Evidence map›Paper›PMID 25253997›Full record

ArticleProteome science2014

Proteomic study of sporadic inclusion body myositis.

Ke Li, Chuanqiang Pu, Xusheng Huang, Jiexiao Liu, Yanling Mao, Xianghui Lu

Open access · goldAbstract read
In one paragraph

Article in Proteome science, 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
0.6field-weighted citation impact, top 28% 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, 8 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.

Ke LiDepartment of Geriatric Neurology, Chinese PLA General Hospital, Beijing, 100853 China.
Chuanqiang PuDepartment of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China.
Xusheng HuangDepartment of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China.
Jiexiao LiuDepartment of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China.
Yanling MaoDepartment of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China.
Xianghui LuDepartment of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853 China.
Chinese PLA General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSporadic inclusion body myositis (s-IBM) is the most commonly occurring acquired inflammatory myopathy in elderly people (>45 years); however, pathogenic mechanisms are poorly understood and diagnostic tools are limited. In view of this, new therapeutic and diagnostic molecular markers for s-IBM need to be identified. EXPERIMENTAL

designIn this study, the proteomes from three s-IBM cases were compared with those from three cases of neurogenic muscular atrophy (control). Proteins were separated by 2-dimensional polyacrylamide gel electrophoresis and profiled by mass spectrometric sequencing and subsequently validated by western blot.

resultsDifferential expression was noted in 29 proteins (16 upregulated and 13 downregulated) in s-IBM compared with the control group. Functions of these proteins include oxidative stress response, regulation of apoptosis, signal transduction, and cytoskeleton. Expression of both amyloid precursor protein (APP) and αB-crystallin was increased in s-IBM cases.

conclusionsOur study reveals a unique pattern of protein expression in s-IBM, which should be further investigated in a wider cohort of IBM patients to fully realize the potential diagnostic or therapeutic benefits.

Indexed as

APPNeurogenic muscular atrophySporadic inclusion body myositisαB-crystallin

Identifiers

PMID25253997
PMCPMC4172931
OpenAlexW2105449285

What OpenQuestion holds

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Registered trials

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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.