Evidence map›Paper›PMID 24019858›Full record

ArticleBiomicrofluidics2012

Rapid detection of live methicillin-resistant Staphylococcus aureus by using an integrated microfluidic system capable of ethidium monoazide pre-treatment and molecular diagnosis.

Yu-Hsin Liu, Chih-Hung Wang, Jiunn-Jong Wu, Gwo-Bin Lee

Open access · bronzeAbstract read
In one paragraph

Article in Biomicrofluidics, 2012. 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
2.5field-weighted citation impact, top 12% 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, 28 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

4 authors at 2 institutions in 1 country.

Yu-Hsin LiuDepartment of Engineering Science, National Cheng Kung University, Tainan, Taiwan 701.
Chih-Hung Wang
Jiunn-Jong Wu
Gwo-Bin Lee
National Cheng Kung University · TWNational Tsing Hua University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium resistant to all existing penicillin and lactam-based antimicrobial drugs and, therefore, has become one of the most prevalent antibiotic-resistant pathogens found in hospitals. The multi-drug resistant characteristics of MRSA make it challenging to clinically treat infected patients. Therefore, early diagnosis of MRSA has become a public-health priority worldwide. Conventionally, cell-culture based methodology and microscopic identification are commonly used for MRSA detection. However, they are relatively time-consuming and labor-intensive. Recently, molecular diagnosis based on nucleic acid amplification techniques, such as polymerase chain reaction (PCR), has been widely investigated for the rapid detection of MRSA. However, genomic DNA of both live and dead pathogens can be distinguished by conventional PCR. These results thus could not provide sufficient confirmation of an active infection for clinicians. In this study, live MRSA was rapidly detected by using a new integrated microfluidic system. The microfluidic system has been demonstrated to have 100% specificity to detect live MRSA with S. aureus and other pathogens commonly found in hospitals. The experimental results showed that the limit of detection for live MRSA from biosamples was approximately 10(2) CFU/μl. In addition, the entire diagnostic protocol, from sample pre-treatment to fluorescence observation, can be automatically completed within 2.5 h. Consequently, this microfluidic system may be a powerful tool for the rapid molecular diagnosis of live MRSA.

Identifiers

PMID24019858
PMCPMC3461804
OpenAlexW2035461350

What OpenQuestion holds

Textmetadata
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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.