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Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer's Disease

Overview of attention for article published in PLOS ONE, September 2008
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2 X users
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2 Facebook pages

Citations

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103 Mendeley
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Title
Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer's Disease
Published in
PLOS ONE, September 2008
DOI 10.1371/journal.pone.0003111
Pubmed ID
Authors

Martín Gómez Ravetti, Pablo Moscato

Abstract

Alzheimer's disease (AD) is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer's is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment. In general the drug-treatments are directed at slowing the progression of symptoms. They have proved to be effective in a large group of patients but success is directly correlated with identifying the disease carriers at its early stages. This justifies the need for timely and accurate forms of diagnosis via molecular means. We report here a 5-protein biomarker molecular signature that achieves, on average, a 96% total accuracy in predicting clinical AD. The signature is composed of the abundances of IL-1alpha, IL-3, EGF, TNF-alpha and G-CSF.

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X Demographics

The data shown below were collected from the profiles of 2 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Spain 2 2%
Argentina 1 <1%
France 1 <1%
Australia 1 <1%
Belgium 1 <1%
Unknown 95 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 29%
Student > Ph. D. Student 18 17%
Student > Master 9 9%
Professor 7 7%
Student > Postgraduate 5 5%
Other 19 18%
Unknown 15 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 19%
Computer Science 12 12%
Medicine and Dentistry 11 11%
Neuroscience 9 9%
Biochemistry, Genetics and Molecular Biology 7 7%
Other 21 20%
Unknown 23 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 May 2013.
All research outputs
#14,168,910
of 22,708,120 outputs
Outputs from PLOS ONE
#115,897
of 193,897 outputs
Outputs of similar age
#71,362
of 85,137 outputs
Outputs of similar age from PLOS ONE
#383
of 434 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,897 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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We're also able to compare this research output to 434 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.