Rahul Purwar

1.9k total citations
38 papers, 1.4k citations indexed

About

Rahul Purwar is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Rahul Purwar has authored 38 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Immunology, 13 papers in Molecular Biology and 12 papers in Oncology. Recurrent topics in Rahul Purwar's work include CAR-T cell therapy research (9 papers), Immunotherapy and Immune Responses (6 papers) and T-cell and B-cell Immunology (6 papers). Rahul Purwar is often cited by papers focused on CAR-T cell therapy research (9 papers), Immunotherapy and Immune Responses (6 papers) and T-cell and B-cell Immunology (6 papers). Rahul Purwar collaborates with scholars based in India, Germany and United States. Rahul Purwar's co-authors include Miriam Wittmann, Rachael A. Clark, Thomas S. Kupper, Thomas Werfel, William G. Richards, George F. Murphy, James J. Campbell, Ralf Gutzmer, Atharva Karulkar and Samia J. Khoury and has published in prestigious journals such as Nature Medicine, Blood and The Journal of Immunology.

In The Last Decade

Rahul Purwar

32 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rahul Purwar India 17 792 375 279 223 136 38 1.4k
Andrea Tuettenberg Germany 21 1.8k 2.3× 555 1.5× 452 1.6× 109 0.5× 80 0.6× 53 2.4k
Kerstin Siegmund Austria 17 1.5k 1.9× 380 1.0× 280 1.0× 73 0.3× 132 1.0× 35 1.9k
Belinda Palermo Italy 20 1.4k 1.8× 705 1.9× 304 1.1× 91 0.4× 90 0.7× 42 1.9k
Niklas Czeloth Germany 17 1.4k 1.8× 428 1.1× 371 1.3× 83 0.4× 112 0.8× 20 1.8k
Sofia V. Gearty United States 5 563 0.7× 257 0.7× 276 1.0× 192 0.9× 58 0.4× 6 1.1k
Margarete Schön Germany 18 483 0.6× 266 0.7× 324 1.2× 154 0.7× 163 1.2× 26 1.1k
Marc A. Becker Germany 21 1.2k 1.5× 430 1.1× 460 1.6× 53 0.2× 178 1.3× 37 2.0k
Clemens Esche United States 21 1.0k 1.3× 587 1.6× 415 1.5× 111 0.5× 83 0.6× 34 1.5k
Katalin Ferenczi United States 16 564 0.7× 282 0.8× 267 1.0× 678 3.0× 89 0.7× 42 1.3k
Adrian Tun-Kyi United States 12 1.5k 2.0× 475 1.3× 434 1.6× 455 2.0× 65 0.5× 15 1.9k

Countries citing papers authored by Rahul Purwar

Since Specialization
Citations

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

Fields of papers citing papers by Rahul Purwar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rahul Purwar

