Javier Pinilla‐Ibarz

12.9k total citations
236 papers, 5.8k citations indexed

About

Javier Pinilla‐Ibarz is a scholar working on Genetics, Hematology and Molecular Biology. According to data from OpenAlex, Javier Pinilla‐Ibarz has authored 236 papers receiving a total of 5.8k indexed citations (citations by other indexed papers that have themselves been cited), including 149 papers in Genetics, 114 papers in Hematology and 57 papers in Molecular Biology. Recurrent topics in Javier Pinilla‐Ibarz's work include Chronic Lymphocytic Leukemia Research (137 papers), Chronic Myeloid Leukemia Treatments (91 papers) and Lymphoma Diagnosis and Treatment (52 papers). Javier Pinilla‐Ibarz is often cited by papers focused on Chronic Lymphocytic Leukemia Research (137 papers), Chronic Myeloid Leukemia Treatments (91 papers) and Lymphoma Diagnosis and Treatment (52 papers). Javier Pinilla‐Ibarz collaborates with scholars based in United States, Italy and Germany. Javier Pinilla‐Ibarz's co-authors include Eduardo M. Sotomayor, David A. Scheinberg, Jörge E. Cortes, Fengdong Cheng, John J. Powers, Alejandro Villagra, Eva Sahakian, Tatyana Korontsvit, Rami S. Komrokji and Pearlie K. Epling‐Burnette and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and Blood.

In The Last Decade

Javier Pinilla‐Ibarz

223 papers receiving 5.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Javier Pinilla‐Ibarz United States 41 2.6k 2.3k 1.9k 1.8k 1.4k 236 5.8k
Jeffrey Tyner United States 46 2.9k 1.1× 2.1k 0.9× 3.3k 1.7× 1.3k 0.7× 1.3k 0.9× 238 7.1k
John Mascarenhas United States 40 3.2k 1.2× 3.7k 1.7× 2.7k 1.4× 915 0.5× 1.2k 0.8× 358 6.4k
Philippe Martiat Belgium 38 1.6k 0.6× 1.4k 0.6× 2.0k 1.1× 1.3k 0.8× 932 0.7× 110 5.6k
Nikolas von Bubnoff Germany 31 2.0k 0.7× 1.3k 0.6× 1.1k 0.6× 1.1k 0.6× 708 0.5× 138 4.1k
Katherine R. Calvo United States 39 2.4k 0.9× 1.4k 0.6× 1.7k 0.9× 843 0.5× 1.3k 0.9× 141 5.1k
Ronald Paquette United States 33 3.2k 1.2× 2.4k 1.0× 1.5k 0.8× 610 0.3× 757 0.5× 119 5.0k
Martin Höglund Sweden 37 3.4k 1.3× 1.6k 0.7× 1.5k 0.8× 1.2k 0.7× 591 0.4× 134 5.1k
Steffen Koschmieder Germany 37 2.4k 0.9× 1.7k 0.7× 2.8k 1.5× 730 0.4× 708 0.5× 222 5.2k
Ali G. Turhan France 37 2.0k 0.8× 1.4k 0.6× 1.5k 0.8× 1.0k 0.6× 473 0.3× 184 4.0k
Johan Richter Sweden 36 1.7k 0.7× 1.2k 0.5× 1.5k 0.8× 851 0.5× 773 0.5× 145 4.2k

Countries citing papers authored by Javier Pinilla‐Ibarz

Since Specialization
Citations

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

Fields of papers citing papers by Javier Pinilla‐Ibarz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Javier Pinilla‐Ibarz. 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 Javier Pinilla‐Ibarz. The network helps show where Javier Pinilla‐Ibarz may publish in the future.

