Mouse models for immuno-oncology
Trends in Cancer, ISSN: 2405-8033, Vol: 9, Issue: 7, Page: 578-590
2023
- 11Citations
- 7Usage
- 35Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations11
- Citation Indexes11
- 11
- CrossRef1
- Usage7
- Abstract Views7
- Captures35
- Readers35
- 35
Review Description
Realizing the clinical promise of cancer immunotherapy is hindered by gaps in our knowledge of in vivo mechanisms underlying treatment response as well as treatment limiting toxicity. Preclinical in vivo model systems and technologies are required to address these knowledge gaps and to surmount the challenges faced in the clinical application of immunotherapy. Mice are commonly used for basic and translational research to support development and testing of immune interventions, including for cancer. Here, we discuss the advantages and the limitations of current models as well as future developments.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2405803323000419; http://dx.doi.org/10.1016/j.trecan.2023.03.009; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85152940649&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/37087398; https://linkinghub.elsevier.com/retrieve/pii/S2405803323000419; https://mouseion.jax.org/stfb2023/155; https://mouseion.jax.org/cgi/viewcontent.cgi?article=1142&context=stfb2023; https://dx.doi.org/10.1016/j.trecan.2023.03.009
Elsevier BV
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