Veit Buecklein

642 total citations
32 papers, 230 citations indexed

About

Veit Buecklein is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Veit Buecklein has authored 32 papers receiving a total of 230 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Oncology, 12 papers in Molecular Biology and 10 papers in Immunology. Recurrent topics in Veit Buecklein's work include CAR-T cell therapy research (23 papers), Acute Myeloid Leukemia Research (5 papers) and Viral Infectious Diseases and Gene Expression in Insects (5 papers). Veit Buecklein is often cited by papers focused on CAR-T cell therapy research (23 papers), Acute Myeloid Leukemia Research (5 papers) and Viral Infectious Diseases and Gene Expression in Insects (5 papers). Veit Buecklein collaborates with scholars based in Germany, United States and Spain. Veit Buecklein's co-authors include Marion Subklewe, Suresh Agarwal, Mojca Jongen‐Lavrencic, Rolf D. Issels, Roland B. Walter, Anthony S. Stein, Gert J. Ossenkoppele, Farhad Ravandi, Sophia K. Khaldoyanidi and Hagop M. Kantarjian and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Cancer Research.

In The Last Decade

Veit Buecklein

28 papers receiving 226 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Veit Buecklein Germany 7 160 69 68 62 44 32 230
Nicholas D’Cunha United States 9 151 0.9× 49 0.7× 85 1.3× 103 1.7× 37 0.8× 32 282
David M. Cordas dos Santos Germany 8 251 1.6× 49 0.7× 82 1.2× 45 0.7× 16 0.4× 16 304
Sanbin Wang China 9 137 0.9× 102 1.5× 74 1.1× 52 0.8× 10 0.2× 31 259
Elizabeth Budde United States 9 161 1.0× 88 1.3× 105 1.5× 48 0.8× 14 0.3× 26 275
Azam Ghafoor United States 7 84 0.5× 23 0.3× 59 0.9× 43 0.7× 90 2.0× 15 221
Sandra B. Horowitz United States 9 227 1.4× 78 1.1× 77 1.1× 65 1.0× 12 0.3× 14 305
Bryan Do United States 6 187 1.2× 32 0.5× 49 0.7× 53 0.9× 17 0.4× 7 288
Lida Bubuteishvili‐Pacaud United States 8 316 2.0× 71 1.0× 84 1.2× 32 0.5× 11 0.3× 21 357
Christoph Maintz Germany 6 70 0.4× 39 0.6× 81 1.2× 25 0.4× 27 0.6× 14 187
Gonzalo Carreño‐Tarragona Spain 9 94 0.6× 84 1.2× 81 1.2× 107 1.7× 8 0.2× 32 290

Countries citing papers authored by Veit Buecklein

Since Specialization
Citations

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

Fields of papers citing papers by Veit Buecklein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Veit Buecklein

