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Record W2739291737 · doi:10.1089/ten.tec.2017.0222

Bioprinting Pattern-Dependent Electrical/Mechanical Behavior of Cardiac Alginate Implants: Characterization and <i>Ex Vivo</i> Phase-Contrast Microtomography Assessment

2017· article· en· W2739291737 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTissue Engineering Part C Methods · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsBiomedical engineeringMaterials sciencePorosityEx vivoX-ray microtomographyElastic modulusImplantModulusComposite materialIn vivoMedicineSurgeryRadiology

Abstract

fetched live from OpenAlex

Three-dimensional (3D)-bioprinting techniques may be used to modulate electrical/mechanical properties and porosity of hydrogel constructs for fabrication of suitable cardiac implants. Notably, characterization of these properties after implantation remains a challenge, raising the need for the development of novel quantitative imaging techniques for monitoring hydrogel implant behavior in situ. This study aims at (i) assessing the influence of hydrogel bioprinting patterns on electrical/mechanical behavior of cardiac implants based on a 3D-printing technique and (ii) investigating the potential of synchrotron X-ray phase-contrast imaging computed tomography (PCI-CT) for estimating elastic modulus/impedance/porosity and microstructural features of 3D-printed cardiac implants in situ via an ex vivo study. Alginate laden with human coronary artery endothelial cells was bioprinted layer by layer, forming cardiac constructs with varying architectures. The elastic modulus, impedance, porosity, and other structural features, along with the cell viability and degradation of printed implants were examined in vitro over 25 days. Two selected cardiac constructs were surgically implanted onto the myocardium of rats and 10 days later, the rat hearts with implants were imaged ex vivo by means of PCI-CT at varying X-ray energies and CT-scan times. The elastic modulus/impedance, porosity, and structural features of the implant were inferred from the PCI-CT images by using statistical models and compared with measured values. The printing patterns had significant effects on implant porosity, elastic modulus, and impedance. A particular 3D-printing pattern with an interstrand distance of 900 μm and strand alignment angle of 0/45/90/135° provided relatively higher stiffness and electrical conductivity with a suitable porosity, maintaining high cell viability over 7 days. The X-ray photon energy of 30-33 keV utilizing a CT-scan time of 1-1.2 h resulted in a low-dose PCI-CT, which provided a good visibility of the low-X-ray absorbent alginate implants. After 10 days postimplantation, the PCI-CT provided a reasonably accurate estimation of implant strand thickness and alignment, pore size and interconnectivity, porosity, elastic modulus, and impedance, which were consistent with our measurements. Findings from this study suggest that 3D-printing patterns can be used to modulate electrical/mechanical behavior of alginate implants, and PCI-CT can be potentially used as a 3D quantitative imaging tool for assessing structural and electrical/mechanical behavior of hydrogel cardiac implants in small animal models.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.370
Teacher spread0.349 · 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