. CONCLUSION: CLSM in combination with a radiolabel technique can describe both the location and degree of protein deposition on different contact lens materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it