Suchitra Pandey

1.7k total citations · 1 hit paper
28 papers, 1.2k citations indexed

About

Suchitra Pandey is a scholar working on Management of Technology and Innovation, Biochemistry and Infectious Diseases. According to data from OpenAlex, Suchitra Pandey has authored 28 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Management of Technology and Innovation, 12 papers in Biochemistry and 8 papers in Infectious Diseases. Recurrent topics in Suchitra Pandey's work include Blood donation and transfusion practices (13 papers), Blood transfusion and management (12 papers) and Blood groups and transfusion (7 papers). Suchitra Pandey is often cited by papers focused on Blood donation and transfusion practices (13 papers), Blood transfusion and management (12 papers) and Blood groups and transfusion (7 papers). Suchitra Pandey collaborates with scholars based in United States, Canada and Netherlands. Suchitra Pandey's co-authors include Girish N. Vyas, Philip J. Norris, Lewis L. Lanier, Sandra López‐Vergès, Douglas F. Nixon, Jeffrey M. Milush, Janice Arakawa‐Hoyt, Hanspeter Pircher, Vanessa A. York and Jagat R. Kanwar and has published in prestigious journals such as Blood, The Journal of Immunology and Clinical Infectious Diseases.

In The Last Decade

Suchitra Pandey

25 papers receiving 1.2k citations

Hit Papers

CD57 defines a functional... 2010 2026 2015 2020 2010 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suchitra Pandey United States 10 561 201 194 178 174 28 1.2k
Matthew S. Karafin United States 20 82 0.1× 264 1.3× 28 0.1× 73 0.4× 573 3.3× 77 1.0k
Kurt Leibundgut Switzerland 21 128 0.2× 70 0.3× 103 0.5× 16 0.1× 332 1.9× 92 1.4k
S. Gerald Sandler United States 16 69 0.1× 165 0.8× 62 0.3× 63 0.4× 389 2.2× 49 756
Kim Janatpour United States 15 63 0.1× 122 0.6× 48 0.2× 38 0.2× 210 1.2× 23 700
Lijun Sun China 14 44 0.1× 211 1.0× 236 1.2× 86 0.5× 83 0.5× 39 1.1k
Gaobin Bao United States 17 180 0.3× 58 0.3× 15 0.1× 157 0.9× 49 0.3× 35 1.4k
Jacqueline D. Trudeau Canada 12 509 0.9× 39 0.2× 20 0.1× 23 0.1× 34 0.2× 23 1.0k
Jeanne A. Lumadue United States 11 45 0.1× 60 0.3× 48 0.2× 24 0.1× 92 0.5× 16 567
Ben Saxon Australia 12 38 0.1× 51 0.3× 39 0.2× 25 0.1× 356 2.0× 37 690
Dennis C.W. Poland Netherlands 12 156 0.3× 42 0.2× 9 0.0× 41 0.2× 38 0.2× 22 809

Countries citing papers authored by Suchitra Pandey

Since Specialization
Citations

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

Fields of papers citing papers by Suchitra Pandey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suchitra Pandey

