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Record W2326932500 · doi:10.1166/jbn.2013.1622

Effects of Electrospun Nanostructure versus Microstructure on Human Aortic Endothelial Cell Behavior

2013· article· en· W2326932500 on OpenAlexaff
Afra Hadjizadeh, Abdellah Ajji, Benoît Liberelle, Grégory De Crescenzo

Bibliographic record

VenueJournal of Biomedical Nanotechnology · 2013
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceFiberComposite materialMorphology (biology)Polyethylene terephthalateScanning electron microscopeMicrostructureAdhesionElectrospinningMicrofiberBiomedical engineeringBiophysicsPolymerMedicine

Abstract

fetched live from OpenAlex

This study examines the effect of electrospun polyethylene terephthalate mats fiber diameter, orientation, and surface properties on the Human Aortic Endothelial Cell behavior. Mats with two different average fiber diameters (740 +/- 200 nm and 1.8 +/- 0.2 microm); orientations (low and high); NaOH-treated and untreated were prepared. NaOH treatment altered mats physical properties. AlamarBlue assay revealed that all four test mats supported cell adhesion and growth. Cell growth was observed to be faster for mat with large fiber diameter than for the small fiber diameter mat. Fluorescent staining and scanning electron microscopy showed that fiber diameter and orientation influenced cell morphology. Cells were randomly spread on the 740-nm diameter fibers whereas most of them were oriented along the fibers with 1.8 microm diameter. Mat with higher fiber alignment showed higher cell orientation. Cells penetrated into the mats having 1.8 +/- 0.2 microm fiber diameter but remained on the surface of the mat with 740 +/- 200 nm, as determined from histological analysis. These findings highly suggest that the two mats may be potential materials to construct a two layer vascular graft scaffold in which the mat with small diameter fibers forms the luminal surface and the mat with larger fiber diameter the abluminal surface.

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 score0.013
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.001
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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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