Investigating Molecular Interactions between Mucin and Contact Lens Thin Films Using Nuclear Magnetic Resonance
Bibliographic record
Abstract
Poly(2-hydroxyethyl methacrylate) (PHEMA) and polyvinylpyrrolidone (PVP) are common polymers used in contact lens materials. In these systems, PHEMA forms the hydrogel network and PVP is used as a wetting agent. These lens materials, like other implantable devices, suffer from interactions with biological macromolecules that ultimately foul the surface of the lens. Despite the widespread adoption of contact lenses, the mechanisms of protein adsorption onto lenses are not well understood. To investigate the interactions responsible for fouling on contact lens materials, we developed a spin-coating apparatus to deposit thin polymer films onto NMR tube surfaces. We then used direct saturation compensated (DISCO) NMR on PHEMA-coated NMR tubes to investigate mucin binding at the surface of the coatings and the effect of varying PVP concentrations. Using this technique, this study was able to characterize the binding profiles of PHEMA-only and PHEMA+PVP hydrogels. The results show that the mucin-binding profile of PHEMA evolves as the PVP content increases. In the absence of PVP, the protons in the PHEMA backbone interact with mucin. However, when PHEMA and PVP form a hydrogel network, the protons on the PHEMA side chain become mucoadhesive in addition to the backbone. When the PVP content is further increased, only the side chain protons retain their interaction. Interestingly, the NMR spectra of PHEMA+PVP hydrogels suggest that PVP preferentially accumulates within the PHEMA matrix, with relatively little presenting at the surface of the hydrogels. In conjunction, SEM imaging of the PHEMA and PHEMA+PVP hydrogels shows that an increase in porosity accompanies the change in mucin-binding behavior, indicating that the change in morphology is correlated to the change in mucin-binding behavior rather than solely the change in composition. Overall, this study develops a tool to study the interactions between macromolecules and polymeric surfaces with atomic precision, uncovering structure–activity relationships that govern mucoadhesion of polymeric hydrogels.
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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 | 0.000 | 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 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".