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Record W1965462419 · doi:10.1002/jbm.a.31910

Surface modification of collagen‐based artificial cornea for reduced endothelialization

2008· article· en· W1965462419 on OpenAlexaff
Mehrdad Rafat, Takeshi Matsuura, Fengfu Li, May Griffith

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2008
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceSurface modificationCorneaGraftingBiomedical engineeringAdhesionContact angleSelf-healing hydrogelsSwellingCell adhesionFourier transform infrared spectroscopyBiophysicsComposite materialChemical engineeringPolymer chemistryOphthalmologyMedicine

Abstract

fetched live from OpenAlex

Our objective was to develop collagen-based hydrogels as tissue substitutes for corneal transplantation. The design of the full-thickness corneal grafts includes prevention of cell migration onto the posterior surface of the implants, using a plasma-assisted surface modification technique. Briefly, the hydrogel materials were subjected to ammonia plasma functionalization followed by grafting of alginate macromolecules to the target surface. The treated materials surfaces showed observable decreases in endothelial cell attachment. The decrease in cell attachment and adhesion was dependant upon the concentration of alginate and plasma radio frequency (RF) power. High concentrations of alginate 5% (w/v) and high RF power of 100 W produced surfaces with minimal cell attachment. The plasma-alginate treatment did not adversely affect the optical or swelling properties of the constructs. Contact angle measurement analysis revealed that the posterior surface hydrophilicity significantly increased after the treatment. The grafting of alginate to the implants surfaces was confirmed by fourier transform infrared spectroscopy. Both of the untreated and alginate grafted corneal materials were found to be superior to human cornea in optical and swelling properties.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.272
GPT teacher head0.434
Teacher spread0.163 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2008
Admission routes1
Has abstractyes

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