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Find video protocols related to scientific articles indexed in Pubmed.
Serum amyloid A and clusterin as potential predictive biomarkers for severe hand, foot and mouth disease by 2D-DIGE proteomics analysis.
PLoS ONE
PUBLISHED: 01-01-2014
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Hand, foot, and mouth disease (HFMD) affects more than one million children, is responsible for several hundred child deaths every year in China and is the cause of widespread concerns in society. Only a small fraction of HFMD cases will develop further into severe HFMD with neurologic complications. A timely and accurate diagnosis of severe HFMD is essential for assessing the risk of progression and planning the appropriate treatment. Human serum can reflect the physiological or pathological states, which is expected to be an excellent source of disease-specific biomarkers. In the present study, a comparative serological proteome analysis between severe HFMD patients and healthy controls was performed via a two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) strategy. Fifteen proteins were identified as differentially expressed in the sera of the severe HFMD patients compared with the controls. The identified proteins were classified into different groups according to their molecular functions, biological processes, protein classes and physiological pathways by bioinformatics analysis. The up-regulations of two identified proteins, serum amyloid A (SAA) and clusterin (CLU), were confirmed in the sera of the HFMD patients by ELISA assay. This study not only increases our background knowledge about and scientific insight into the mechanisms of HFMD, but also reveals novel potential biomarkers for the clinical diagnosis of severe HFMD.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.