John Maher

10.7k total citations · 2 hit papers
188 papers, 7.8k citations indexed

About

John Maher is a scholar working on Oncology, Immunology and Biomedical Engineering. According to data from OpenAlex, John Maher has authored 188 papers receiving a total of 7.8k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Oncology, 66 papers in Immunology and 29 papers in Biomedical Engineering. Recurrent topics in John Maher's work include CAR-T cell therapy research (89 papers), Immunotherapy and Immune Responses (32 papers) and Immune Cell Function and Interaction (30 papers). John Maher is often cited by papers focused on CAR-T cell therapy research (89 papers), Immunotherapy and Immune Responses (32 papers) and Immune Cell Function and Interaction (30 papers). John Maher collaborates with scholars based in United Kingdom, United States and Ireland. John Maher's co-authors include David M. Davies, Michel Sadelain, Renier J. Brentjens, Gertrude Gunset, Isabelle Rivière, Lynsey M. Whilding, May CI van Schalkwyk, Scott Wilkie, Ana C. Parente‐Pereira and Daniela Achkova and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and The Lancet.

In The Last Decade

John Maher

181 papers receiving 7.5k citations

Hit Papers

Human T-lymphocyte cytoto... 2002 2026 2010 2018 2002 2021 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
John Maher 4.5k 2.7k 1.9k 1.7k 1.3k 188 7.8k
Richard Aplenc 8.1k 1.8× 2.9k 1.1× 3.3k 1.8× 1.8k 1.0× 2.2k 1.7× 320 13.3k
Michael von Bergwelt‐Baildon 3.4k 0.8× 2.3k 0.8× 1.1k 0.6× 496 0.3× 366 0.3× 214 6.3k
Michael Wang 8.2k 1.8× 2.6k 1.0× 5.9k 3.1× 532 0.3× 674 0.5× 554 16.3k
John A. Thompson 9.2k 2.0× 4.3k 1.6× 3.4k 1.8× 666 0.4× 667 0.5× 111 12.4k
Gerardo Botti 6.7k 1.5× 1.8k 0.7× 5.2k 2.8× 500 0.3× 679 0.5× 541 14.9k
Leo I. Gordon 6.6k 1.5× 2.2k 0.8× 2.3k 1.2× 363 0.2× 603 0.5× 453 15.0k
Depei Wu 2.0k 0.4× 1.5k 0.6× 1.7k 0.9× 469 0.3× 359 0.3× 512 6.5k
Eugene P. Frenkel 2.6k 0.6× 686 0.3× 3.1k 1.7× 1.2k 0.7× 563 0.4× 203 9.5k
Thomas Eigentler 6.3k 1.4× 2.5k 0.9× 3.4k 1.8× 346 0.2× 338 0.3× 264 9.2k
So Yeon Park 3.5k 0.8× 947 0.3× 3.4k 1.8× 570 0.3× 843 0.6× 488 11.8k

Countries citing papers authored by John Maher

Since Specialization
Citations

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

Fields of papers citing papers by John Maher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Maher

