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Record W2955255437 · doi:10.1002/cjce.23588

Graphene functionalized decellularized scaffold promotes skin cell proliferation

2019· article· en· W2955255437 on OpenAlexvenueno aff
Mahboubeh Jafarkhani, Zeinab Salehi, Zahra Bagheri, Zahra Aayanifard, Ali Rezvan, Hamid Doosthosseini, Mohammad Ali Shokrgozar

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDecellularizationBiocompatibilityScaffoldTissue engineeringMaterials scienceGrapheneViability assayBiomedical engineeringNanotechnologyExtracellular matrixCell adhesionAdhesionCellChemistryComposite materialMedicine

Abstract

fetched live from OpenAlex

An increasing number of new strategies for skin tissue engineering have been developed with the potential to mimic the biological properties of native tissue with a high degree of complexity, flexibility, and reproducibility. In this study, decellularized tissue (DT) was prepared from the bovine heart by using chemical treatments. However, the mechanical properties of the DT constructs were poorer than the extra cellular matrix of the skin tissue. To overcome this challenge, hybrid scaffolds of DT and graphene oxide (GO) were developed and the effects of the GO concentration on the morphology, pore size, porosity, mechanical strength, and water uptake capacity of the samples were evaluated. Moreover, the biocompatibility of hybrid scaffolds was studied by Live/Dead staining. The results show that a hybrid scaffold incorporating 3 % graphene oxide improved the mechanical strength and cell viability by ~25 % in comparison to the DT scaffolds. Cell viability results confirmed that the porous scaffolds could support cell adhesion, proliferation, and cell activity for 7 days. This study provides new insight into and opportunities for using graphene‐based materials to develop biomimetic constructs for clinical applications.

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 score0.003
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.159
Teacher spread0.154 · 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

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