Sunil Kumar Saini

5.9k total citations
30 papers, 1.1k citations indexed

About

Sunil Kumar Saini is a scholar working on Oncology, Immunology and Molecular Biology. According to data from OpenAlex, Sunil Kumar Saini has authored 30 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 10 papers in Immunology and 6 papers in Molecular Biology. Recurrent topics in Sunil Kumar Saini's work include Immunotherapy and Immune Responses (9 papers), T-cell and B-cell Immunology (7 papers) and CAR-T cell therapy research (5 papers). Sunil Kumar Saini is often cited by papers focused on Immunotherapy and Immune Responses (9 papers), T-cell and B-cell Immunology (7 papers) and CAR-T cell therapy research (5 papers). Sunil Kumar Saini collaborates with scholars based in Denmark, United States and India. Sunil Kumar Saini's co-authors include Timothy M. Wick, Sine Reker Hadrup, Kenneth Williams, Sebastian Springer, Tripti Tamhane, Ditte Stampe Hersby, Anne Ortved Gang, Martin Zacharias, Helle Rus Povlsen and Morten Nielsen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Sunil Kumar Saini

27 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sunil Kumar Saini Denmark 14 502 365 258 196 137 30 1.1k
Christine Wolf Germany 18 693 1.4× 636 1.7× 164 0.6× 190 1.0× 98 0.7× 36 1.4k
Marius Strioga Lithuania 15 594 1.2× 328 0.9× 279 1.1× 142 0.7× 301 2.2× 21 1.6k
Yutaka Kawano Japan 13 503 1.0× 424 1.2× 298 1.2× 50 0.3× 189 1.4× 52 1.7k
Nobukazu Watanabe Japan 20 549 1.1× 267 0.7× 140 0.5× 63 0.3× 138 1.0× 51 1.2k
Reinhart Seibl Switzerland 11 625 1.2× 233 0.6× 210 0.8× 217 1.1× 109 0.8× 12 1.3k
W. Scott Goebel United States 21 317 0.6× 539 1.5× 221 0.9× 254 1.3× 438 3.2× 55 1.8k
Lyndah Chow United States 17 445 0.9× 344 0.9× 322 1.2× 72 0.4× 296 2.2× 62 1.3k
Pontus Blomberg Sweden 22 412 0.8× 836 2.3× 219 0.8× 132 0.7× 375 2.7× 44 1.6k
Toshiyuki Owaki Japan 17 959 1.9× 202 0.6× 500 1.9× 107 0.5× 117 0.9× 24 1.5k
Jianmin Fang China 11 113 0.2× 572 1.6× 131 0.5× 185 0.9× 163 1.2× 20 1.1k

Countries citing papers authored by Sunil Kumar Saini

Since Specialization
Citations

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

Fields of papers citing papers by Sunil Kumar Saini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunil Kumar Saini. 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 Sunil Kumar Saini. The network helps show where Sunil Kumar Saini may publish in the future.

