Evidence map›Paper›PMID 26572227›Full record

ArticleBMC microbiology2015

Development of reverse-transcription loop-mediated isothermal amplification assay for rapid detection and differentiation of dengue virus serotypes 1-4.

Sheng-feng Hu, Miao Li, Lan-lan Zhong, Shi-miao Lu, Ze-xia Liu, Jie-ying Pu, Jin-sheng Wen, Xi Huang

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in BMC microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
5.6field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 55 citations in OpenAlex.

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  14. Rapid and Specific Detection of All KnownFrontiers in microbiology · 2019
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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.

Sheng-feng HuProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. hushengfengsysu@126.com.
Miao LiProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. limiao2@mail2.sysu.edu.cn.
Lan-lan ZhongProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. lanlanzhong74@163.com.
Shi-miao LuProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. 735768685@qq.com.
Ze-xia LiuProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. ll.zailushang@ymail.com.
Jie-ying PuProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. pujy1105@163.com.
Jin-sheng WenDepartment of microbiology and immunology, Wenzhou, Medical University, Wenzhou, China. wjs@wmu.edu.cn.
Xi HuangProgram of Immunology, Institute of Human Virology, Affiliated Guangzhou Women and Children's Medical Center, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou, 510080, China. huangxi6@mail.sysu.edu.cn.
Sun Yat-sen University · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDengue virus (DENV), the most widely prevalent arbovirus, continues to be a threat to human health in the tropics and subtropics. Early and rapid detection of DENV infection during the acute phase of illness is crucial for proper clinical patient management and preventing the spread of infection. The aim of the current study was to develop a specific, sensitive, and robust reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay for detection and differentiation of DENV1-4 serotypes.

resultsThe method detection primers, which were designed to target the different DENV serotypes, were identified by inspection of multiple sequence alignments of the non-structural protein (NS) 2A of DENV1, NS4B of DENV2, NS4A of DENV3 and the 3' untranslated region of the NS protein of DENV4. No cross-reactions of the four serotypes were observed during the tests. The detection limits of the DENV1-4-specific RT-LAMP assays were approximately 10-copy templates per reaction. The RT-LAMP assays were ten-fold more sensitive than RT-PCR or real-time PCR. The diagnostic rate was 100% for clinical strains of DENV, and 98.9% of the DENV-infected patients whose samples were tested were detected by RT-LAMP. Importantly, no false-positives were detected with the new equipment and methodology that was used to avoid aerosol contamination of the samples.

conclusionThe RT-LAMP method used in our study is specific, sensitive, and suitable for further investigation as a useful alternative to the current methods used for clinical diagnosis of DENV1-4, especially in hospitals and laboratories that lack sophisticated diagnostic systems.

Indexed as

DengueDengue VirusDNA PrimersHumansMolecular Diagnostic TechniquesNucleic Acid Amplification TechniquesReverse TranscriptionSensitivity and SpecificitySerogroupTemperatureTime FactorsDNA Primers

Identifiers

PMID26572227
PMCPMC4647581
OpenAlexW2175815746

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.