This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Purwar. A scholar is included among the top collaborators of Rahul Purwar 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 Rahul Purwar. Rahul Purwar 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.
Chatterjee, Abhishek, et al.. (2025). Mass spectrometry-based proteomics in glioblastoma – current understanding and future impact. Brain Research. 1866. 149890–149890.
2.
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Goda, Jayant Sastri, Atharva Karulkar, Nirali N. Shah, et al.. (2024). Development of a Novel Humanized BCMA-Directed CAR for Multiple Myeloma: Robust Activity and Low Toxicity in Preclinical Studies. Blood. 144(Supplement 1). 4809–4809.
4.
Chaudhary, Namit, Lisa N. Kasiewicz, Mariah L. Arral, et al.. (2024). Amine headgroups in ionizable lipids drive immune responses to lipid nanoparticles by binding to the receptors TLR4 and CD1d. Nature Biomedical Engineering. 8(11). 1483–1498. 49 indexed citations
5.
Chatterjee, Abhishek, et al.. (2023). Oncolytic immunovirotherapy for high-grade gliomas: A novel and an evolving therapeutic option. Frontiers in Immunology. 14. 1118246–1118246. 31 indexed citations
6.
Purwar, Rahul, et al.. (2023). CAR‐T cell therapy in hematological malignancies: Where are we now and where are we heading for?. European Journal Of Haematology. 112(1). 6–18. 37 indexed citations
7.
Attrish, Diksha, Sushant Kumar, K. V. Venkatesh, et al.. (2023). IL-17A Orchestrates Reactive Oxygen Species/HIF1α–Mediated Metabolic Reprogramming in Psoriasis. The Journal of Immunology. 212(2). 302–316. 11 indexed citations
8.
Kumar, Sushant, Sarbari Ghosh, Farhat Ali Khan, et al.. (2021). Multiomics Analysis and Systems Biology Integration Identifies the Roles of IL-9 in Keratinocyte Metabolic Reprogramming. Journal of Investigative Dermatology. 141(8). 1932–1942. 11 indexed citations
9.
Kumar, Sushant, Sarbari Ghosh, Alka Dwivedi, et al.. (2020). The Th9 Axis Reduces the Oxidative Stress and Promotes the Survival of Malignant T Cells in Cutaneous T-Cell Lymphoma Patients. Molecular Cancer Research. 18(4). 657–668. 23 indexed citations
10.
Kumar, Sushant, et al.. (2020). 889 Interleukin-9 promotes malignant T cell survival by inhibiting oxidative stress and lactic acidosis in cutaneous T cell lymphoma. Journal of Investigative Dermatology. 140(7). S117–S117.
11.
Dwivedi, Alka, Sushant Kumar, & Rahul Purwar. (2017). B16 Lung Melanoma Model to Study the Role of Th9 Cells in Cancer. Methods in molecular biology. 1585. 217–222. 2 indexed citations
12.
Purwar, Rahul, Christoph Schlapbach, Sheng Xiao, et al.. (2012). Robust tumor immunity to melanoma mediated by interleukin-9–producing T cells. Nature Medicine. 18(8). 1248–1253. 319 indexed citations
13.
Purwar, Rahul, Wolfgang Bäumer, Margarete Niebuhr, et al.. (2011). A protective role of complement component 3 in T cell‐mediated skin inflammation. Experimental Dermatology. 20(9). 709–714. 16 indexed citations
14.
Purwar, Rahul, James J. Campbell, George F. Murphy, et al.. (2011). Resident Memory T Cells (TRM) Are Abundant in Human Lung: Diversity, Function, and Antigen Specificity. PLoS ONE. 6(1). e16245–e16245. 235 indexed citations
15.
Gschwandtner, Maria, Rahul Purwar, Miriam Wittmann, et al.. (2008). Histamine Upregulates Keratinocyte MMP-9 Production via the Histamine H1 Receptor. Journal of Investigative Dermatology. 128(12). 2783–2791. 40 indexed citations
16.
17.
Purwar, Rahul, et al.. (2007). Modulation of Keratinocyte-Derived MMP-9 by IL-13: A Possible Role for the Pathogenesis of Epidermal Inflammation. Journal of Investigative Dermatology. 128(1). 59–66. 61 indexed citations
18.
Purwar, Rahul, Miriam Wittmann, Jörg Zwirner, et al.. (2006). Induction of C3 and CCL2 by C3a in Keratinocytes: A Novel Autocrine Amplification Loop of Inflammatory Skin Reactions. The Journal of Immunology. 177(7). 4444–4450. 38 indexed citations
19.
Gutzmer, Ralf, Brigitta Köther, Jörg Zwirner, et al.. (2006). Human Plasmacytoid Dendritic Cells Express Receptors for Anaphylatoxins C3a and C5a and Are Chemoattracted to C3a and C5a. Journal of Investigative Dermatology. 126(11). 2422–2429. 49 indexed citations
20.
Purwar, Rahul, Thomas Werfel, & Miriam Wittmann. (2006). IL-13-Stimulated Human Keratinocytes Preferentially Attract CD4+CCR4+ T cells: Possible Role in Atopic Dermatitis. Journal of Investigative Dermatology. 126(5). 1043–1051. 73 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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