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Record W2663837402 · doi:10.1042/cs20170055

Biophysical stimulation for <i>in vitro</i> engineering of functional cardiac tissues

2017· review· en· W2663837402 on OpenAlex

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

VenueClinical Science · 2017
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsHeart and Stroke FoundationUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsIn vivoTissue engineeringIn vitroFunction (biology)Drug discoveryNeuroscienceStimulationComputational biologyBiologyComputer scienceBiomedical engineeringCell biologyMedicineBioinformaticsBiotechnology

Abstract

fetched live from OpenAlex

Engineering functional cardiac tissues remains an ongoing significant challenge due to the complexity of the native environment. However, our growing understanding of key parameters of the in vivo cardiac microenvironment and our ability to replicate those parameters in vitro are resulting in the development of increasingly sophisticated models of engineered cardiac tissues (ECT). This review examines some of the most relevant parameters that may be applied in culture leading to higher fidelity cardiac tissue models. These include the biochemical composition of culture media and cardiac lineage specification, co-culture conditions, electrical and mechanical stimulation, and the application of hydrogels, various biomaterials, and scaffolds. The review will also summarize some of the recent functional human tissue models that have been developed for in vivo and in vitro applications. Ultimately, the creation of sophisticated ECT that replicate native structure and function will be instrumental in advancing cell-based therapeutics and in providing advanced models for drug discovery and testing.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.213
GPT teacher head0.477
Teacher spread0.264 · 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