Title |
Post-Genomics and Vaccine Improvement for Leishmania
|
---|---|
Published in |
Frontiers in Microbiology, April 2016
|
DOI | 10.3389/fmicb.2016.00467 |
Pubmed ID | |
Authors |
Negar Seyed, Tahereh Taheri, Sima Rafati |
Abstract |
Leishmaniasis is a parasitic disease that primarily affects Asia, Africa, South America, and the Mediterranean basin. Despite extensive efforts to develop an effective prophylactic vaccine, no promising vaccine is available yet. However, recent advancements in computational vaccinology on the one hand and genome sequencing approaches on the other have generated new hopes in vaccine development. Computational genome mining for new vaccine candidates is known as reverse vaccinology and is believed to further extend the current list of Leishmania vaccine candidates. Reverse vaccinology can also reduce the intrinsic risks associated with live attenuated vaccines. Individual epitopes arranged in tandem as polytopes are also a possible outcome of reverse genome mining. Here, we will briefly compare reverse vaccinology with conventional vaccinology in respect to Leishmania vaccine, and we will discuss how it influences the aforementioned topics. We will also introduce new in vivo models that will bridge the gap between human and laboratory animal models in future studies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 25% |
Switzerland | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 1% |
Spain | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 88 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 19 | 21% |
Student > Ph. D. Student | 18 | 20% |
Student > Bachelor | 13 | 14% |
Researcher | 8 | 9% |
Student > Doctoral Student | 6 | 7% |
Other | 12 | 13% |
Unknown | 15 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 22% |
Immunology and Microbiology | 16 | 18% |
Biochemistry, Genetics and Molecular Biology | 15 | 16% |
Medicine and Dentistry | 6 | 7% |
Computer Science | 3 | 3% |
Other | 13 | 14% |
Unknown | 18 | 20% |