Silicone hydrogels grafted with natural amino acids for ophthalmological application
Bibliographic record
Abstract
In this report, protein repelling silicone hydrogels with improved hydrophilicity were prepared by photo-polymerization of silicone-containing monomer and glycidyl methacrylate followed by grafting zwitterionic amino acids. The grafted silicone hydrogels possessed excellent hydrophilic surfaces due to the enrichment of amino acids, which was confirmed by attenuated total reflectance Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, contact angle, and equilibrium water content measurements. Remarkable resistance to bovine serum albumin and lysozyme fouling was observed for the silicone hydrogels immobilized with neutrally charged amino acids because of the formation of zwitterionic surfaces with pairs of protonated secondary ammonium cations and deprotonated carboxyl anions. Meanwhile, the silicone hydrogels grafted with positively or negatively charged amino acids were able to repulse same charged protein with reduced deposition and attract oppositely charged protein with increased adsorption. Preliminary cytotoxicity test indicated that the zwitterionic silicone hydrogels were non-cytotoxic. Similarly, three types of natural amino acids, including serine, aspartic acid and histidine, modified silicone hydrogel contact lenses exhibited excellent hydrophilicity and non-damage to the rabbit's eyes, but only serine modified zwitterionic contact lens showed superior protein fouling resistance compared with the current commercial hydrogel contact lens, which may have great potential application in ophthalmology.
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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 | 0.000 | 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) | 0.001 | 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".