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Record W2419131817 · doi:10.1007/978-1-59745-443-8_16

Practical Aspects of Cardiac Tissue Engineering With Electrical Stimulation

2007· article· en· W2419131817 on OpenAlex
Christopher Cannizzaro, Nina Tandon, Elisa Figallo, Hyoungshin Park, Sharon Gerecht, Milica Radisic, Nicola Elvassore, Gordana Vunjak‐Novakovic

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.

Bibliographic record

VenueMethods in molecular medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsEconomic shortageStimulationTissue engineeringNeuroscienceTransplantationBiological tissueMedicineBiomedical engineeringBiologyInternal medicine

Abstract

fetched live from OpenAlex

Heart disease is a leading cause of death in western society. Despite the success of heart transplantation, a chronic shortage of donor organs, along with the associated immunological complications of this approach, demands that alternative treatments be found. One such option is to repair, rather than replace, the heart with engineered cardiac tissue. Multiple studies have shown that to attain functional tissue, assembly signaling cues must be recapitulated in vitro. In their native environment, cardiomyocytes are directed to beat in synchrony by propagation of pacing current through the tissue. Recently, we have shown that electrical stimulation directs neonatal cardiomyocytes to assemble into native-like tissue in vitro. This chapter provides detailed methods we have employed in taking this "biomimetic" approach. After an initial discussion on how electric field stimulation can influence cell behavior, we examine the practical aspects of cardiac tissue engineering with electrical stimulation, such as electrode selection and cell seeding protocols, and conclude with what we feel are the remaining challenges to be overcome.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.254
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.024
GPT teacher head0.407
Teacher spread0.382 · 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