Evidence map›Paper›PMID 23638224›Full record

ArticleInternational journal of clinical and experimental pathology2013

Intestinal protein expression profile identifies inflammatory bowel disease and predicts relapse.

Jun Shen, Yuqi Qiao, Zhihua Ran, Tianrong Wang, Jiangtao Xu, Jinsun Feng

Open access · greenAbstract read
In one paragraph

Article in International journal of clinical and experimental pathology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

11 citing papers in PubMed, 14 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

6 authors at 1 institution in 1 country.

Jun ShenDivision of Gastroenterology and Hepatology, Shanghai Jiao-Tong University School of Medicine, Renji Hospital, Shanghai Institute of Digestive Disease, Shanghai, 200127, China.
Yuqi Qiao
Zhihua Ran
Tianrong Wang
Jiangtao Xu
Jinsun Feng
Renji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To date, most studies have applied individual factors as indicators of disease classification and prognosis. The aim of this study is to determine whether clustering analysis of protein expression profiles in intestinal epithelia improves classification and prognosis in patients with inflammatory bowel disease (IBD). One hundred and twenty Crohn's disease (CD) patients, 117 ulcerative colitis (UC) patients and 120 cases of nonspecific colitis provided intestinal biopsy samples for tissue microarray (TMA). Both unsupervised and supervised analyses were used for evaluation of clustering and association with relapse. There was a significant concordance between cluster groups based on immunostaining data of TMA and clinical classification in distinguishing IBD from nonspecific colitis (kappa= 0.498, p<0.001). CD27, CD70, CD40, TRAF3, TRAF4 and TRAF2 presented similar immunostaining features, which were different from clusters of CD154, CD80 and TRAF5. Moreover, higher expression of TRAF2 was a predictor of relapse in patients with UC (p=0.006).Thus, protein expression profiles can distinguish IBD and nonspecific colitis, and combination analysis protein expression profiles show that TRAF2 can predict relapse of UC.

Indexed as

AdultBiomarkersCluster AnalysisFemaleHumansImmunohistochemistryInflammatory Bowel DiseasesMalePrognosisProteomeRecurrenceTissue Array AnalysisTNF Receptor-Associated Factor 2BiomarkersProteomeTNF Receptor-Associated Factor 2classificationCrohn’s diseaserelapseulcerative colitis

Identifiers

PMID23638224
PMCPMC3638103
OpenAlexW2286996076

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.