| Title |
Efficient delivery of Notch1 siRNA to SKOV3 cells by cationic cholesterol derivative-based liposome
|
|---|---|
| Published in |
International Journal of Nanomedicine, October 2016
|
| DOI | 10.2147/ijn.s115367 |
| Pubmed ID | |
| Authors |
Yun-Chun Zhao, Li Zhang, Shi-Sen Feng, Lu Hong, Hai-Li Zheng, Li-Li Chen, Xiao-Ling Zheng, Yi-Qing Ye, Meng-Dan Zhao, Wen-Xi Wang, Cai-Hong Zheng |
| Abstract |
A novel cationic cholesterol derivative-based small interfering RNA (siRNA) interference strategy was suggested to inhibit Notch1 activation in SKOV3 cells for the gene therapy of ovarian cancer. The cationic cholesterol derivative, N-(cholesterylhemisuccinoyl-amino-3-propyl)-N, N-dimethylamine (DMAPA-chems) liposome, was incubated with siRNA at different nitrogen-to-phosphate ratios to form stabilized, near-spherical siRNA/DMAPA-chems nanoparticles with sizes of 100-200 nm and zeta potentials of 40-50 mV. The siRNA/DMAPA-chems nanoparticles protected siRNA from nuclease degradation in 25% fetal bovine serum. The nanoparticles exhibited high cell uptake and Notch1 gene knockdown efficiency in SKOV3 cells at an nitrogen-to-phosphate ratio of 100 and an siRNA concentration of 50 nM. They also inhibited the growth and promoted the apoptosis of SKOV3 cells. These results may provide the potential for using cationic cholesterol derivatives as efficient nonviral siRNA carriers for the suppression of Notch1 activation in ovarian cancer cells. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 23% |
| Student > Ph. D. Student | 4 | 15% |
| Researcher | 3 | 12% |
| Student > Postgraduate | 2 | 8% |
| Student > Doctoral Student | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 8 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 15% |
| Agricultural and Biological Sciences | 3 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Medicine and Dentistry | 2 | 8% |
| Engineering | 2 | 8% |
| Other | 4 | 15% |
| Unknown | 8 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 21 October 2016.
All research outputs
#16,721,208
of 25,373,627 outputs
Outputs from International Journal of Nanomedicine
#2,088
of 4,123 outputs
Outputs of similar age
#207,195
of 332,569 outputs
Outputs of similar age from International Journal of Nanomedicine
#57
of 111 outputs
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