Co-authorship network of co-authors of Javier Pinilla‐Ibarz

This figure shows the co-authorship network connecting the top 25 collaborators of Javier Pinilla‐Ibarz. A scholar is included among the top collaborators of Javier Pinilla‐Ibarz 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 Javier Pinilla‐Ibarz. Javier Pinilla‐Ibarz 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.
Davids, Matthew S., Saad S. Kenderian, Ian W. Flinn, et al.. (2025). ZUMA-8: a phase 1 study of brexucabtagene autoleucel in patients with relapsed/refractory chronic lymphocytic leukemia. Blood. 146(8). 938–943. 2 indexed citations
2.
Mediavilla-Varela, Melanie, John J. Powers, Julio C. Chávez, et al.. (2025). Targeting S100A9-mediated inflammation: a novel therapeutic approach for CLL. Blood Advances. 9(20). 5219–5233. 1 indexed citations
3.
Simon‐Molas, Helga, Melanie Mediavilla-Varela, Julio C. Chávez, et al.. (2025). Mitigating T-cell mitochondrial dysfunction in CLL to augment CAR T-cell therapy: evaluation in an immunocompetent model. Blood Advances. 9(10). 2511–2529. 3 indexed citations
4.
Jain, Akriti, Somedeb Ball, Onyee Chan, et al.. (2024). Incidence of pleural effusion with dasatinib and the effect of switching therapy to a different TKI in patients with chronic phase CML. Annals of Hematology. 103(6). 1941–1945. 5 indexed citations
5.
Tebbi, Cameron K., Eva Sahakian, Melanie Mediavilla-Varela, et al.. (2024). Mycovirus-Containing Aspergillus flavus Alters Transcription Factors in Normal and Acute Lymphoblastic Leukemia Cells. International Journal of Molecular Sciences. 25(19). 10361–10361. 1 indexed citations
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Atallah, Ehab, Michael S. Broder, Onyee Chan, et al.. (2024). U.S. Expert Consensus on Defining Intolerance to Tyrosine Kinase Inhibitor Treatment in Chronic Phase Chronic Myeloid Leukemia (CML). Blood. 144(Supplement 1). 5052–5052.
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Harro, Carly M., Kimberly B. Sprenger, Ricardo A. Chaurio, et al.. (2023). Sézary syndrome originates from heavily mutated hematopoietic progenitors. Blood Advances. 7(18). 5586–5602. 5 indexed citations
11.
Whiting, Junmin, Michael Jaglal, Hayder Saeed, et al.. (2023). A Retrospective Study on Outcomes of Secondary CNS Lymphoma: Pattern of Relapse, Prognostic Factors, and Role of Consolidation Therapy. Blood. 142(Supplement 1). 1750–1750.
12.
Dean, Erin, Rahul Mhaskar, Hong Lü, et al.. (2020). High metabolic tumor volume is associated with decreased efficacy of axicabtagene ciloleucel in large B-cell lymphoma. Blood Advances. 4(14). 3268–3276. 146 indexed citations
13.
Brayer, Jason, Allison Distler, Mark B. Meads, et al.. (2017). HDAC11 Is a Candidate Therapeutic Target in Multiple Myeloma. Blood. 130. 1800–1800. 1 indexed citations
14.
Ritchie, Ellen K., Rosalind Catchatourian, Rebecca B. Klisovic, et al.. (2017). Results from Enestgoal: A Phase 2 Study of Treatment-Free Remission (TFR) in Patients (pts) with Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Who Switched from Imatinib to Nilotinib. Blood. 130. 2875–2875. 7 indexed citations
15.
Kharfan‐Dabaja, Mohamed A., Ambuj Kumar, Farhad Khimani, et al.. (2017). Allogeneic Hematopoietic Cell Transplantation for Richter Syndrome: A Single-Center Experience. Clinical Lymphoma Myeloma & Leukemia. 18(1). e35–e39. 16 indexed citations
16.
Cheng, Fengdong, Hongwei Wang, Pedro Horna, et al.. (2012). Stat3 Inhibition Augments the Immunogenicity of B-cell Lymphoma Cells, Leading to Effective Antitumor Immunity. Cancer Research. 72(17). 4440–4448. 15 indexed citations
17.
O’Brien, Susan, Camille N. Abboud, Mojtaba Akhtari, et al.. (2012). Chronic Myelogenous Leukemia. Journal of the National Comprehensive Cancer Network. 10(1). 64–110. 20 indexed citations
18.
Dubovsky, Jason A., Douglas G. McNeel, John J. Powers, et al.. (2009). Treatment of Chronic Lymphocytic Leukemia with a Hypomethylating Agent Induces Expression of NXF2, an Immunogenic Cancer Testis Antigen. Clinical Cancer Research. 15(10). 3406–3415. 43 indexed citations
19.
O’Brien, Susan, Ellin Berman, Hossein Borghaei, et al.. (2009). Chronic Myelogenous Leukemia. Journal of the National Comprehensive Cancer Network. 7(9). 984–1023. 175 indexed citations
20.
Kappel, Barry J., Javier Pinilla‐Ibarz, Adam A. Kochman, et al.. (2005). Remodeling specific immunity by use of MHC tetramers: demonstration in a graft-versus-host disease model. Blood. 107(5). 2045–2051. 12 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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