Evidence map›Paper›PMID 25276827›Full record

ArticleBioMed research international2014

Systematic expression profiling analysis identifies specific microRNA-gene interactions that may differentiate between active and latent tuberculosis infection.

Lawrence Shih-Hsin Wu, Shih-Wei Lee, Kai-Yao Huang, Tzong-Yi Lee, Paul Wei-Che Hsu, Julia Tzu-Ya Weng

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% 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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 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 4 institutions in 1 country.

Lawrence Shih-Hsin WuInstitute of Medical Sciences, Tzu Chi University, Hualien 97004, Taiwan.
Shih-Wei LeeTaoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 33004, Taiwan ; Department of Life Sciences, National Central University, Taoyuan 32001, Taiwan.ORCID 0000-0002-8367-8647
Kai-Yao HuangDepartment of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan.
Tzong-Yi LeeDepartment of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan ; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan.
Paul Wei-Che HsuBioinformatics Core Laboratory, Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan.
Julia Tzu-Ya WengDepartment of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan ; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan.ORCID 0000-0003-0127-0945
Yuan Ze University · TWInstitute of Molecular Biology, Academia Sinica · TWNational Central University · TWTzu Chi University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) is the second most common cause of death from infectious diseases. About 90% of those infected are asymptomatic--the so-called latent TB infections (LTBI), with a 10% lifetime chance of progressing to active TB. To further understand the molecular pathogenesis of TB, several molecular studies have attempted to compare the expression profiles between healthy controls and active TB or LTBI patients. However, the results vary due to diverse genetic backgrounds and study designs and the inherent complexity of the disease process. Thus, developing a sensitive and efficient method for the detection of LTBI is both crucial and challenging. For the present study, we performed a systematic analysis of the gene and microRNA profiles of healthy individuals versus those affected with TB or LTBI. Combined with a series of in silico analysis utilizing publicly available microRNA knowledge bases and published literature data, we have uncovered several microRNA-gene interactions that specifically target both the blood and lungs. Some of these molecular interactions are novel and may serve as potential biomarkers of TB and LTBI, facilitating the development for a more sensitive, efficient, and cost-effective diagnostic assay for TB and LTBI for the Taiwanese population.

Indexed as

Gene Expression ProfilingCase-Control StudiesDiagnosis, DifferentialDown-RegulationGene Regulatory NetworksHumansLatent TuberculosisMicroRNAsReproducibility of ResultsUp-RegulationMicroRNAs

Identifiers

PMID25276827
PMCPMC4167957
OpenAlexW2126935395

What OpenQuestion holds

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LicenceCC BY
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.