Shuhui Deng

1.5k total citations
83 papers, 747 citations indexed

About

Shuhui Deng is a scholar working on Hematology, Molecular Biology and Oncology. According to data from OpenAlex, Shuhui Deng has authored 83 papers receiving a total of 747 indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Hematology, 40 papers in Molecular Biology and 37 papers in Oncology. Recurrent topics in Shuhui Deng's work include Multiple Myeloma Research and Treatments (65 papers), Protein Degradation and Inhibitors (18 papers) and Chronic Lymphocytic Leukemia Research (13 papers). Shuhui Deng is often cited by papers focused on Multiple Myeloma Research and Treatments (65 papers), Protein Degradation and Inhibitors (18 papers) and Chronic Lymphocytic Leukemia Research (13 papers). Shuhui Deng collaborates with scholars based in China, United States and United Kingdom. Shuhui Deng's co-authors include Lugui Qiu, Yan Xu, Mu Hao, Dehui Zou, Gang An, Weiwei Sui, Fenghuang Zhan, Shuhua Yi, Gary An and Zengjun Li and has published in prestigious journals such as Blood, Cancer and Scientific Reports.

In The Last Decade

Shuhui Deng

67 papers receiving 738 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shuhui Deng China 13 516 475 330 153 117 83 747
Gerrit‐Jan Schuurhuis Netherlands 12 488 0.9× 267 0.6× 151 0.5× 104 0.7× 110 0.9× 23 693
Talha Badar United States 14 390 0.8× 222 0.5× 161 0.5× 65 0.4× 192 1.6× 106 642
Heinz-A. Horst Germany 6 443 0.9× 360 0.8× 163 0.5× 230 1.5× 253 2.2× 6 791
Lori A. Ehrlich United States 9 409 0.8× 302 0.6× 465 1.4× 48 0.3× 48 0.4× 20 707
Miroslav Penka Czechia 14 371 0.7× 280 0.6× 146 0.4× 98 0.6× 148 1.3× 50 573
Annika Dufour Germany 14 656 1.3× 414 0.9× 82 0.2× 128 0.8× 253 2.2× 33 843
Abhijit Ramachandran United States 11 565 1.1× 410 0.9× 131 0.4× 49 0.3× 247 2.1× 28 926
Lorenz Kleeberg Germany 10 372 0.7× 392 0.8× 359 1.1× 54 0.4× 51 0.4× 16 674
Nisha S. Joseph United States 13 645 1.3× 498 1.0× 389 1.2× 24 0.2× 107 0.9× 69 783
Swapna Thota United States 11 328 0.6× 269 0.6× 91 0.3× 71 0.5× 141 1.2× 55 621

Countries citing papers authored by Shuhui Deng

Since Specialization
Citations

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

Fields of papers citing papers by Shuhui Deng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuhui Deng

