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Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders

Overview of attention for article published in PLoS Computational Biology, November 2014
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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4 news outlets
blogs
1 blog
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12 X users
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2 Facebook pages

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248 Mendeley
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Article details
Title
Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders
Published in
PLoS Computational Biology, November 2014
DOI 10.1371/journal.pcbi.1003956
Pubmed ID
Authors

Yasser Iturria-Medina, Roberto C. Sotero, Paule J. Toussaint, Alan C. Evans, and the Alzheimer's Disease Neuroimaging Initiative

Abstract

Misfolded proteins (MP) are a key component in aging and associated neurodegenerative disorders. For example, misfolded Amyloid-ß (Aß) and tau proteins are two neuropathogenic hallmarks of Alzheimer's disease. Mechanisms underlying intra-brain MP propagation/deposition remain essentially uncharacterized. Here, is introduced an epidemic spreading model (ESM) for MP dynamics that considers propagation-like interactions between MP agents and the brain's clearance response across the structural connectome. The ESM reproduces advanced Aß deposition patterns in the human brain (explaining 46∼56% of the variance in regional Aß loads, in 733 subjects from the ADNI database). Furthermore, this model strongly supports a) the leading role of Aß clearance deficiency and early Aß onset age during Alzheimer's disease progression, b) that effective anatomical distance from Aß outbreak region explains regional Aß arrival time and Aß deposition likelihood, c) the multi-factorial impact of APOE e4 genotype, gender and educational level on lifetime intra-brain Aß propagation, and d) the modulatory impact of Aß propagation history on tau proteins concentrations, supporting the hypothesis of an interrelated pathway between Aß pathophysiology and tauopathy. To our knowledge, the ESM is the first computational model highlighting the direct link between structural brain networks, production/clearance of pathogenic proteins and associated intercellular transfer mechanisms, individual genetic/demographic properties and clinical states in health and disease. In sum, the proposed ESM constitutes a promising framework to clarify intra-brain region to region transference mechanisms associated with aging and neurodegenerative disorders.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 248 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Canada 2 <1%
United States 1 <1%
Sweden 1 <1%
Korea, Republic of 1 <1%
Japan 1 <1%
Italy 1 <1%
Unknown 241 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 59 24%
Researcher 48 19%
Student > Master 20 8%
Student > Bachelor 18 7%
Other 13 5%
Other 42 17%
Unknown 48 19%
Readers by discipline
Readers by discipline Count As %
Neuroscience 54 22%
Agricultural and Biological Sciences 29 12%
Medicine and Dentistry 24 10%
Engineering 17 7%
Computer Science 15 6%
Other 50 20%
Unknown 59 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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 May 2022.
All research outputs
#1,240,562
of 31,823,548 outputs
Outputs from PLoS Computational Biology
#810
of 9,989 outputs
Outputs of similar age
#13,488
of 403,071 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 135 outputs
Altmetric has tracked 31,823,548 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,989 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. 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 403,071 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 135 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 91% of its contemporaries.