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Effects of pre-analytical processes on blood samples used in metabolomics studies

Overview of attention for article published in Analytical & Bioanalytical Chemistry, March 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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7 X users
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Citations

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385 Mendeley
Title
Effects of pre-analytical processes on blood samples used in metabolomics studies
Published in
Analytical & Bioanalytical Chemistry, March 2015
DOI 10.1007/s00216-015-8565-x
Pubmed ID
Authors

Peiyuan Yin, Rainer Lehmann, Guowang Xu

Abstract

Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography-mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 385 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 4 1%
Spain 2 <1%
Sweden 1 <1%
Netherlands 1 <1%
United Kingdom 1 <1%
South Africa 1 <1%
Unknown 375 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 83 22%
Researcher 62 16%
Student > Bachelor 45 12%
Student > Master 43 11%
Student > Doctoral Student 20 5%
Other 60 16%
Unknown 72 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 73 19%
Agricultural and Biological Sciences 62 16%
Medicine and Dentistry 49 13%
Chemistry 44 11%
Pharmacology, Toxicology and Pharmaceutical Science 13 3%
Other 57 15%
Unknown 87 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 March 2022.
All research outputs
#5,380,869
of 26,017,215 outputs
Outputs from Analytical & Bioanalytical Chemistry
#823
of 9,818 outputs
Outputs of similar age
#60,843
of 276,574 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#11
of 144 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,818 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 276,574 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.