↓ Skip to main content

Modeling Alzheimer’s disease: from past to future

Overview of attention for article published in Frontiers in Pharmacology, January 2013
Altmetric Badge

Mentioned by

twitter
1 X user

Readers on

mendeley
197 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
Modeling Alzheimer’s disease: from past to future
Published in
Frontiers in Pharmacology, January 2013
DOI 10.3389/fphar.2013.00077
Pubmed ID
Authors

Claudia Saraceno, Stefano Musardo, Elena Marcello, Silvia Pelucchi, Monica Di Luca

Abstract

Alzheimer's disease (AD) is emerging as the most prevalent and socially disruptive illness of aging populations, as more people live long enough to become affected. Although AD is placing a considerable and increasing burden on society, it represents the largest unmet medical need in neurology, because current drugs improve symptoms, but do not have profound disease-modifying effects. Although AD pathogenesis is multifaceted and difficult to pinpoint, genetic and cell biological studies led to the amyloid hypothesis, which posits that amyloid β (Aβ) plays a pivotal role in AD pathogenesis. Amyloid precursor protein (APP), as well as β- and γ-secretases are the principal players involved in Aβ production, while α-secretase cleavage on APP prevents Aβ deposition. The association of early onset familial AD with mutations in the APP and γ-secretase components provided a potential tool of generating animal models of the disease. However, a model that recapitulates all the aspects of AD has not yet been produced. Here, we face the problem of modeling AD pathology describing several models, which have played a major role in defining critical disease-related mechanisms and in exploring novel potential therapeutic approaches. In particular, we will provide an extensive overview on the distinct features and pros and contras of different AD models, ranging from invertebrate to rodent models and finally dealing with computational models and induced pluripotent stem cells.

X Demographics

X Demographics

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 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 <1%
Netherlands 1 <1%
Chile 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Unknown 192 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 24%
Student > Bachelor 38 19%
Researcher 21 11%
Student > Master 21 11%
Student > Doctoral Student 11 6%
Other 19 10%
Unknown 39 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 31%
Neuroscience 33 17%
Biochemistry, Genetics and Molecular Biology 17 9%
Medicine and Dentistry 8 4%
Pharmacology, Toxicology and Pharmaceutical Science 6 3%
Other 26 13%
Unknown 45 23%
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 19 June 2013.
All research outputs
#20,195,024
of 22,712,476 outputs
Outputs from Frontiers in Pharmacology
#9,924
of 15,939 outputs
Outputs of similar age
#248,758
of 280,743 outputs
Outputs of similar age from Frontiers in Pharmacology
#92
of 167 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 15,939 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 280,743 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 167 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.