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Record W2053604203 · doi:10.1115/sbc2012-80204

A Microfabricated Platform to Measure and Manipulate the Mechanics of Engineered Cardiac Microtissues

2012· article· en· W2053604203 on OpenAlexaff
Thomas Boudou, Wesley R. Legant, Anbin Mu, Michael A. Borochin, Nimalan Thavandiran, Milica Radisic, Peter W. Zandstra, Jonathan A. Epstein, Kenneth B. Margulies, Christopher S. Chen

Bibliographic record

VenueASME 2012 Summer Bioengineering Conference, Parts A and B · 2012
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContractilityContraction (grammar)Rigidity (electromagnetism)Biomedical engineeringMaterials scienceMicroelectromechanical systemsStiffnessTissue engineeringMatrix (chemical analysis)Cardiac muscleNanotechnologyComputer scienceBiological systemEngineeringComposite materialCardiologyAnatomyBiology

Abstract

fetched live from OpenAlex

Cardiac tissue engineering is currently limited by the incapacity to test the wide range of parameters that might impact the engineered tissue in a high throughput and combinatorial manner. Here we used microelectromechanical systems (MEMS) technology to generate arrays of cardiac microtissues (CMTs) embedded within three-dimensional micropatterned matrices. Microcantilevers constrain CMT contraction and report generated forces. We demonstrate the ability to routinely produce ∼200 CMTs per million cardiac cells whose spontaneous contraction frequency, duration, and forces can be tracked. Independently varying the mechanical stiffness of the cantilevers and collagen matrix revealed that the CMT contractility increased with boundary or matrix rigidity. We also show that the combination of electrical stimulation and auxotonic load strongly improve both the structure and the function of the CMTs. Finally, we demonstrate the suitability of our technique for high throughput monitoring of drug-induced changes in spontaneous frequency or contractility in CMTs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.048
GPT teacher head0.260
Teacher spread0.213 · 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.

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

Citations11
Published2012
Admission routes1
Has abstractyes

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Same venueASME 2012 Summer Bioengineering Conference, Parts A and BSame topic3D Printing in Biomedical ResearchFrench-language works237,207