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Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

Overview of attention for article published in Proteome Science, April 2017
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Title
Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies
Published in
Proteome Science, April 2017
DOI 10.1186/s12953-017-0116-2
Pubmed ID
Authors

Jonatan Eriksson, Simone Andersson, Roger Appelqvist, Elisabet Wieslander, Mikael Truedsson, May Bugge, Johan Malm, Magnus Dahlbäck, Bo Andersson, Thomas E. Fehniger, György Marko-Varga

Abstract

Data from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data. We built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values). We created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database. We have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Bachelor 3 15%
Student > Master 3 15%
Student > Postgraduate 2 10%
Professor > Associate Professor 2 10%
Other 3 15%
Unknown 2 10%
Readers by discipline Count As %
Medicine and Dentistry 5 25%
Agricultural and Biological Sciences 2 10%
Biochemistry, Genetics and Molecular Biology 2 10%
Engineering 2 10%
Chemistry 2 10%
Other 4 20%
Unknown 3 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 26 April 2017.
All research outputs
#20,414,746
of 22,965,074 outputs
Outputs from Proteome Science
#159
of 192 outputs
Outputs of similar age
#269,625
of 309,918 outputs
Outputs of similar age from Proteome Science
#3
of 4 outputs
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So far Altmetric has tracked 192 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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