Evidence map›Paper›PMID 26243830›Full record

ArticleIET systems biology2015

Understanding the aristolochic acid toxicities in rat kidneys with regulatory networks.

Yin-Ying Wang, Zhiguang Li, Tao Chen, Xing-Ming Zhao

Open access · greenAbstract read
In one paragraph

Article in IET systems biology, 2015. 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
0.5field-weighted citation impact, top 32% 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, 3 citations in OpenAlex.

  1. Article
  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

4 authors at 4 institutions in 2 countries.

Yin-Ying WangSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, People's Republic of China.
Zhiguang LiInstitute of Cancer Stem Cell, Cancer Center, Dalian Medical University, 9 Lvshun Road South, Dalian, Liaoning 116044, People's Republic of China.
Tao ChenDivision of Genetic and Molecular Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Xing-Ming ZhaoDepartment of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, People's Republic of China. xm_zhao@tongji.edu.cn.
Dalian Medical University · CNNational Center for Toxicological Research · USShanghai Research Institute of Materials · CNTongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The natural products containing aristolochic acid (AA) have been widely used for acne, gastritis and so on. Recently, it is becoming accepted that AA may be responsible for acute and chronic renal failures as the side effects of Chinese herbs. However, it is unclear what happens in the cells after the AA treatment. In this study, the authors built a gene regulatory network as well as a microRNA-gene regulatory network to investigate the molecular dynamics induced by AA from a systematic perspective. With the regulatory networks, they detected some important pathways and biological processes that were affected by AA treatment, which can help explain the nephrotoxicity and carcinogenicity of AA. They found some important regulators and genes responding to AA treatment, and these genes have been reported to be related to the kidney functions, indicating their important roles in the toxicity of AA.

Indexed as

Models, BiologicalAnimalsAristolochic AcidsComputer SimulationGene Expression ProfilingGene Regulatory NetworksKidneyProteomeRatsSignal Transductionaristolochic acid IAristolochic AcidsProteome

Identifiers

PMID26243830
PMCPMC8687369
OpenAlexW830106886

What OpenQuestion holds

Textmetadata
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.