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Record W2977414689 · doi:10.1002/app.48510

A simple and effective approach to produce tubular polysaccharide‐based hydrogel scaffolds

2019· article· en· W2977414689 on OpenAlexafffund
Fernanda Carla Bombaldi de Souza, Dimitria Bonizol Camasão, Renata Francielle Bombaldi de Souza, Bernard Drouin, Diego Mantovani, Ângela Maria Moraes

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceFabricationTissue engineeringScaffoldPolymerPorosityCastingChitosanNanotechnologyChemical engineeringBiomedical engineeringComposite material

Abstract

fetched live from OpenAlex

ABSTRACT The production of porous tubular scaffolds is of great interest in the field of tissue engineering, given the existence of several tubular structures in the human body. In this work, a methodology was developed for the fabrication of tubular‐shaped scaffolds based on the casting of polymeric solutions by controlled crosslinking mediated by a semipermeable cast. The fabrication of hydrogel tubular scaffolds from chitosan–pectin polymeric mixtures (tCh‐P, 3% w/v) was performed to attest the feasibility of the technique. Tubular structures with about 4.15 mm internal diameter and 1.55 mm wall thickness were produced. The structures are highly porous, presenting interconnected pores with average diameter of about 360 μm. Seeding of human smooth muscle cells on the material was successfully achieved by using collagen gel to facilitate cell migration and retention inside the structure of the scaffold. The methodology herein proposed was successfully validated for the production of tubular constructs, opening new perspectives for the fabrication of matrices based on polymers that are passive of crosslinking with small molecules. Besides being an interesting approach to produce tubular scaffolds, this methodology can be considered an useful platform to obtain materials for drug screening and diagnostic studies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137 , 48510.

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)0.000
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 score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.233
Teacher spread0.229 · 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

Citations31
Published2019
Admission routes2
Has abstractyes

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