| Title |
Non-viral gene delivery systems for tissue repair and regeneration
|
|---|---|
| Published in |
Journal of Translational Medicine, February 2018
|
| DOI | 10.1186/s12967-018-1402-1 |
| Pubmed ID | |
| Authors |
Pan Wu, Haojiao Chen, Ronghua Jin, Tingting Weng, Jon Kee Ho, Chuangang You, Liping Zhang, Xingang Wang, Chunmao Han |
| Abstract |
Critical tissue defects frequently result from trauma, burns, chronic wounds and/or surgery. The ideal treatment for such tissue loss is autografting, but donor sites are often limited. Tissue engineering (TE) is an inspiring alternative for tissue repair and regeneration (TRR). One of the current state-of-the-art methods for TRR is gene therapy. Non-viral gene delivery systems (nVGDS) have great potential for TE and have several advantages over viral delivery including lower immunogenicity and toxicity, better cell specificity, better modifiability, and higher productivity. However, there is no ideal nVGDS for TRR, hence, there is widespread research to improve their properties. This review introduces the basic principles and key aspects of commonly-used nVGDSs. We focus on recent advances in their applications, current challenges, and future directions. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 176 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 32 | 18% |
| Student > Master | 24 | 14% |
| Researcher | 18 | 10% |
| Student > Bachelor | 11 | 6% |
| Other | 9 | 5% |
| Other | 17 | 10% |
| Unknown | 65 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 29 | 16% |
| Chemistry | 14 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 11 | 6% |
| Materials Science | 10 | 6% |
| Medicine and Dentistry | 10 | 6% |
| Other | 30 | 17% |
| Unknown | 72 | 41% |
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 15 February 2018.
All research outputs
#15,492,327
of 23,023,224 outputs
Outputs from Journal of Translational Medicine
#2,259
of 4,029 outputs
Outputs of similar age
#291,593
of 474,288 outputs
Outputs of similar age from Journal of Translational Medicine
#48
of 82 outputs
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