Title |
Advanced model systems and tools for basic and translational human immunology
|
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Published in |
Genome Medicine, September 2018
|
DOI | 10.1186/s13073-018-0584-8 |
Pubmed ID | |
Authors |
Lisa E. Wagar, Robert M. DiFazio, Mark M. Davis |
Abstract |
There are fundamental differences between humans and the animals we typically use to study the immune system. We have learned much from genetically manipulated and inbred animal models, but instances in which these findings have been successfully translated to human immunity have been rare. Embracing the genetic and environmental diversity of humans can tell us about the fundamental biology of immune cell types and the elasticity of the immune system. Although people are much more immunologically diverse than conventionally housed animal models, tools and technologies are now available that permit high-throughput analysis of human samples, including both blood and tissues, which will give us deep insights into human immunity in health and disease. As we gain a more detailed picture of the human immune system, we can build more sophisticated models to better reflect this complexity, both enabling the discovery of new immunological mechanisms and facilitating translation into the clinic. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 21% |
United Kingdom | 3 | 13% |
Portugal | 2 | 8% |
Sweden | 1 | 4% |
Curaçao | 1 | 4% |
Norway | 1 | 4% |
Canada | 1 | 4% |
Czechia | 1 | 4% |
Malaysia | 1 | 4% |
Other | 0 | 0% |
Unknown | 8 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 12 | 50% |
Scientists | 12 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 210 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 44 | 21% |
Researcher | 33 | 16% |
Student > Master | 29 | 14% |
Student > Bachelor | 21 | 10% |
Student > Doctoral Student | 9 | 4% |
Other | 29 | 14% |
Unknown | 45 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Immunology and Microbiology | 34 | 16% |
Biochemistry, Genetics and Molecular Biology | 26 | 12% |
Medicine and Dentistry | 26 | 12% |
Agricultural and Biological Sciences | 23 | 11% |
Engineering | 11 | 5% |
Other | 31 | 15% |
Unknown | 59 | 28% |