Evidence map›Paper›PMID 25539592›Full record

ArticleBMC systems biology2014

Insights from systems pharmacology into cardiovascular drug discovery and therapy.

Peng Li, Yingxue Fu, Jinlong Ru, Chao Huang, Jiangfeng Du, Chunli Zheng, Xuetong Chen, Pidong Li, Aiping Lu, Ling Yang and 1 more

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 22 citations in OpenAlex.

  1. Role of Pyruvate Kinase M2 (PKM2) in Cardiovascular Diseases.Journal of cardiovascular translational research · 2023
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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

11 authors at 5 institutions in 3 countries.

Peng LiCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. lpeng@nwsuaf.edu.cn.
Yingxue FuCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 760587207@qq.com.
Jinlong RuCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 527104360@qq.com.
Chao HuangCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 994625904@qq.com.
Jiangfeng DuDepartment of Biochemistry, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands. j.du@maastrichtuniversity.nl.
Chunli ZhengCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 523041423@qq.com.
Xuetong ChenCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 908687194@qq.com.
Pidong LiCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. 78956287@qq.com.
Aiping LuSchool of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong. lap64067611@126.com.
Ling YangLab of Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, China. yling@dicp.ac.cn.
Yonghua WangCenter of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100, China. yh_wang@nwsuaf.edu.cn.
North West Agriculture and Forestry University · CNNorthwest University · CNDalian Institute of Chemical Physics · CNHong Kong Baptist University · HKMaastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets.

resultsHere, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity < 30%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes, the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs.

conclusionsAll these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective.

Indexed as

Models, BiologicalCardiovascular AgentsCardiovascular DiseasesDrug DiscoveryHumansPharmacologySystems BiologyCardiovascular Agents

Identifiers

PMID25539592
PMCPMC4297424
OpenAlexW2131110455

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.