Matteo Da Vià

2.6k total citations
34 papers, 738 citations indexed

About

Matteo Da Vià is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Matteo Da Vià has authored 34 papers receiving a total of 738 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Hematology, 19 papers in Molecular Biology and 14 papers in Oncology. Recurrent topics in Matteo Da Vià's work include Multiple Myeloma Research and Treatments (20 papers), Protein Degradation and Inhibitors (9 papers) and Acute Myeloid Leukemia Research (7 papers). Matteo Da Vià is often cited by papers focused on Multiple Myeloma Research and Treatments (20 papers), Protein Degradation and Inhibitors (9 papers) and Acute Myeloid Leukemia Research (7 papers). Matteo Da Vià collaborates with scholars based in Italy, Germany and United Kingdom. Matteo Da Vià's co-authors include Antonio Giovanni Solimando, Mario Cazzola, Emanuela Boveri, Hermann Einsele, Ilaria Ambaglio, Erica Travaglino, Marta Ubezio, Matteo Giovanni Della Porta, K. Martin Kortüm and Anna Gallì and has published in prestigious journals such as Blood, International Journal of Molecular Sciences and Frontiers in Immunology.

In The Last Decade

Matteo Da Vià

32 papers receiving 735 citations

Peers

Matteo Da Vià
Paulo Lúcio Portugal
Alison R. Sehgal United States
Anna van Rhenen Netherlands
Rose Beck United States
Kun Ru China
Matteo Da Vià
Citations per year, relative to Matteo Da Vià Matteo Da Vià (= 1×) peers Ilana Zalcberg

Countries citing papers authored by Matteo Da Vià

Since Specialization
Citations

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

Fields of papers citing papers by Matteo Da Vià

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matteo Da Vià. 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 Matteo Da Vià. The network helps show where Matteo Da Vià may publish in the future.

