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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Exploiting the CD1d-iNKT Cell Axis for Potentiation of DC-Based Cancer Vaccines.
|
|---|---|
| Chapter number | 14 |
| Book title |
Cancer Vaccines
|
| Published in |
Methods in molecular biology, March 2014
|
| DOI | 10.1007/978-1-4939-0345-0_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-0344-3, 978-1-4939-0345-0
|
| Authors |
Lameris R, Schneiders FL, de Gruijl TD, van der Vliet HJ, Roeland Lameris, Famke L. Schneiders, Tanja D. de Gruijl, Hans J. van der Vliet |
| Abstract |
Invariant natural killer T cells (iNKT) and dendritic cells (DC) play a central role in tumor immunity through downstream activation of immune effector cells by pro-inflammatory cytokines. Evidence is accumulating that the CD1d-iNKT cell axis can be effectively used to potentiate DC-based cancer vaccines. Here, we provide a detailed methodology for the generation of (CD1d-expressing) monocyte-derived DC (moDC) and their subsequent loading with the iNKT cell agonist α-galactosylceramide (α-GalCer) or their direct ligation by agonistic anti-CD1d monoclonal antibodies. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 3% |
| Switzerland | 1 | 3% |
| Unknown | 38 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Master | 4 | 10% |
| Student > Doctoral Student | 2 | 5% |
| Student > Bachelor | 2 | 5% |
| Other | 7 | 18% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 20% |
| Agricultural and Biological Sciences | 6 | 15% |
| Immunology and Microbiology | 6 | 15% |
| Medicine and Dentistry | 3 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 12 | 30% |
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 November 2014.
All research outputs
#18,369,403
of 22,751,628 outputs
Outputs from Methods in molecular biology
#7,861
of 13,088 outputs
Outputs of similar age
#160,818
of 220,996 outputs
Outputs of similar age from Methods in molecular biology
#53
of 160 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,088 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 220,996 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 160 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.