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Record W3035495218 · doi:10.1002/adhm.202000358

z‐Wire: A Microscaffold That Supports Guided Tissue Assembly and Intramyocardium Delivery for Cardiac Repair

2020· article· en· W3035495218 on OpenAlexafffund
Luis Eduardo Portillo‐Esquivel, Vibudha Nanduri, Feng Zhang, Wenbin Liang, Boyang Zhang

Bibliographic record

VenueAdvanced Healthcare Materials · 2020
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of OttawaMcMaster University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsScaffoldTissue engineeringBiomedical engineeringModular designMaterials scienceMyocyteNanotechnologyComputer scienceCell biologyMedicineBiology

Abstract

fetched live from OpenAlex

Tissue engineering holds promise to replace damaged tissues for repair of vital organs in the human body. In cardiac repairs specifically, approaches are developed for intramyocardial delivery of cells and the epicardial delivery of tissue-engineered cardiac patches, providing benefit of cell localization and tissue structure, respectively. However, to improve cell retention and integration, there is a need for the intramyocardial delivery of functional tissues while preserving anisotropic muscle alignment. Here, a biodegradable z-wire scaffold that supports the scalable gel-free production of an array of functional cardiac tissues in a 384-well plate format is developed. The z-wire scaffold design supports cellular alignment, provides tunable mechanical support, and allows for tissue contraction. When the scaffold is imparted with magnetic properties, individual tissues can be assembled with macroscopic alignment under magnetic guidance. When used in combination with a customized surgical delivery tool, z-wire tissues can be injected directly into the myocardial wall, with controlled tissue orientation according to the injection path. This modular tissue engineering approach, in combination with the use of smart scaffolds, can expand opportunity in functional tissue delivery.

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)
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 score0.219
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
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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 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

Citations4
Published2020
Admission routes2
Has abstractyes

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