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Induced pluripotent stem cells in Alzheimer’s disease: applications for disease modeling and cell-replacement therapy

Overview of attention for article published in Molecular Neurodegeneration, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#49 of 574)
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
3 tweeters
facebook
3 Facebook pages

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
142 Mendeley
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Title
Induced pluripotent stem cells in Alzheimer’s disease: applications for disease modeling and cell-replacement therapy
Published in
Molecular Neurodegeneration, May 2016
DOI 10.1186/s13024-016-0106-3
Pubmed ID
Authors

Juan Yang, Song Li, Xi-Biao He, Cheng, Weidong Le

Abstract

Alzheimer's disease (AD) is the most common cause of dementia in those over the age of 65. While a numerous of disease-causing genes and risk factors have been identified, the exact etiological mechanisms of AD are not yet completely understood, due to the inability to test theoretical hypotheses on non-postmortem and patient-specific research systems. The use of recently developed and optimized induced pluripotent stem cells (iPSCs) technology may provide a promising platform to create reliable models, not only for better understanding the etiopathological process of AD, but also for efficient anti-AD drugs screening. More importantly, human-sourced iPSCs may also provide a beneficial tool for cell-replacement therapy against AD. Although considerable progress has been achieved, a number of key challenges still require to be addressed in iPSCs research, including the identification of robust disease phenotypes in AD modeling and the clinical availabilities of iPSCs-based cell-replacement therapy in human. In this review, we highlight recent progresses of iPSCs research and discuss the translational challenges of AD patients-derived iPSCs in disease modeling and cell-replacement therapy.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 1 <1%
Korea, Republic of 1 <1%
Unknown 140 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 26%
Student > Master 34 24%
Student > Bachelor 20 14%
Researcher 18 13%
Unspecified 13 9%
Other 20 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 28%
Biochemistry, Genetics and Molecular Biology 31 22%
Neuroscience 29 20%
Unspecified 13 9%
Engineering 9 6%
Other 20 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 15 April 2017.
All research outputs
#949,092
of 13,500,063 outputs
Outputs from Molecular Neurodegeneration
#49
of 574 outputs
Outputs of similar age
#26,764
of 264,073 outputs
Outputs of similar age from Molecular Neurodegeneration
#2
of 10 outputs
Altmetric has tracked 13,500,063 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 574 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. 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 264,073 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 89% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.