Federico Perosa

3.8k total citations
93 papers, 2.4k citations indexed

About

Federico Perosa is a scholar working on Immunology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Federico Perosa has authored 93 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Immunology, 36 papers in Radiology, Nuclear Medicine and Imaging and 23 papers in Molecular Biology. Recurrent topics in Federico Perosa's work include Monoclonal and Polyclonal Antibodies Research (36 papers), T-cell and B-cell Immunology (22 papers) and Systemic Sclerosis and Related Diseases (18 papers). Federico Perosa is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (36 papers), T-cell and B-cell Immunology (22 papers) and Systemic Sclerosis and Related Diseases (18 papers). Federico Perosa collaborates with scholars based in Italy, United States and Belarus. Federico Perosa's co-authors include Franco Dammacco, Marcella Prete, Vito Racanelli, Elvira Favoino, Soldano Ferrone, Angelo Vacca, Patrizia Leone, Eui‐Cheol Shin, Piero Ruscitti and Roberto Giacomelli and has published in prestigious journals such as Journal of Clinical Investigation, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Federico Perosa

89 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Federico Perosa Italy 25 1.0k 716 522 430 383 93 2.4k
Hiroki Mitoma Japan 24 1.3k 1.3× 767 1.1× 320 0.6× 263 0.6× 714 1.9× 68 2.9k
M. Hart Netherlands 30 1.3k 1.2× 518 0.7× 400 0.8× 560 1.3× 343 0.9× 44 2.7k
Karel J.M. Assmann Netherlands 34 1.3k 1.2× 993 1.4× 256 0.5× 340 0.8× 613 1.6× 98 4.0k
Kendall M. Mohler United States 15 1.4k 1.3× 667 0.9× 555 1.1× 300 0.7× 842 2.2× 17 3.2k
Keishi Fujio Japan 35 2.2k 2.1× 848 1.2× 859 1.6× 330 0.8× 1.1k 2.8× 208 4.1k
Fleur Bossi Italy 29 1.5k 1.4× 600 0.8× 226 0.4× 150 0.3× 495 1.3× 57 2.8k
Tineke C. T. M. van der Pouw Kraan Netherlands 29 891 0.9× 556 0.8× 529 1.0× 194 0.5× 852 2.2× 47 2.3k
Miguel Ángel López‐Nevot Spain 38 2.6k 2.5× 945 1.3× 926 1.8× 212 0.5× 724 1.9× 152 4.4k
Gerald D. Johnson United Kingdom 26 1.1k 1.0× 780 1.1× 291 0.6× 262 0.6× 150 0.4× 38 2.8k
Alla Skapenko Germany 25 1.4k 1.3× 768 1.1× 532 1.0× 132 0.3× 684 1.8× 69 2.8k

Countries citing papers authored by Federico Perosa

Since Specialization
Citations

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

Fields of papers citing papers by Federico Perosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Federico Perosa. 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 Federico Perosa. The network helps show where Federico Perosa may publish in the future.

Co-authorship network of co-authors of Federico Perosa

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Perosa. A scholar is included among the top collaborators of Federico Perosa 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 Federico Perosa. Federico Perosa 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.
Prete, Marcella, et al.. (2024). Classic pyoderma gangrenosum. SHILAP Revista de lepidopterología. 12(2). e8446–e8446. 2 indexed citations
2.
Prete, Marcella, Nicola Susca, Patrizia Leone, et al.. (2023). Impact of belimumab therapy on the quality of life in patients with systemic lupus erythematosus: A cohort study. Lupus. 32(13). 1528–1535. 2 indexed citations
3.
Prete, Marcella, et al.. (2020). SARS-CoV-2 infection complicated by inflammatory syndrome. Could high-dose human immunoglobulin for intravenous use (IVIG) be beneficial?. Autoimmunity Reviews. 19(7). 102559–102559. 21 indexed citations
4.
5.
Prete, Marcella, Elvira Favoino, Roberto Giacomelli, et al.. (2019). Evaluation of the influence of social, demographic, environmental, work-related factors and/or lifestyle habits on Raynaud’s phenomenon: a case–control study. Clinical and Experimental Medicine. 20(1). 31–37. 4 indexed citations
6.
Iacono, Daniela, Elvira Favoino, Serena Fasano, et al.. (2018). Low mortality rate in Italian rheumatoid arthritis patients from a tertiary center: putative implication of a low anti-carbamylated protein antibodies prevalence. Open Access Rheumatology Research and Reviews. Volume 10. 129–134. 2 indexed citations
7.
Perosa, Federico, Elvira Favoino, Isabella Favia, et al.. (2016). Subspecificities of anticentromeric protein A antibodies identify systemic sclerosis patients at higher risk of pulmonary vascular disease. Medicine. 95(25). e3931–e3931. 14 indexed citations
8.
Prete, Marcella, Maria Celeste Fatone, Elvira Favoino, & Federico Perosa. (2014). Raynaud's phenomenon: From molecular pathogenesis to therapy. Autoimmunity Reviews. 13(6). 655–667. 74 indexed citations
9.
Racanelli, Vito, Claudia Brunetti, Vallì De Re, et al.. (2011). Antibody Vh Repertoire Differences between Resolving and Chronically Evolving Hepatitis C Virus Infections. PLoS ONE. 6(9). e25606–e25606. 24 indexed citations
10.
Prete, Marcella, et al.. (2011). Extra-articular manifestations of rheumatoid arthritis: An update. Autoimmunity Reviews. 11(2). 123–131. 138 indexed citations
11.
Perosa, Federico, et al.. (2009). Two Structurally Different Rituximab-Specific CD20 Mimotope Peptides Reveal That Rituximab Recognizes Two Different CD20-Associated Epitopes. The Journal of Immunology. 182(1). 416–423. 27 indexed citations
12.
Perosa, Federico, Marcella Prete, Vito Racanelli, & Franco Dammacco. (2009). CD20‐depleting therapy in autoimmune diseases: from basic research to the clinic. Journal of Internal Medicine. 267(3). 260–277. 70 indexed citations
13.
14.
Perosa, Federico, et al.. (2005). CD20 Mimicry by a mAb Rituximab-Specific Linear Peptide: A Potential Tool for Active Immunotherapy of Autoimmune Diseases. Annals of the New York Academy of Sciences. 1051(1). 672–683. 14 indexed citations
15.
Prete, Marcella, Federico Perosa, Elvira Favoino, & Franco Dammacco. (2005). Biological therapy with monoclonal antibodies: a novel treatment approach to autoimmune disease. Clinical and Experimental Medicine. 5(4). 141–160. 10 indexed citations
16.
Perosa, Federico, et al.. (2003). β2-Microglobulin-Free HLA Class I Heavy Chain Epitope Mimicry by Monoclonal Antibody HC-10-Specific Peptide. The Journal of Immunology. 171(4). 1918–1926. 138 indexed citations
17.
Perosa, Federico, et al.. (1998). Evaluation of biotinylated cells as a source of antigens for characterization of their molecular profile. International Journal of Clinical & Laboratory Research. 28(4). 246–251. 3 indexed citations
18.
Perosa, Federico & Franco Dammacco. (1994). Anti-idiotypic monoclonal antibodies (mAb) to an anti-CD4 mAb induce CD4+ T cell depletion in rabbit. International Journal of Clinical & Laboratory Research. 24(4). 208–212. 2 indexed citations
19.
20.
Perosa, Federico & Soldano Ferrone. (1988). Syngeneic antiidiotypic monoclonal antibodies to the murine anti-HLA-DR,DP monoclonal antibody CR11-462. Human Immunology. 23(4). 255–269. 15 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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