Daniel Li

23 papers receiving 3.3k citations

Hit Papers

Infectious complications of CD19-targeted chimeric antigen receptor–modified T-cell immunotherapy 2017 · 391 citations
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Peers

Daniel Li
Comparison fields: 5 of 89
  • Oncology 2.8k
  • Genetics 814
  • Immunology 614
  • Biomedical Engineering 863
  • Hematology 209
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Countries citing papers authored by Daniel Li

Since Specialization
Citations

This map shows the geographic impact of Daniel Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Li more than expected).

Fields of papers citing papers by Daniel Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Li. The network helps show where Daniel Li may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Li Line = papers co-authored together Daniel Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Endothelial Activation and Blood–Brain Barrier Disruption in Neurotoxicity after Adoptive Immunotherapy with CD19 CAR-T Cells
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2017948
2
Kinetics and biomarkers of severe cytokine release syndrome after CD19 chimeric antigen receptor–modified T-cell therapy
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2017775
3
Immunotherapy of non-Hodgkin’s lymphoma with a defined ratio of CD8 + and CD4 + CD19-specific chimeric antigen receptor–modified T cells
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2016741
4
Infectious complications of CD19-targeted chimeric antigen receptor–modified T-cell immunotherapy
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2017391
5 2019133
6 201678
7 201846
8 201745
9 202023
10 202223
11 202322
12 202218
13 202116
14 201715
15 202214
16 202214
17 202312
18 20128
19 20168
20 20227

About Daniel Li

Daniel Li is a scholar working on Oncology, Virology, Genetics, Developmental Neuroscience and Otorhinolaryngology, having authored 24 papers that have together received 3.3k indexed citations. Recurring topics across this work include CAR-T cell therapy research (10 papers), Advancements in Semiconductor Devices and Circuit Design (4 papers), Biosimilars and Bioanalytical Methods (2 papers), Integrated Circuits and Semiconductor Failure Analysis (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Hemophilia Treatment and Research (1 paper), HIV Research and Treatment (1 paper) and Long-Term Effects of COVID-19 (1 paper). The work is most often cited by research in Oncology (2.8k citations), Genetics (814 citations), Immunology (614 citations), Biomedical Engineering (863 citations) and Hematology (209 citations). Daniel Li has collaborated with scholars based in United States, Canada and China. Frequent co-authors include David G. Maloney, Cameron J. Turtle, Stanley R. Riddell, Laïla‐Aïcha Hanafi, Kevin A. Hay, Sindhu Cherian, Xueyan Chen, Juliane Gust, Susanna Harju-Baker and Junmei Chen. Their work appears in journals such as Blood, Journal of Alzheimer s Disease, Cancer Discovery, Nature Communications and Journal of Medical Virology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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