| Title |
The Efficiency of the Human CD8+ T Cell Response: How Should We Quantify It, What Determines It, and Does It Matter?
|
|---|---|
| Published in |
PLoS Computational Biology, February 2012
|
| DOI | 10.1371/journal.pcbi.1002381 |
| Pubmed ID | |
| Authors |
Marjet Elemans, Nafisa-Katrin Seich al Basatena, Becca Asquith |
| Abstract |
Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses. |
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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 4% |
| Germany | 1 | 1% |
| Sweden | 1 | 1% |
| Netherlands | 1 | 1% |
| India | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 64 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 29% |
| Student > Ph. D. Student | 17 | 24% |
| Student > Bachelor | 7 | 10% |
| Student > Master | 5 | 7% |
| Student > Doctoral Student | 3 | 4% |
| Other | 10 | 14% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 29% |
| Medicine and Dentistry | 11 | 15% |
| Immunology and Microbiology | 10 | 14% |
| Biochemistry, Genetics and Molecular Biology | 9 | 13% |
| Computer Science | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 11 | 15% |