This figure shows the co-authorship network connecting the top 25 collaborators of Veit Buecklein. A scholar is included among the top collaborators of Veit Buecklein 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 Veit Buecklein. Veit Buecklein 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.
Díaz, Juan Manuel Mancilla, Saurabh Dahiya, Maeve O’Reilly, et al.. (2024). Cohort Comparison of Therapy-Related Myeloid Neoplasms after CAR T-Cell Therapy, Autologous Stem Cell Transplant, and Standard Cancer Therapies. Blood. 144(Supplement 1). 7239–7239.
2.
Heitmann, Jonas S., Juliane S. Walz, Helmut R. Salih, et al.. (2024). A Phase 1, Open-Label, Dose Escalation and Dose Expansion Study of Cln-049 for the Treatment of Acute Myeloid Leukemia Patients with Measurable Residual Disease. Blood. 144(Supplement 1). 2883.1–2883.1. 1 indexed citations
3.
Buecklein, Veit, Francis Ayuk, Tobias A.W. Holderried, et al.. (2024). Estimating the Impact on Survival of Not Receiving CAR T Therapy Despite Being Eligible in Relapsed or Refractory (R/R) Diffuse Large B-Cell Lymphoma (DLBCL) Patients in Germany. Blood. 144(Supplement 1). 5037–5037. 1 indexed citations
4.
Ravandi, Farhad, Marion Subklewe, Roland B. Walter, et al.. (2024). Safety and tolerability of AMG 330 in adults with relapsed/refractory AML: a phase 1a dose-escalation study. Leukemia & lymphoma. 65(9). 1281–1291. 12 indexed citations
5.
Rohrbacher, Lisa, Daniel Nixdorf, Bettina Brauchle, et al.. (2023). Two Players, One Goal: BiTE ® Vs CART Targeting FLT3 in AML. Blood. 142(Supplement 1). 3444–3444. 1 indexed citations
6.
Marcinek, Anetta, Tobias Straub, Gerulf Hänel, et al.. (2023). Beyond Mitigating Immune-Related Adverse Events: Glucocorticoids Ameliorate Bispecific Antibody-Mediated T-Cell Exhaustion I n Vitro. Blood. 142(Supplement 1). 5389–5389. 1 indexed citations
7.
Rejeski, Kai, Doris K. Hansen, Yucai Wang, et al.. (2023). Applying the Novel EHA/EBMT Grading System for Icaht Following CAR-T Therapy: Comparative Incidence across Disease Entities and Association with Infections and Mortality. Blood. 142(Supplement 1). 359–359. 1 indexed citations
10.
Rohrbacher, Lisa, Daniel Nixdorf, Bettina Brauchle, et al.. (2022). The Race Is on: BiTE Vs CART As FLT3-Directed Immunotherapies in AML. Blood. 140(Supplement 1). 10233–10234. 1 indexed citations
11.
Rejeski, Kai, David M. Cordas dos Santos, Lian Liu, et al.. (2022). Body Composition and Immunonutritional Status Impact Survival Outcomes after CD19 CAR T-Cell Therapy. Blood. 140(Supplement 1). 10405–10406. 2 indexed citations
13.
14.
Hänel, Gerulf, Vesna Pulko, Christina Claus, et al.. (2021). Augmenting Efficacy of T-Cell Bispecific Antibodies in AML through a Tumor Stroma-Targeted 4-1BB Agonist. Blood. 138(Supplement 1). 1178–1178. 1 indexed citations
15.
Marcinek, Anetta, Bettina Brauchle, Gerulf Hänel, et al.. (2021). CD33 BiTE ® Construct Mediated Immunological Synapse Formation and Downstream Signaling in T Cells Is Dependent on Expression of Costimulatory Molecules on Target Cells. Blood. 138(Supplement 1). 2237–2237. 1 indexed citations
16.
Orth, Martin F., Veit Buecklein, Eric Kampmann, et al.. (2020). A comparative view on the expression patterns of PD-L1 and PD-1 in soft tissue sarcomas. Cancer Immunology Immunotherapy. 69(7). 1353–1362. 33 indexed citations
17.
Blumenberg, Viktoria, Maria‐Luisa Schubert, Eli Zamir, et al.. (2020). Antibiotic Therapy and Low Gut Microbiome Diversity Is Associated with Decreased Response and High Toxicity in BCP-ALL and DLBCL Patients after Treatment with CD19. CAR T-Cells. Blood. 136(Supplement 1). 33–34. 13 indexed citations
18.
Rejeski, Kai, Carolina Berger, Semjon Willier, et al.. (2020). Identification of Predictive Markers of Severe and Prolonged Neutropenia after CD19-Specific CAR T-Cell Treatment in Patients with Relapsed/Refractory B-Cell Malignancies. Blood. 136(Supplement 1). 41–42. 1 indexed citations
19.
Hänel, Gerulf, Maryam Kazerani, Veit Buecklein, et al.. (2020). Treatment-Free Intervals Mitigate T-Cell Exhaustion Induced By Continuous CD19xCD3-BiTE® Construct Stimulation in Vitro. Blood. 136(Supplement 1). 44–45. 2 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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