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Record W4386213068 · doi:10.1021/acs.biomac.3c00387

Elastomeric Polyesters in Cardiovascular Tissue Engineering and Organs-on-a-Chip

2023· review· en· W4386213068 on OpenAlexafffund
Sargol Okhovatian, Amid Shakeri, Locke Davenport Huyer, Milica Radisic

Bibliographic record

VenueBiomacromolecules · 2023
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsDalhousie UniversityToronto General HospitalUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchFulbright CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPolyesterElastomerTissue engineeringMaterials scienceBiocompatible materialNanotechnologyElectrospinningBiomedical engineeringPolymerComposite materialEngineering

Abstract

fetched live from OpenAlex

Cardiovascular tissue constructs provide unique design requirements due to their functional responses to substrate mechanical properties and cyclic stretching behavior of cardiac tissue that requires the use of durable elastic materials. Given the diversity of polyester synthesis approaches, an opportunity exists to develop a new class of biocompatible, elastic, and immunomodulatory cardiovascular polymers. Furthermore, elastomeric polyester materials have the capability to provide tailored biomechanical synergy with native tissue and hence reduce inflammatory response in vivo and better support tissue maturation in vitro . In this review, we highlight underlying chemistry and design strategies of polyester elastomers optimized for cardiac tissue scaffolds. The major advantages of these materials such as their tunable elasticity, desirable biodegradation, and potential for incorporation of bioactive compounds are further expanded. Unique fabrication methods using polyester materials such as micromolding, 3D stamping, electrospinning, laser ablation, and 3D printing are discussed. Moreover, applications of these biomaterials in cardiovascular organ-on-a-chip devices and patches are analyzed. Finally, we outline unaddressed challenges in the field that need further study to enable the impactful translation of soft polyesters to 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.290
Teacher spread0.268 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2023
Admission routes2
Has abstractyes

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