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Record W2814892626 · doi:10.1161/circresaha.118.311213

Can We Engineer a Human Cardiac Patch for Therapy?

2018· review· en· W2814892626 on OpenAlexaff
Jianyi Zhang, Wuqiang Zhu, Milica Radisic, Gordana Vunjak‐Novakovic

Bibliographic record

VenueCirculation Research · 2018
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Heart, Lung, and Blood Institute
KeywordsMedicineIntensive care medicineCardiology

Abstract

fetched live from OpenAlex

Some of the most significant leaps in the history of modern civilization-the development of article in China, the steam engine, which led to the European industrial revolution, and the era of computers-have occurred when science converged with engineering. Recently, the convergence of human pluripotent stem cell technology with biomaterials and bioengineering have launched a new medical innovation: functional human engineered tissue, which promises to revolutionize the treatment of failing organs including most critically, the heart. This compendium covers recent, state-of-the-art developments in the fields of cardiovascular tissue engineering, as well as the needs and challenges associated with the clinical use of these technologies. We have not attempted to provide an exhaustive review in stem cell biology and cardiac cell therapy; many other important and influential reports are certainly merit but already been discussed in several recent reviews. Our scope is limited to the engineered tissues that have been fabricated to repair or replace components of the heart (eg, valves, vessels, contractile tissue) that have been functionally compromised by diseases or developmental abnormalities. In particular, we have focused on using an engineered myocardial tissue to mitigate deficiencies in contractile function.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.314
GPT teacher head0.494
Teacher spread0.180 · 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 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

Citations163
Published2018
Admission routes1
Has abstractyes

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