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Record W4378231863 · doi:10.3389/fbioe.2023.1161804

Optimization of 3D printing and in vitro characterization of alginate/gelatin lattice and angular scaffolds for potential cardiac tissue engineering

2023· article· en· W4378231863 on OpenAlexafffund
Farinaz Ketabat, Titouan Maris, Xiaoman Duan, Zahra Yazdanpanah, Michael Kelly, Ildikó Badea, Daniel Chen

Bibliographic record

VenueFrontiers in Bioengineering and Biotechnology · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of SaskatchewanNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research FoundationHeart and Stroke Foundation of Canada
KeywordsGelatinTissue engineeringCharacterization (materials science)Biomedical engineering3D printingMaterials scienceIn vitroNanotechnologyChemistryChemical engineeringComposite materialEngineeringOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Background: Engineering cardiac tissue that mimics the hierarchical structure of cardiac tissue remains challenging, raising the need for developing novel methods capable of creating structures with high complexity. Three-dimensional (3D)-printing techniques are among promising methods for engineering complex tissue constructs with high precision. By means of 3D printing, this study aims to develop cardiac constructs with a novel angular structure mimicking cardiac architecture from alginate (Alg) and gelatin (Gel) composite. The 3D-printing conditions were optimized and the structures were characterized in vitro , with human umbilical vein endothelial cells (HUVECs) and cardiomyocytes (H9c2 cells), for potential cardiac tissue engineering. Methods: We synthesized the composites of Alg and Gel with varying concentrations and examined their cytotoxicity with both H9c2 cells and HUVECs, as well as their printability for creating 3D structures of varying fibre orientations (angular design). The 3D-printed structures were characterized in terms of morphology by both scanning electron microscopy (SEM) and synchrotron radiation propagation-based imaging computed tomography (SR-PBI-CT), and elastic modulus, swelling percentage, and mass loss percentage as well. The cell viability studies were conducted via measuring the metabolic activity of the live cells with MTT assay and visualizing the cells with live/dead assay kit. Results: Among the examined composite groups of Alg and Gel, two combinations with ratios of 2 to 1 and 3 to 1 (termed as Alg2Gel1 and Alg3Gel1) showed the highest cell survival; they accordingly were used to fabricate two different structures: a novel angular and a conventional lattice structure. Scaffolds made of Alg3Gel1 showed higher elastic modulus, lower swelling percentage, less mass loss, and higher cell survival compared to that of Alg2Gel1. Although the viability of H9c2 cells and HUVECs on all scaffolds composed of Alg3Gel1 was above 99%, the group of the constructs with the angular design maintained significantly more viable cells compared to other investigated groups. Conclusion: The group of angular 3D-ptinted constructs has illustrated promising properties for cardiac tissue engineering by providing high cell viability for both endothelial and cardiac cells, high mechanical strength as well as appropriate swelling, and degradation properties during 21 days of incubation. Statement of Significance: 3D-printing is an emerging method to create complex constructs with high precision in a large scale. In this study, we have demonstrated that 3D-printing can be used to create compatible constructs from the composite of Alg and Gel with endothelial cells and cardiac cells. Also, we have demonstrated that these constructs are able to enhance the viability of cardiac and endothelial cells via creating a 3D structure mimicking the alignment and orientation of the fibers in the native heart.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.212
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2023
Admission routes2
Has abstractyes

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