| Title |
A new era of disease modeling and drug discovery using induced pluripotent stem cells
|
|---|---|
| Published in |
Archives of Pharmacal Research, December 2016
|
| DOI | 10.1007/s12272-016-0871-0 |
| Pubmed ID | |
| Authors |
Wonhee Suh |
| Abstract |
In 2006, Shinya Yamanaka first reported that in vitro reprogramming of somatic cells toward pluripotency was achieved by simple induction of specific transcription factors. Induced pluripotent stem cell (iPSC) technology has since revolutionized the ways in which we explore the mechanisms of human diseases and develop therapeutics. Here, I describe the recent advances in human iPSC-based disease modeling and drug discovery and discuss the current challenges. Additionally, I outline potential future applications of human iPSCs in classifying patients based on their response to drugs in clinical trials and elucidating optimal patient-specific therapeutic strategies, which will contribute to reduced attrition rates and the development of precision medicine. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| France | 1 | 1% |
| Unknown | 67 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 18 | 26% |
| Student > Ph. D. Student | 17 | 25% |
| Student > Master | 11 | 16% |
| Researcher | 6 | 9% |
| Student > Doctoral Student | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 19 | 28% |
| Agricultural and Biological Sciences | 11 | 16% |
| Neuroscience | 9 | 13% |
| Medicine and Dentistry | 8 | 12% |
| Immunology and Microbiology | 2 | 3% |
| Other | 7 | 10% |
| Unknown | 13 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 13 December 2016.
All research outputs
#4,041,012
of 22,912,409 outputs
Outputs from Archives of Pharmacal Research
#132
of 1,297 outputs
Outputs of similar age
#79,030
of 415,994 outputs
Outputs of similar age from Archives of Pharmacal Research
#5
of 15 outputs
Altmetric has tracked 22,912,409 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,297 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 89% 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 415,994 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 80% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.