Evidence map›Paper›PMID 31297148›Full record

ReviewJournal of biological engineering2019

Cardiac tissue engineering: state-of-the-art methods and outlook.

Anh H Nguyen, Paul Marsh, Lauren Schmiess-Heine, Peter J Burke, Abraham Lee, Juhyun Lee, Hung Cao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of biological engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 1 synthesis or guideline pooled it.

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2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anh H Nguyen *1Electrical and Computer Engineering Department, University of Alberta, Edmonton, Alberta Canada.
Paul Marsh *2Electrical Engineering and Computer Science Department, University of California Irvine, Irvine, CA USA.
Lauren Schmiess-Heine2Electrical Engineering and Computer Science Department, University of California Irvine, Irvine, CA USA.
Peter J Burke2Electrical Engineering and Computer Science Department, University of California Irvine, Irvine, CA USA.
Abraham Lee3Biomedical Engineering Department, University of California Irvine, Irvine, CA USA.
Juhyun Lee6Bioengineering Department, University of Texas at Arlington, Arlington, TX USA.
Hung Cao2Electrical Engineering and Computer Science Department, University of California Irvine, Irvine, CA USA.ORCID 0000-0003-4197-7208

Funding

Real-time Monitoring of Zebrafish ECG with Automated Aberrant Pattern DetectionR41OD024874 · OD · SENSORIIS, INC. · PI CAO, HUNG, LAU, MICHAEL P.H. · 2018 to 2018
$224k
NIH HHS R41 OD024874
6 · The paper itself

Abstract

The purpose of this review is to assess the state-of-the-art fabrication methods, advances in genome editing, and the use of machine learning to shape the prospective growth in cardiac tissue engineering. Those interdisciplinary emerging innovations would move forward basic research in this field and their clinical applications. The long-entrenched challenges in this field could be addressed by novel 3-dimensional (3D) scaffold substrates for cardiomyocyte (CM) growth and maturation. Stem cell-based therapy through genome editing techniques can repair gene mutation, control better maturation of CMs or even reveal its molecular clock. Finally, machine learning and precision control for improvements of the construct fabrication process and optimization in tissue-specific clonal selections with an outlook of cardiac tissue engineering are also presented.

Indexed as

3D scaffoldsCardiac tissue engineeringCRISPR/Cas9 systemsMachine learning

Identifiers

PMID31297148
PMCPMC6599291

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.