Evidence map›Paper›PMID 34931483›Full record

ReviewESC heart failure2022

Models of cardiovascular surgery biobanking to facilitate translational research and precision medicine.

YingYan Zhu, Dan Jackson, Benjamin Hunter, Lorna Beattie, Lisa Turner, Brett D Hambly, Richmond W Jeremy, Cassandra Malecki, Elizabeth N Robertson, Amy Li and 6 more

Open access · goldAbstract readReview
In one paragraph

Review in ESC heart failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.6field-weighted citation impact, top 24% of its field
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

10 citing papers in PubMed, 16 citations in OpenAlex.

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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

16 authors at 4 institutions in 2 countries.

YingYan ZhuDepartment of Cardiothoracic Surgery, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
Dan JacksonDepartment of Cardiothoracic Surgery, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
Benjamin HunterCardiovascular Precision Laboratory, The University of Sydney, Sydney, New South Wales, 2006, Australia.
Lorna BeattieDepartment of Cardiothoracic Surgery, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
Lisa TurnerDepartment of Cardiothoracic Surgery, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
Brett D HamblyCharles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Richmond W JeremyFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Cassandra MaleckiFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Elizabeth N RobertsonFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Amy LiFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Cris Dos RemediosFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
David RichmondFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Christopher SemsarianFaculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
John F O'SullivanCardiovascular Precision Laboratory, The University of Sydney, Sydney, New South Wales, 2006, Australia.
Paul G BannonDepartment of Cardiothoracic Surgery, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
Sean LalCardiovascular Precision Laboratory, The University of Sydney, Sydney, New South Wales, 2006, Australia.
Royal Prince Alfred Hospital · AUThe University of Sydney · AUCentenary Institute · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biobanking in health care has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi-organizational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilize cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease relies primarily on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e. myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease. In this review, we describe a model for cardiovascular tissue biobanking and databasing, and its potential to facilitate basic and translational research. We share techniques to procure endocardial samples from patients with hypertrophic cardiomyopathy, heart failure with reduced ejection fraction, and heart failure with preserved ejection fraction, in addition to aortic disease samples. We discuss some of the issues with respect to data collection, privacy, biobank consent, and the governance of tissue biobanking. The development of tissue biobanks as described here has significant scope to improve and facilitate translational research in multi-omic fields such as genomics, transcriptomics, proteomics, and metabolomics. This research heralds an era of precision medicine, in which patients with cardiovascular pathology can be provided with optimized and personalized medical care for the treatment of their individual phenotype.

Indexed as

Biological Specimen BanksBiomedical ResearchAnimalsGenomicsHumansPrecision MedicineTranslational Research, BiomedicalHeart failureHypertrophic obstructive cardiomyopathyTissue biobankingTranslational research

Identifiers

PMID34931483
PMCPMC8787984
OpenAlexW4200196923

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.