↓ Skip to main content

Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview

Overview of attention for article published in Frontiers in Neurology, February 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
10 X users

Citations

dimensions_citation
47 Dimensions

Readers on

mendeley
93 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview
Published in
Frontiers in Neurology, February 2018
DOI 10.3389/fneur.2018.00037
Pubmed ID
Authors

Felix Carbonell, Yasser Iturria-Medina, Alan C. Evans

Abstract

Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 93 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 23%
Researcher 16 17%
Student > Master 14 15%
Student > Bachelor 9 10%
Student > Doctoral Student 5 5%
Other 13 14%
Unknown 15 16%
Readers by discipline Count As %
Neuroscience 15 16%
Biochemistry, Genetics and Molecular Biology 11 12%
Agricultural and Biological Sciences 11 12%
Engineering 8 9%
Mathematics 7 8%
Other 23 25%
Unknown 18 19%
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 24 February 2018.
All research outputs
#4,831,327
of 24,176,243 outputs
Outputs from Frontiers in Neurology
#3,875
of 13,208 outputs
Outputs of similar age
#103,315
of 446,867 outputs
Outputs of similar age from Frontiers in Neurology
#35
of 224 outputs
Altmetric has tracked 24,176,243 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,208 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has gotten more attention than average, scoring higher than 70% 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 446,867 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 76% of its contemporaries.
We're also able to compare this research output to 224 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.