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An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples

Overview of attention for article published in Clinical Proteomics, January 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#42 of 332)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 X user
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6 patents

Citations

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14 Dimensions

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35 Mendeley
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Title
An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples
Published in
Clinical Proteomics, January 2015
DOI 10.1186/1559-0275-12-3
Pubmed ID
Authors

Xiao-jun Li, Lik Wee Lee, Clive Hayward, Mi-Youn Brusniak, Pui-Yee Fong, Matthew McLean, JoAnne Mulligan, Douglas Spicer, Kenneth C Fang, Stephen W Hunsucker, Paul Kearney

Abstract

Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

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The data shown below were collected from the profile of 1 X user 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 8 23%
Unspecified 4 11%
Student > Bachelor 2 6%
Other 2 6%
Other 7 20%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 23%
Medicine and Dentistry 7 20%
Biochemistry, Genetics and Molecular Biology 6 17%
Unspecified 4 11%
Chemistry 3 9%
Other 4 11%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 February 2024.
All research outputs
#3,711,927
of 25,385,509 outputs
Outputs from Clinical Proteomics
#42
of 332 outputs
Outputs of similar age
#51,131
of 361,672 outputs
Outputs of similar age from Clinical Proteomics
#3
of 6 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 332 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done well, scoring higher than 86% 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 361,672 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 85% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.