This figure shows the co-authorship network connecting the top 25 collaborators of Suchitra Pandey. A scholar is included among the top collaborators of Suchitra Pandey 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 Suchitra Pandey. Suchitra Pandey 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
2.
Pandey, Suchitra, et al.. (2024). The use of mixed reality technology within the donor collection experience. Transfusion. 64(2). 315–324. 1 indexed citations
4.
Nguyen, AnhThu, et al.. (2024). Incidence of formation of anti‐D between patients with and without a history of solid organ transplant. Vox Sanguinis. 119(4). 363–367. 1 indexed citations
5.
Tayyar, Ralph, et al.. (2023). High‐titer post‐vaccine COVID‐19 convalescent plasma for immunocompromised patients during the first omicron surge. Transplant Infectious Disease. 25(2). e14055–e14055. 1 indexed citations
6.
Custer, Brian, Barbee Whitaker, Lance M. Pollack, et al.. (2023). HIV risk behavior profiles among men who have sex with men interested in donating blood: Findings from the Assessing Donor Variability and New Concepts in Eligibility study. Transfusion. 63(10). 1872–1884. 6 indexed citations
7.
Kaufman, Richard M., Denese C. Marks, Yael Flamand, et al.. (2023). Risk factors for T‐cell lymphopenia in frequent platelet donors: The BEST collaborative study. Transfusion. 63(11). 2072–2082.
8.
Buren, Nancy L. Van, Srijana Rajbhandary, Jed B. Gorlin, et al.. (2022). Demographics of first‐time donors returning for donation during the pandemic: COVID‐19 convalescent plasma versus standard blood product donors. Transfusion. 63(3). 552–563. 4 indexed citations
9.
Fine, Alan, et al.. (2022). Factors associated with first‐time and repeat blood donation: Adverse reactions and effects on donor behavior. Transfusion. 62(6). 1269–1279. 8 indexed citations
10.
Wirz, Oliver F., Katharina Röltgen, Bryan Stevens, et al.. (2021). Use of Outpatient-Derived COVID-19 Convalescent Plasma in COVID-19 Patients Before Seroconversion. Frontiers in Immunology. 12. 739037–739037. 3 indexed citations
11.
Pham, Tho D., et al.. (2021). How do I implement pathogen‐reduced platelets?. Transfusion. 61(12). 3295–3302. 3 indexed citations
12.
Nguyen, Khoa D., Oliver F. Wirz, Katharina Röltgen, et al.. (2021). Efficient Identification of High-Titer Anti–Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Plasma Samples by Pooling Method. Archives of Pathology & Laboratory Medicine. 145(10). 1221–1227.
13.
Weng, Yingjie, Suchitra Pandey, Angela J. Rogers, et al.. (2021). Active surveillance of serious adverse events following transfusion of COVID‐19 convalescent plasma. Transfusion. 62(1). 28–36. 3 indexed citations
14.
Wirtz, Mathijs R., Ruqayyah J. Almizraq, Nina C. Weber, et al.. (2020). Red‐blood‐cell manufacturing methods and storage solutions differentially induce pulmonary cell activation. Vox Sanguinis. 115(5). 395–404. 2 indexed citations
15.
Murugesan, Kanagavel, Prasanna Jagannathan, Tho D. Pham, et al.. (2020). Interferon-γ Release Assay for Accurate Detection of Severe Acute Respiratory Syndrome Coronavirus 2 T-Cell Response. Clinical Infectious Diseases. 73(9). e3130–e3132. 92 indexed citations
16.
Almizraq, Ruqayyah J., Philip J. Norris, Heather C. Inglis, et al.. (2018). Blood manufacturing methods affect red blood cell product characteristics and immunomodulatory activity. Blood Advances. 2(18). 2296–2306. 35 indexed citations
17.
Rollins, Mark D., Ari B. Molofsky, Ashok Nambiar, et al.. (2012). Two septic transfusion reactions presenting as transfusion-related acute lung injury from a split plateletpheresis unit. Critical Care Medicine. 40(8). 2488–2491. 7 indexed citations
18.
Carrick, Danielle M., Philip J. Norris, Robert O. Endres, et al.. (2011). Establishing assay cutoffs for HLA antibody screening of apheresis donors. Transfusion. 51(10). 2092–2101. 26 indexed citations
19.
Carrick, Danielle M., Steven Kleinman, John D. Roback, et al.. (2010). Agreement among HLA antibody detection assays is higher in ever‐pregnant donors and improved using a consensus cutoff. Transfusion. 51(5). 1105–1116. 14 indexed citations
20.
López‐Vergès, Sandra, Jeffrey M. Milush, Suchitra Pandey, et al.. (2009). CD57 defines a functionally unique subset of NK cells in humans (134.1). The Journal of Immunology. 182(Supplement_1). 134.1–134.1. 1 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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