Evidence map›Paper›PMID 34430623›Full record

ArticleAnnals of translational medicine2021

Plasma proteomic profiling reveals biomarkers associated with aortic dilation in patients with bicuspid aortic valve.

Wenrui Ma, Jingjing Zhang, Kehua Xu, Shiqiang Yan, Dingqian Liu, Hui Huang, Yuyi Tang, Guoquan Yan, Yongxin Sun, Jun Li and 4 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
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

14 authors at 3 institutions in 1 country.

Wenrui MaDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Jingjing ZhangInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Kehua XuInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Shiqiang YanInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Dingqian LiuDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Hui HuangInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Yuyi TangInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Guoquan YanInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Yongxin SunDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Jun LiDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Weijia ZhangInstitutes of Biomedical Sciences and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai Medical College, Fudan University, Shanghai, China.
Chunsheng WangDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Kai ZhuDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Hao LaiDepartment of Cardiac Surgery and Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Shanghai Medical College of Fudan University · CNZhongshan Hospital · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBicuspid aortic valve (BAV) is the most common congenital heart anomaly and is prone to cause complications, such as valvular stenosis and thoracic aortic dilation. There is currently no reliable way to predict the progression rate to thoracic aortic aneurysm. Here, we aimed to characterize the proteomic landscape in the plasma of stenotic BAV patients and provide potential biomarkers to predict progressive aortic dilation.

methodsPlasma samples were obtained from 45 subjects (30 stenotic BAV patients and 15 healthy controls). All samples were properly prepared and analyzed using mass spectrometry (MS)-based label-free quantitative proteomics.

resultsA total of 748 plasma proteins had missingness <50%, and 193 (25.8%) were differentially expressed in the BAV patients. Functions regarding cell junction and actin cytoskeleton were largely enriched.

conclusionsOur data reveal unique features in the proteomic architecture of stenotic BAV patients' plasma, and we propose the potential of Notch signaling proteins

Indexed as

aortic dilationbicuspid aortic valve (BAV)biomarkerNotch signalingPlasma proteomics

Identifiers

PMID34430623
PMCPMC8350663
OpenAlexW3186043081

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.