Francesca Di Rosa

7.1k total citations · 1 hit paper
45 papers, 3.9k citations indexed

About

Francesca Di Rosa is a scholar working on Immunology, Oncology and Hematology. According to data from OpenAlex, Francesca Di Rosa has authored 45 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Immunology, 7 papers in Oncology and 4 papers in Hematology. Recurrent topics in Francesca Di Rosa's work include T-cell and B-cell Immunology (32 papers), Immune Cell Function and Interaction (30 papers) and Immunotherapy and Immune Responses (30 papers). Francesca Di Rosa is often cited by papers focused on T-cell and B-cell Immunology (32 papers), Immune Cell Function and Interaction (30 papers) and Immunotherapy and Immune Responses (30 papers). Francesca Di Rosa collaborates with scholars based in Italy, United States and United Kingdom. Francesca Di Rosa's co-authors include Polly Matzinger, J P Ridge, Angela Santoni, Reinhard Pabst, Ambra Natalini, Fabrizio Antonangeli, Francesca Aloisi, Barbara Serafini, M. Epstein and Dragana Janković and has published in prestigious journals such as Nature, The Journal of Experimental Medicine and Blood.

In The Last Decade

Francesca Di Rosa

44 papers receiving 3.9k citations

Hit Papers

A conditioned dendritic c... 1998 2026 2007 2016 1998 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Francesca Di Rosa Italy 21 3.2k 890 626 343 205 45 3.9k
Patrick Schaerli Switzerland 20 3.3k 1.0× 1.0k 1.2× 798 1.3× 324 0.9× 152 0.7× 21 4.5k
Klaas P. J. M. van Gisbergen Netherlands 32 2.8k 0.9× 772 0.9× 655 1.0× 372 1.1× 179 0.9× 63 3.5k
Alina C. Boesteanu United States 24 3.2k 1.0× 749 0.8× 556 0.9× 270 0.8× 241 1.2× 35 3.9k
Kurt Shanebeck Canada 16 3.4k 1.0× 834 0.9× 650 1.0× 644 1.9× 161 0.8× 21 4.3k
Masako Kohyama Japan 21 3.2k 1.0× 767 0.9× 951 1.5× 323 0.9× 236 1.2× 44 4.1k
Adam P. Uldrich Australia 34 4.5k 1.4× 1.2k 1.3× 528 0.8× 616 1.8× 223 1.1× 54 5.0k
Wataru Ise Japan 23 3.0k 0.9× 533 0.6× 760 1.2× 275 0.8× 209 1.0× 41 3.8k
David L. Boone United States 19 3.5k 1.1× 746 0.8× 762 1.2× 579 1.7× 200 1.0× 35 4.2k
Kazuko Shibuya Japan 34 3.8k 1.2× 805 0.9× 911 1.5× 402 1.2× 220 1.1× 90 5.0k
Lois L. Cavanagh Australia 24 2.7k 0.8× 613 0.7× 566 0.9× 303 0.9× 160 0.8× 35 3.5k

Countries citing papers authored by Francesca Di Rosa

Since Specialization
Citations

This map shows the geographic impact of Francesca Di Rosa'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 Francesca Di Rosa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesca Di Rosa more than expected).

Fields of papers citing papers by Francesca Di Rosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Francesca Di Rosa. 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 Francesca Di Rosa. The network helps show where Francesca Di Rosa may publish in the future.

