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Record W2113241079 · doi:10.1002/jbm.b.33285

Novel genipin‐collagen immobilization of polylactic acid (PLA) fibers for use as tissue engineering scaffolds

2014· article· en· W2113241079 on OpenAlexaff
Nisarg Tambe, Jin Di, Ze Zhang, Susan H. Bernacki, Ahmed El‐Shafei, Martin W. King

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité Laval
FundersNational Science Foundation
KeywordsGenipinTissue engineeringSurface modificationAdhesionPolylactic acidCell adhesionChemistryExtracellular matrixBiocompatible materialNanotechnologyMaterials scienceBiomedical engineeringBiochemistryPolymerChitosanOrganic chemistry

Abstract

fetched live from OpenAlex

The material surface plays an important role in the case of biomaterials used as tissue engineering scaffolds. On exposure to a biological environment, extra cellular matrix (ECM) proteins are adsorbed non-specifically onto the surface and cells interact indirectly with the surface through the adsorbed proteins. Most synthetic polymeric biomaterials lack the desirable surface properties for cells as well as have poor cellular adhesion due to their hydrophobic nature. The main objective of this study was to harness surface functionalization technologies to fabricate scaffolds that would be biocompatible and support the adhesion and proliferation of fibroblast cells. The collagen was immobilized on the surface of functionalized PLA via a novel natural cross-linking molecule genipin which resulted in improved cell proliferation of human dermal fibroblasts as compared to the PLA surface coated with collagen without genipin. It is believed that genipin helps reduce steric problems between the functional groups and large protein molecules, and enables immobilized peptide to move more freely in a biological environment.

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)0.010
metaresearch head score (Gemma)0.002
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 score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.338
Teacher spread0.298 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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