Evidence map›Paper›PMID 36166114›Full record

SynthesisBiological trace element research2023

Meta-analysis of TLR4 pathway-related protein alterations induced by arsenic exposure.

Nanxin Ma, Jian Guo, Xiaolong Wu, Zhenzhong Liu, Tian Yao, Qian Zhao, Ben Li, Fengjie Tian, Xiaoyan Yan, Wenping Zhang and 2 more

Abstract readMeta-AnalysisReview
PubMed Publisher
In one paragraph

Synthesis in Biological trace element research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 54% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
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

12 authors at 4 institutions in 1 country.

Nanxin Ma *Department of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Jian Guo *Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Xiaolong WuDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Zhenzhong LiuSchool of Public Health, North Sichuan Medical College, Nanchong, 637000, Sichuan, China.
Tian YaoThe First Hospital of Shanxi Medical University, Shanxi, 030001, China.
Qian ZhaoDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Ben LiDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Fengjie TianDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Xiaoyan YanDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Wenping ZhangDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Yulan QiuDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China.
Yi GaoDepartment of Toxicology, School of Public Health, Shanxi Medical University, 56 Xin-Jian South Road, Taiyuan, 030001, Shanxi, China. ivygao@sxmu.edu.cn.
Shanxi Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNFirst Hospital of Shanxi Medical University · CNNorth Sichuan Medical University · CN

Funding

National Natural Science Foundation of China 82173644Natural Science Foundation of Shanxi Province 202103021224242PhD Start-up Foundation of Shanxi Medical University 03201523Shanxi Scholarship Council of China 2021-085
6 · The paper itself

Abstract

Arsenic is a toxic metal, which ultimately leads to cell apoptosis. TLR4 signaling pathway played a key role in immunomodulatory. Therefore, alterations in related proteins on the TLR4 signaling pathway induced by arsenic exposure was systematically reviewed and analyzed by meta-analysis. Some databases were searched including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), and WANFANG MED ONLINE. The results of NF-κB, IKK, NF-κBp65, phospho-NF-κBp65, and TLR4 expressions were analyzed by Review Manage 5.3. In the arsenic intervention group, NF-κB, phospho-NF-κBp65, and TLR4 expression levels were higher than the control group, respectively. SMD and 95%CI were 11.29 (6.34, 16.24), 4.71(1.73, 7.68), and 5.79 (-4.22, 15.80). Compared to controls, in the exposed group, IKK levels were found to be 38.11-fold higher (Z = 0.97; P = 0.33); NF-κBp65 levels were found to be 0.92-fold higher (Z = 3.33; P = 0.0009) for normal cells and tissue, while IKK levels were found to be 5.18-fold lower (Z = 5.34; P < 0.0001); NF-κBp65 levels were found to be 2.01-fold lower (Z = 3.87; P = 0.0001) for abnormal cells. With comparing of low dose, high dose of arsenic exposure was found to reduce the expression of NF-κB, but increase the expression of NF-κBp65. This review supports the alterations in related proteins on the TLR4 signaling pathway induced by arsenic exposure, which is helpful to provide theoretical basis for the mechanism of toxicity of arsenic-induced immune system damage.

Indexed as

ArsenicNF-kappa BApoptosisSignal TransductionToll-Like Receptor 4ArsenicNF-kappa BToll-Like Receptor 4ArsenicMeta-analysisNF-κBNF-κBp65TLR4

Identifiers

PMID36166114
OpenAlexW4297256604

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.