Co-authorship network of co-authors of Sunil Kumar Saini

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Kumar Saini. A scholar is included among the top collaborators of Sunil Kumar Saini 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 Sunil Kumar Saini. Sunil Kumar Saini 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.
Müller, Thomas, Takuya Sekine, Julia Niessl, et al.. (2023). Anamnestic expansion of Omicron-reactive CD8+ T cells after booster SARS-CoV-2 mRNA vaccination across different immunocompromised states. The Journal of Immunology. 210(Supplement_1). 75.16–75.16. 1 indexed citations
2.
Hersby, Ditte Stampe, Tripti Tamhane, Maria Tagliamonte, et al.. (2023). Three doses of BNT162b2 COVID-19 mRNA vaccine establish long-lasting CD8+ T cell immunity in CLL and MDS patients. Frontiers in Immunology. 13. 1035344–1035344. 7 indexed citations
3.
Kumar, Rakesh, et al.. (2023). Year-round breast cancer awareness: Empowering young women in the fight against breast cancer. International Journal of Microbiology Research. 158(4). 347–349. 2 indexed citations
4.
Saini, Sunil Kumar, Anne-Mette Bjerregaard, Ashwin Unnikrishnan, et al.. (2022). Neoantigen reactive T cells correlate with the low mutational burden in hematological malignancies. Leukemia. 36(11). 2734–2738. 4 indexed citations
5.
Saini, Sunil Kumar, Ditte Stampe Hersby, Tripti Tamhane, et al.. (2021). SARS-CoV-2 genome-wide T cell epitope mapping reveals immunodominance and substantial CD8 + T cell activation in COVID-19 patients. Science Immunology. 6(58). 138 indexed citations
6.
Saini, Sunil Kumar, Tripti Tamhane, Sofie Ramskov, et al.. (2019). Empty peptide-receptive MHC class I molecules for efficient detection of antigen-specific T cells. Science Immunology. 4(37). 53 indexed citations
7.
Arora, Anshika, et al.. (2019). Cancer pain, anxiety, and depression in admitted patients at a tertiary care hospital: A prospective observational study. Indian Journal of Palliative Care. 25(4). 562–562. 17 indexed citations
8.
Bjerregaard, Anne-Mette, et al.. (2018). Human endogenous retroviruses and their implication for immunotherapeutics of cancer. Annals of Oncology. 29(11). 2183–2191. 62 indexed citations
9.
Ørskov, Andreas Due, Sunil Kumar Saini, Anne-Mette Bjerregaard, et al.. (2017). Activation of T Cells Specific to Endogenous Retroviral Peptides: Possible Association with Clinical Response to Azacitidine in Myeloid Malignancies. Blood. 130. 4243–4243. 1 indexed citations
10.
Singh, Vineet K., et al.. (2017). Clinicopathological profile, diagnosis and treatment of skin cancers at a tertiary care center: a retrospective study. International Surgery Journal. 4(8). 2549–2549. 3 indexed citations
11.
Saini, Sunil Kumar, Nicolle H. Rekers, & Sine Reker Hadrup. (2017). Novel tools to assist neoepitope targeting in personalized cancer immunotherapy. Annals of Oncology. 28(suppl_12). xii3–xii10. 31 indexed citations
12.
Bentzen, Amalie Kai, Andrea Marion Marquard, Rikke Lyngaa, et al.. (2016). Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes. Nature Biotechnology. 34(10). 1037–1045. 216 indexed citations
13.
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15.
Hein, Zeynep, Hannes Uchtenhagen, Esam T. Abualrous, et al.. (2014). Peptide-independent stabilization of MHC class I molecules breaches cellular quality control*. Journal of Cell Science. 127(Pt 13). 2885–97. 54 indexed citations
16.
Saini, Sunil Kumar, et al.. (2013). Not all empty MHC class I molecules are molten globules: Tryptophan fluorescence reveals a two-step mechanism of thermal denaturation. Molecular Immunology. 54(3-4). 386–396. 31 indexed citations
17.
Walker, Esteban, Kentaro Shinohara, Pan Hui, et al.. (2012). Evaluation of Osteoconductive Scaffolds in the Canine Femoral Multi-Defect Model. Tissue Engineering Part A. 19(5-6). 634–648. 28 indexed citations
18.
Saini, Sunil Kumar & Timothy M. Wick. (2003). Concentric Cylinder Bioreactor for Production of Tissue Engineered Cartilage: Effect of Seeding Density and Hydrodynamic Loading on Construct Development. Biotechnology Progress. 19(2). 510–521. 141 indexed citations
19.
Williams, Kenneth, Sunil Kumar Saini, & Timothy M. Wick. (2002). Computational Fluid Dynamics Modeling of Steady‐State Momentum and Mass Transport in a Bioreactor for Cartilage Tissue Engineering. Biotechnology Progress. 18(5). 951–963. 132 indexed citations
20.
Boas, Steven R., et al.. (1998). Anaerobic Exercise Testing in Children with Asthma. Journal of Asthma. 35(6). 481–487. 10 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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