This figure shows the co-authorship network connecting the top 25 collaborators of John Maher. A scholar is included among the top collaborators of John Maher 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 John Maher. John Maher 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.
Larcombe-Young, Daniel, Fahima Kausar, Caroline Hull, et al.. (2024). Solid tumor immunotherapy using NKG2D-based adaptor CAR T cells. Cell Reports Medicine. 5(11). 101827–101827. 12 indexed citations
2.
Hull, Caroline, Daniel Larcombe-Young, Roberta Mazza, et al.. (2024). Granzyme B-activated IL18 potentiates αβ and γδ CAR T cell immunotherapy in a tumor-dependent manner. Molecular Therapy. 32(7). 2373–2392. 18 indexed citations
3.
Davies, David M., et al.. (2024). Engineering a Dual Specificity γδ T-Cell Receptor for Cancer Immunotherapy. Biology. 13(3). 196–196. 2 indexed citations
4.
Taylor, Chelsea, et al.. (2024). CAR-T cell technologies that interact with the tumour microenvironment in solid tumours. Expert Review of Clinical Immunology. 20(8). 849–871. 4 indexed citations
5.
Maher, John & David M. Davies. (2023). CAR-Based Immunotherapy of Solid Tumours—A Survey of the Emerging Targets. Cancers. 15(4). 1171–1171. 17 indexed citations
6.
Gabizón, Alberto, Hilary Shmeeda, Benjamin Draper, et al.. (2023). Harnessing Nanomedicine to Potentiate the Chemo-Immunotherapeutic Effects of Doxorubicin and Alendronate Co-Encapsulated in Pegylated Liposomes. Pharmaceutics. 15(11). 2606–2606. 3 indexed citations
7.
Maher, John. (2023). Solid tumours: Building bridges to CAR‐T success. SHILAP Revista de lepidopterología. 3(2). 2 indexed citations
8.
Maher, John. (2023). Chimeric Antigen Receptor (CAR) T-Cell Therapy for Patients with Lung Cancer: Current Perspectives. OncoTargets and Therapy. Volume 16. 515–532. 7 indexed citations
9.
Achkova, Daniela, Richard Beatson, & John Maher. (2022). CAR T-Cell Targeting of Macrophage Colony-Stimulating Factor Receptor. Cells. 11(14). 2190–2190. 8 indexed citations
10.
Parente‐Pereira, Ana C., Richard Beatson, David M. Davies, et al.. (2022). Generation and application of TGFβ-educated human Vγ9Vδ2 T cells. STAR Protocols. 3(2). 101319–101319. 3 indexed citations
11.
Halim, Leena, Farideh Miraki‐Moud, David Taussig, et al.. (2021). Priming Death Receptor Mediated Apoptosis with Arginine Starvation Sensitises Arginine Auxotrophic B-ALL to CAR-T. Blood. 138(Supplement 1). 2787–2787.
12.
Shrotri, Madhumita, May CI van Schalkwyk, Nathan Post, et al.. (2020). Cellular immune response to SARS-CoV-2 infection in humans: a systematic review. medRxiv. 4 indexed citations
13.
Kosti, Paris, et al.. (2020). Editor's Pick: Tumour-Associated Hypoxia: Can We Give Chimeric Antigen Receptor T Cells More Breathing Space?. SHILAP Revista de lepidopterología. 1 indexed citations
14.
Yazdanifar, Mahboubeh, Ru Zhou, Priyanka Grover, et al.. (2019). Overcoming Immunological Resistance Enhances the Efficacy of a Novel Anti-tMUC1-CAR T Cell Treatment against Pancreatic Ductal Adenocarcinoma. Cells. 8(9). 1070–1070. 49 indexed citations
15.
Mortlock, Thomas, et al.. (2018). Extreme water levels, waves and coastal impacts during a severe tropical cyclone in northeastern Australia: a case study for cross-sector data sharing. Natural hazards and earth system sciences. 18(9). 2603–2623. 10 indexed citations
16.
Petrovic, Roseanna M., Daniela Achkova, Tomasz Zabinski, et al.. (2017). CAR T-cell immunotherapy of MET-expressing malignant mesothelioma. OncoImmunology. 6(12). e1363137–e1363137. 52 indexed citations
17.
Gilham, David E. & John Maher. (2017). ‘Atypical’ CAR T Cells: NKG2D and Erb-B as Examples of Natural Receptor/Ligands to Target Recalcitrant Solid Tumors. Immunotherapy. 9(9). 723–733. 11 indexed citations
18.
Schalkwyk, May CI van, Sophie Papa, Jean‐Pierre Jeannon, et al.. (2013). Design of a Phase I Clinical Trial to Evaluate Intratumoral Delivery of ErbB-Targeted Chimeric Antigen Receptor T-Cells in Locally Advanced or Recurrent Head and Neck Cancer. PubMed. 24(3). 134–142. 105 indexed citations
19.
Davies, David M., Julie Foster, Sjoukje J. C. van der Stegen, et al.. (2012). Flexible Targeting of ErbB Dimers That Drive Tumorigenesis by Using Genetically Engineered T Cells. Molecular Medicine. 18(4). 565–576. 95 indexed citations
20.
Taylor, Addison A., et al.. (1971). Renal function following cortical necrosis in childhood. The Journal of Pediatrics. 79(2). 267–275. 9 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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