This figure shows the co-authorship network connecting the top 25 collaborators of Shuhui Deng. A scholar is included among the top collaborators of Shuhui Deng 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 Shuhui Deng. Shuhui Deng 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.
Xiong, Yu, Tengteng Yu, Shuaishuai Zhang, et al.. (2024). Uncommon biphasic CAR-T expansion induces hemophagocytic lymphohistiocytosis-like syndrome and fatal multiple infections following BCMA CAR-T cell therapy: a case report. Journal for ImmunoTherapy of Cancer. 12(11). e010080–e010080. 2 indexed citations
2.
Liu, Jiahui, Yan Xu, Weiwei Sui, et al.. (2024). Development and validation of an individualized and weighted Myeloma Prognostic Score System (MPSS) in patients with newly diagnosed multiple myeloma. American Journal of Hematology. 99(4). 523–533. 7 indexed citations
3.
4.
Li, Lingna, Yuting Yan, Yan Xu, et al.. (2023). Clinical Benefit of Autologous Stem Cell Transplantation for Patients with Multiple Myeloma Achieving Undetectable Minimal Residual Disease after Induction Treatment. Cancer Research Communications. 3(9). 1770–1780. 4 indexed citations
5.
Li, Lingna, Jian Cui, Shuhui Deng, et al.. (2023). Early relapse within 18 months is a powerful dynamic predictor for prognosis and could revise static risk distribution in multiple myeloma. Cancer. 130(3). 421–432. 3 indexed citations
6.
Shi, Lihui, Lingna Li, Jian Cui, et al.. (2023). Immunophenotypic profile defines cytogenetic stability and unveils distinct prognoses in patients with newly-diagnosed multiple myeloma (NDMM). Annals of Hematology. 103(4). 1305–1315. 1 indexed citations
7.
Guo, Shengjie, et al.. (2022). Mixed-Combustion Characteristics and Reaction Kinetics of Municipal Sludge and Corn Straw in Micro-Fluidized Bed. Energies. 15(7). 2637–2637. 3 indexed citations
8.
Yan, Yuting, Xiaoqi Qin, Lanting Liu, et al.. (2021). Clonal Heterogeneity and Evolutionary Phylogeny of Critical Cytogenetic Aberrations in Multiple Myeloma. Blood. 138(Supplement 1). 1608–1608. 1 indexed citations
9.
Yu, Tengteng, Xiaoke Ma, Weiwei Sui, et al.. (2020). Polycomb-like Protein 3 Induces Proliferation and Drug Resistance in Multiple Myeloma and Is Regulated by miRNA-15a. Molecular Cancer Research. 18(7). 1063–1073. 21 indexed citations
10.
Liu, Lanting, Zhen Yu, Hui Cheng, et al.. (2020). Multiple myeloma hinders erythropoiesis and causes anaemia owing to high levels of CCL3 in the bone marrow microenvironment. Scientific Reports. 10(1). 20508–20508. 33 indexed citations
11.
12.
Qin, Yu, Simin Zhang, Shuhui Deng, et al.. (2016). Epigenetic silencing of miR-137 induces drug resistance and chromosomal instability by targeting AURKA in multiple myeloma. Leukemia. 31(5). 1123–1135. 64 indexed citations
13.
14.
Hao, Mu, Meirong Zang, Yan Xu, et al.. (2015). Serum High Expression of Micorna-214 Is a Novel Predictor for Myeloma Bone Disease and Poor Prognosis. Blood. 126(23). 4186–4186. 2 indexed citations
15.
Feng, Xiaoyan, Shuhui Deng, Gang An, et al.. (2015). [Clinical characteristics and survival of newly diagnosed multiple myeloma patients under 40 years old from single center and literature review].. PubMed. 36(11). 933–6. 1 indexed citations
16.
Li, Zengjun, Yan Xu, Gang An, et al.. (2015). [The characteristics of 62 cases of CD20-positive multiple myeloma].. PubMed Central. 36(1). 44–8. 3 indexed citations
17.
Li, Fei, Mu Hao, Xiaoyan Feng, et al.. (2015). Downregulated miR-33b is a novel predictor associated with disease progression and poor prognosis in multiple myeloma. Leukemia Research. 39(7). 793–799. 22 indexed citations
18.
Deng, Shuhui, Yan Xu, Gang An, et al.. (2015). Features of Extramedullary Disease of Multiple Myeloma: High Frequency of P53 Deletion and Poor Survival: A Retrospective Single-Center Study of 834 Cases. Clinical Lymphoma Myeloma & Leukemia. 15(5). 286–291. 52 indexed citations
19.
An, Gang, Chirag Acharya, Shuhui Deng, et al.. (2014). Cytogenetic and clinical marks for defining high-risk myeloma in the context of bortezomib treatment. Experimental Hematology. 43(3). 168–176.e2. 9 indexed citations
20.
An, Gang, Yan Xu, Lihui Shi, et al.. (2013). t(11;14) multiple myeloma: A subtype associated with distinct immunological features, immunophenotypic characteristics but divergent outcome. Leukemia Research. 37(10). 1251–1257. 41 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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