Evidence map›Paper›PMID 28600618›Full record

ReviewClinical rheumatology2018

Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis.

Xi Xie, Fen Li, Shu Li, Jing Tian, Jin-Wei Chen, Jin-Feng Du, Ni Mao, Jian Chen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical rheumatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 18 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

8 authors at 2 institutions in 1 country.

Xi XieDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Fen LiDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China. lifen5295255@sohu.com.
Shu LiDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jing TianDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jin-Wei ChenDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jin-Feng DuDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Ni MaoDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jian ChenDepartment of Rheumatology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Second Xiangya Hospital of Central South University · CNCentral South University · CN

Funding

the National Natural Science Foundation of China 81571599the National Natural Science Foundation of China for Young Scholar 81302567
6 · The paper itself

Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.

Indexed as

ProteomicsAntirheumatic AgentsArthritis, RheumatoidEtanerceptGene Expression ProfilingHumansInfliximabMetabolomicsTreatment OutcomeTumor Necrosis Factor-alphaAntirheumatic AgentsEtanerceptInfliximabTumor Necrosis Factor-alphaAnti-TNF therapyGenomicsMetabolomicsProteomicsRheumatoid arthritisTranscriptomics

Identifiers

PMID28600618
OpenAlexW2624096264

What Socratic 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 Socratic graph.