Co-authorship network of co-authors of Matteo Da Vià

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Da Vià. A scholar is included among the top collaborators of Matteo Da Vià 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 Matteo Da Vià. Matteo Da Vià 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.
Fattizzo, Bruno, Matteo Da Vià, Francesca Lazzaroni, et al.. (2025). Bone marrow microenvironment in autoimmune hemolytic anemia: from trephine biopsy to single cell RNA sequencing. Signal Transduction and Targeted Therapy. 10(1). 277–277.
2.
Leblay, Noémie, Sungwoo Ahn, Holly Lee, et al.. (2024). Immune and Tumor Mediators of Resistance to Daratumumab-IMiDs Based Therapies in Relapsed Multiple Myeloma. Blood. 144(Supplement 1). 4642–4642.
3.
Duell, Johannes, Alexander M. Leipold, Silke Appenzeller, et al.. (2023). Sequential antigen loss and branching evolution in lymphoma after CD19- and CD20-targeted T-cell–redirecting therapy. Blood. 143(8). 685–696. 25 indexed citations
4.
Solimando, Antonio Giovanni, Vanessa Desantis, & Matteo Da Vià. (2022). Visualizing the Interactions Shaping the Imaging of the Microenvironment in Human Cancers. Methods in molecular biology. 2572. 67–79. 3 indexed citations
5.
Fattizzo, Bruno, Francesca Cavallaro, Juri Alessandro Giannotta, et al.. (2022). Seroconversion to mRNA SARS-CoV-2 Vaccines in Hematologic Patients. Frontiers in Immunology. 13. 852158–852158. 10 indexed citations
6.
Botta, Cirino, Bruno Paiva, Marco Santoro, et al.. (2022). Network meta‐analysis of randomized trials in multiple myeloma: Efficacy and safety in frontline therapy for patients not eligible for transplant. Hematological Oncology. 40(5). 987–998. 7 indexed citations
7.
Bianchi, Giada, Peter G. Czarnecki, Matthew Ho, et al.. (2021). ROBO1 Promotes Homing, Dissemination, and Survival of Multiple Myeloma within the Bone Marrow Microenvironment. Blood Cancer Discovery. 2(4). 338–353. 12 indexed citations
8.
Brandl, Andreas, Antonio Giovanni Solimando, Zeinab Mokhtari, et al.. (2021). Junctional adhesion molecule C expression specifies a CD138low/neg multiple myeloma cell population in mice and humans. Blood Advances. 6(7). 2195–2206. 8 indexed citations
9.
Vià, Matteo Da, Marta Lionetti, Erica Travaglino, et al.. (2021). MGUS and Chip: Two Faces, but Not of the Same Medal. Blood. 138(Supplement 1). 3800–3800. 3 indexed citations
10.
Wiercinska, Eliza, Götz Ulrich Grigoleit, Alessandro Mazzoni, et al.. (2020). Progressive multifocal leukoencephalopathy in a patient post allo-HCT successfully treated with JC virus specific donor lymphocytes. Journal of Translational Medicine. 18(1). 177–177. 28 indexed citations
11.
Borlenghi, Erika, Chiara Pagani, Patrizia Zappasodi, et al.. (2020). Validation of the “fitness criteria” for the treatment of older patients with acute myeloid leukemia: A multicenter study on a series of 699 patients by the Network Rete Ematologica Lombarda (REL). Journal of Geriatric Oncology. 12(4). 550–556. 13 indexed citations
12.
Lapa, Constantin, Malte Kircher, Matteo Da Vià, et al.. (2019). Comparison of 11C-Choline and 11C-Methionine PET/CT in Multiple Myeloma. Clinical Nuclear Medicine. 44(8). 620–624. 27 indexed citations
13.
Lapa, Constantin, Malte Kircher, Matteo Da Vià, et al.. (2019). Comparison of 11C-Choline and 11C-Methionine-PET/CT in Multiple Myeloma. Nuklearmedizin - NuclearMedicine. 2 indexed citations
14.
Vià, Matteo Da, Antonio Giovanni Solimando, Andoni Garitano-Trojaola, et al.. (2019). CIC Mutation as a Molecular Mechanism of Acquired Resistance to Combined BRAF-MEK Inhibition in Extramedullary Multiple Myeloma with Central Nervous System Involvement. The Oncologist. 25(2). 112–118. 40 indexed citations
15.
16.
Kircher, Stefan, K. Martin Kortüm, Malte Kircher, et al.. (2018). Hexokinase-2 Expression in 11C-Methionine–Positive, 18F-FDG–Negative Multiple Myeloma. Journal of Nuclear Medicine. 60(3). 348–352. 20 indexed citations
17.
Vià, Matteo Da, Antonio Giovanni Solimando, Andoni Garitano-Trojaola, et al.. (2018). CIC-Mutation As a Potential Molecular Mechanism of Acquired Resistance to Combined BRAF/MEK Inhibition in CNS Multiple Myeloma. Blood. 132(Supplement 1). 3181–3181. 2 indexed citations
18.
Cattaneo, Chiara, Patrizia Zappasodi, Valentina Mancini, et al.. (2016). Emerging resistant bacteria strains in bloodstream infections of acute leukaemia patients: results of a prospective study by the Rete Ematologica Lombarda (Rel). Annals of Hematology. 95(12). 1955–1963. 21 indexed citations
19.
Ambaglio, Ilaria, Luca Malcovati, Elli Papaemmanuil, et al.. (2013). Inappropriately low hepcidin levels in patients with myelodysplastic syndrome carrying a somatic mutation of SF3B1. Haematologica. 98(3). 420–423. 45 indexed citations
20.
Pietra, Daniela, Ilaria Carola Casetti, Matteo Da Vià, et al.. (2012). JAK2 GGCC haplotype in MPL mutated myeloproliferative neoplasms. American Journal of Hematology. 87(7). 746–747. 7 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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