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Record W2949160127 · doi:10.3389/fbioe.2019.00159

Ultraviolet Functionalization of Electrospun Scaffolds to Activate Fibrous Runways for Targeting Cell Adhesion

2019· article· en· W2949160127 on OpenAlexfundno aff
André F. Girão, Paul Wieringa, Susana C. Pinto, Paula A. A. P. Marques, Silvestro Micera, Richard van Wezel, Maqsood Ahmed, Roman Truckenmueller, Lorenzo Moroni

Bibliographic record

VenueFrontiers in Bioengineering and Biotechnology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersFP7 Nanosciences, Nanotechnologies, Materials and new Production TechnologiesFundação para a Ciência e a TecnologiaEuropean CommissionNatural Sciences and Engineering Research Council of CanadaEuropean Regional Development FundPrograma Operacional Regional do Centro
KeywordsSurface modificationCell adhesionMaterials scienceTissue engineeringAdhesionNanofiberElectrospinningScaffoldNanotechnologyBiomoleculePolymerChemistryBiomedical engineeringComposite material

Abstract

fetched live from OpenAlex

. In this work, we report the adaptation of a standard sterilization methodology-UV irradiation-for patterning the surfaces of two complementary polymeric electrospun scaffolds with oxygen cues able to efficiently immobilize biomolecules. Independently of the different polymer chain length of poly(ethylene oxide terephthalate)/poly(butylene terephthalate) (PEOT/PBT) copolymers and PEOT/PBT ratio, it was possible to easily functionalize specific regions of the scaffolds by inducing an optimized and spatially controlled adsorption of proteins capable of boosting the adhesion and spreading of cells along the activated fibrous runways. By allowing an efficient design of cell attachment patterns without inducing any noticeable change on cell morphology nor on the integrity of the electrospun fibers, this procedure offers an affordable and resourceful approach to generate complex biochemical patterns that can decisively complement the functionality of the next generation of tissue engineering scaffolds.

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 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 score0.212
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

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