Co-authorship network of co-authors of Francesca Di Rosa

This figure shows the co-authorship network connecting the top 25 collaborators of Francesca Di Rosa. A scholar is included among the top collaborators of Francesca Di Rosa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesca Di Rosa. Francesca Di Rosa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Natalini, Ambra, et al.. (2022). Durable CD8 T Cell Memory against SARS-CoV-2 by Prime/Boost and Multi-Dose Vaccination: Considerations on Inter-Dose Time Intervals. International Journal of Molecular Sciences. 23(22). 14367–14367. 7 indexed citations
2.
Gerussi, Alessio, Ambra Natalini, Fabrizio Antonangeli, et al.. (2021). Immune-Mediated Drug-Induced Liver Injury: Immunogenetics and Experimental Models. International Journal of Molecular Sciences. 22(9). 4557–4557. 55 indexed citations
3.
Simonetti, Sonia, Ambra Natalini, Giovanna Peruzzi, et al.. (2021). A DNA/Ki67-Based Flow Cytometry Assay for Cell Cycle Analysis of Antigen-Specific CD8 T Cells in Vaccinated Mice. Journal of Visualized Experiments. 7 indexed citations
4.
Muñoz‐Ruiz, Miguel, Irma Pujol‐Autonell, Hefin Rhys, et al.. (2020). Tracking immunodynamics by identification of S-G2/M-phase T cells in human peripheral blood. Journal of Autoimmunity. 112. 102466–102466. 9 indexed citations
5.
Simonetti, Sonia, Ambra Natalini, Antonella Folgori, et al.. (2018). Antigen‐specific CD8 T cells in cell cycle circulate in the blood after vaccination. Scandinavian Journal of Immunology. 89(2). e12735–e12735. 13 indexed citations
6.
Simonetti, Sonia, Sara Vitale, Daniele Runci, et al.. (2017). GM-CSF Inhibits c-Kit and SCF Expression by Bone Marrow-Derived Dendritic Cells. Frontiers in Immunology. 8. 147–147. 9 indexed citations
7.
Rosa, Francesca Di. (2016). Two Niches in the Bone Marrow: A Hypothesis on Life-long T Cell Memory. Trends in Immunology. 37(8). 503–512. 46 indexed citations
8.
9.
Rosa, Francesca Di. (2008). T‐lymphocyte interaction with stromal, bone and hematopoietic cells in the bone marrow. Immunology and Cell Biology. 87(1). 20–29. 49 indexed citations
10.
Parretta, Elisabetta, Giuliana Cassese, Angela Santoni, et al.. (2008). Kinetics of In Vivo Proliferation and Death of Memory and Naive CD8 T Cells: Parameter Estimation Based on 5-Bromo-2′-Deoxyuridine Incorporation in Spleen, Lymph Nodes, and Bone Marrow. The Journal of Immunology. 180(11). 7230–7239. 64 indexed citations
11.
Parretta, Elisabetta, Giuliana Cassese, Pasquale Barba, et al.. (2005). CD8 Cell Division Maintaining Cytotoxic Memory Occurs Predominantly in the Bone Marrow. The Journal of Immunology. 174(12). 7654–7664. 117 indexed citations
12.
Rosa, Francesca Di & Reinhard Pabst. (2005). The bone marrow: a nest for migratory memory T cells. Trends in Immunology. 26(7). 360–366. 231 indexed citations
13.
Giorda, Ezio, Leonardo Sibilio, Aline Martayan, et al.. (2005). Modular usage of the HLA‐DRA promoter in extra‐hematopoietic and hematopoietic cell types of transgenic mice. FEBS Journal. 272(12). 3214–3226. 3 indexed citations
14.
Rosa, Francesca Di & Angela Santoni. (2003). Memory T‐cell competition for bone marrow seeding. Immunology. 108(3). 296–304. 49 indexed citations
15.
Cippitelli, Marco, Cinzia Fionda, Danilo Di Bona, et al.. (2002). Negative Regulation of CD95 Ligand Gene Expression by Vitamin D3 in T Lymphocytes. The Journal of Immunology. 168(3). 1154–1166. 59 indexed citations
16.
Paroli, Marino, Enrico Schiaffella, Francesca Di Rosa, & Vincenzo Barnaba. (2000). Persisting viruses and autoimmunity. Journal of Neuroimmunology. 107(2). 201–204. 20 indexed citations
17.
Ridge, J P, Francesca Di Rosa, & Polly Matzinger. (1998). A conditioned dendritic cell can be a temporal bridge between a CD4+ T-helper and a T-killer cell. Nature. 393(6684). 474–478. 1975 indexed citations breakdown →
18.
Rosa, Francesca Di & Vincenzo Barnaba. (1998). Persisting viruses and chronic inflammation: understanding their relation to autoimmunity. Immunological Reviews. 164(1). 17–27. 43 indexed citations
19.
Rosa, Francesca Di & Polly Matzinger. (1996). Long-lasting CD8 T cell memory in the absence of CD4 T cells or B cells.. The Journal of Experimental Medicine. 183(5). 2153–2163. 90 indexed citations
20.
Epstein, M., Francesca Di Rosa, Dragana Janković, Alan Sher, & Polly Matzinger. (1995). Successful T cell priming in B cell-deficient mice.. The Journal of Experimental Medicine. 182(4). 915–922. 250 indexed citations

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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