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Record W4404853506 · doi:10.1242/dev.204455

Bioengineering vascularization

2024· review· en· W4404853506 on OpenAlexafffund
Shira Landau, Sargol Okhovatian, Yimu Zhao, Chuan Liu, Amid Shakeri, Ying Wang, Kaitlyn Ramsay, Jennifer Kieda, Richard Jiang, Milica Radisic

Bibliographic record

VenueDevelopment · 2024
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiofabricationRegenerative medicineBiologyTissue engineeringOrgan-on-a-chipMicrofluidicsNanotechnologyCell biologyStem cellMaterials science

Abstract

fetched live from OpenAlex

This Review explores the rapidly evolving field of bioengineered vasculature, a key area of focus in tissue engineering and regenerative medicine. The broad relevance of this topic is attributed to its impacts on a wide range of biological processes, enabling studies in tissue development, fundamental biology and drug discovery, and the applications in tissue engineering and regenerative medicine. We outline the design criteria for bioengineered vasculature and the methodologies for constructing these systems by self-assembly and in microfluidics, organs-on-a-chip and macroscale tubular systems that often rely on biofabrication approaches such as 3D printing. We discuss existing challenges in developing functional vasculature that closely mirrors its native equivalent, including achieving hierarchical branching with organ and vessel-specific endothelial and supporting cells, providing perusable vasculature within organoids and scaling the systems for implantation and direct vascular anastomosis.

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 categoriesInsufficient 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 score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.052
GPT teacher head0.329
Teacher spread0.277 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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