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Record W2504698253 · doi:10.1021/acsami.6b07433

“Click” Chemistry-Tethered Hyaluronic Acid-Based Contact Lens Coatings Improve Lens Wettability and Lower Protein Adsorption

2016· article· en· W2504698253 on OpenAlexafffund
Xudong Deng, Myrto Korogiannaki, Banafsheh Rastegari, Jianfeng Zhang, Mengsu Chen, Qiang Fu, Heather Sheardown, Carlos D. M. Filipe, Todd Hoare

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationOntario Innovation Trust
KeywordsMaterials scienceWettingContact lensContact angleAdsorptionMethacrylateCovalent bondClick chemistryProtein adsorptionChemical engineeringCoatingHyaluronic acidNanotechnologyPolymerPolymer chemistryOrganic chemistryChemistryComposite materialOpticsCopolymer

Abstract

fetched live from OpenAlex

Improving the wettability of and reducing the protein adsorption to contact lenses may be beneficial for improving wearer comfort. Herein, we describe a simple "click" chemistry approach to surface functionalize poly(2-hydroxyethyl methacrylate) (pHEMA)-based contact lenses with hyaluronic acid (HA), a carbohydrate naturally contributing to the wettability of the native tear film. A two-step preparation technique consisting of laccase/TEMPO-mediated oxidation followed by covalent grafting of hydrazide-functionalized HA via simple immersion resulted in a model lens surface that is significantly more wettable, more water retentive, and less protein binding than unmodified pHEMA while maintaining the favorable transparency, refractive, and mechanical properties of a native lens. The dipping/coating method we developed to covalently tether the HA wetting agent is simple, readily scalable, and a highly efficient route for contact lens modification.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.231
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations72
Published2016
Admission routes2
Has abstractyes

Explore more

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