Xing-Mei Cao

1.5k total citations
33 papers, 509 citations indexed

About

Xing-Mei Cao is a scholar working on Hematology, Oncology and Molecular Biology. According to data from OpenAlex, Xing-Mei Cao has authored 33 papers receiving a total of 509 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Hematology, 15 papers in Oncology and 11 papers in Molecular Biology. Recurrent topics in Xing-Mei Cao's work include Acute Myeloid Leukemia Research (9 papers), Neutropenia and Cancer Infections (6 papers) and Multiple Myeloma Research and Treatments (5 papers). Xing-Mei Cao is often cited by papers focused on Acute Myeloid Leukemia Research (9 papers), Neutropenia and Cancer Infections (6 papers) and Multiple Myeloma Research and Treatments (5 papers). Xing-Mei Cao collaborates with scholars based in China, Germany and United States. Xing-Mei Cao's co-authors include Wanggang Zhang, Yinxia Chen, Aili He, Wanhong Zhao, Liufang Gu, Jie Liu, Fangxia Wang, Pengyu Zhang, Bo Lei and Yilin Zhang and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Scientific Reports.

In The Last Decade

Xing-Mei Cao

32 papers receiving 498 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xing-Mei Cao China 11 218 191 188 179 59 33 509
Haizhou Xing China 11 208 1.0× 192 1.0× 180 1.0× 162 0.9× 25 0.4× 31 561
Jan‐Henrik Mikesch Germany 15 212 1.0× 330 1.7× 120 0.6× 194 1.1× 35 0.6× 54 680
Xia Mao China 16 512 2.3× 235 1.2× 180 1.0× 194 1.1× 123 2.1× 66 741
Huda Salman United States 11 347 1.6× 136 0.7× 227 1.2× 88 0.5× 111 1.9× 32 525
Silvia Calpe Netherlands 16 196 0.9× 180 0.9× 574 3.1× 82 0.5× 58 1.0× 27 904
Sebastian Klobuch Germany 14 224 1.0× 137 0.7× 194 1.0× 95 0.5× 66 1.1× 36 463
Haodong Cai China 10 182 0.8× 162 0.8× 70 0.4× 94 0.5× 34 0.6× 28 421
Chang‐Ki Min South Korea 16 251 1.2× 271 1.4× 163 0.9× 430 2.4× 15 0.3× 43 698
Liufang Gu China 9 192 0.9× 142 0.7× 92 0.5× 108 0.6× 31 0.5× 22 371
Mai H. Le United States 12 219 1.0× 200 1.0× 236 1.3× 93 0.5× 48 0.8× 26 589

Countries citing papers authored by Xing-Mei Cao

Since Specialization
Citations

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

Fields of papers citing papers by Xing-Mei Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xing-Mei Cao. 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 Xing-Mei Cao. The network helps show where Xing-Mei Cao may publish in the future.

Co-authorship network of co-authors of Xing-Mei Cao

This figure shows the co-authorship network connecting the top 25 collaborators of Xing-Mei Cao. A scholar is included among the top collaborators of Xing-Mei Cao 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 Xing-Mei Cao. Xing-Mei Cao 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.
Wang, Baiyan, Jie Liu, Wanhong Zhao, et al.. (2020). Chimeric Antigen Receptor T Cell Therapy in the Relapsed or Refractory Multiple Myeloma with Extramedullary Disease--a Single Institution Observation in China. Blood. 136(Supplement 1). 6–6. 16 indexed citations
2.
Chen, Hongli, Fangxia Wang, Pengyu Zhang, et al.. (2019). Management of cytokine release syndrome related to CAR-T cell therapy. Frontiers of Medicine. 13(5). 610–617. 77 indexed citations
3.
Meng, Shan, Yanxia Jin, Wanggang Zhang, et al.. (2017). A novel multi‐epitope vaccine from MMSA‐1 and DKK1 for multiple myeloma immunotherapy. British Journal of Haematology. 178(3). 413–426. 49 indexed citations
4.
Fan, Frank, Wanhong Zhao, Jie Liu, et al.. (2017). Durable remissions with BCMA-specific chimeric antigen receptor (CAR)-modified T cells in patients with refractory/relapsed multiple myeloma.. Journal of Clinical Oncology. 35(18_suppl). LBA3001–LBA3001. 54 indexed citations
5.
Shen, Ying, Aili He, Fangxia Wang, et al.. (2017). Granulocyte colony stimulating factor priming chemotherapy is more effective than standard chemotherapy as salvage therapy in relapsed acute myeloid leukemia. Medicina Clínica. 151(9). 339–344. 2 indexed citations
6.
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8.
Dai, Cong, Wanggang Zhang, Jie Liu, et al.. (2016). Lack of association between cytotoxic T-lymphocyte antigen-4 gene polymorphisms and lymphoid malignancy risk: evidence from a meta-analysis. Annals of Hematology. 95(10). 1685–1694. 9 indexed citations
9.
10.
Dai, Cong, Tiansong Zhang, Shuai Lin, et al.. (2016). Role of IL-17A rs2275913 and IL-17F rs763780 polymorphisms in risk of cancer development: an updated meta-analysis. Scientific Reports. 6(1). 20439–20439. 43 indexed citations
11.
Dai, Cong, Cong Dai, Jie Liu, et al.. (2015). Association Between Interleukin-10-3575T>A (rs1800890) Polymorphism and Cancer Risk. Genetic Testing and Molecular Biomarkers. 19(6). 324–330. 3 indexed citations
12.
Ma, Xiaorong, Jin Wang, Wanggang Zhang, et al.. (2015). Comparison of porcine anti‐human lymphocyte globulin and rabbit anti‐human thymocyte globulin in the treatment of severe aplastic anemia: a retrospective single‐center study. European Journal Of Haematology. 96(3). 260–268. 16 indexed citations
13.
Ma, Xiaorong, Jin Wang, Wanggang Zhang, et al.. (2015). [Cohort Study on GHA and New Combined Priming Chemotherapeutic Regimens in Treatment of Refractory Acute Myeloid Leukemia and Myelodysplastic Syndrome].. PubMed. 23(2). 369–74. 2 indexed citations
14.
Zhang, Wenjuan, Wanggang Zhang, Pengyu Zhang, et al.. (2012). The expression and functional characterization associated with cell apoptosis and proteomic analysis of the novel gene MLAA-34 in U937 cells. Oncology Reports. 29(2). 491–506. 8 indexed citations
15.
Zhou, Fuling, et al.. (2010). Peptide-based immunotherapy for multiple myeloma: Current approaches. Vaccine. 28(37). 5939–5946. 10 indexed citations
16.
Gu, Liufang, Fangxia Wang, Xing-Mei Cao, et al.. (2010). Low dose of homoharringtonine and cytarabine combined with granulocyte colony-stimulating factor priming on the outcome of relapsed or refractory acute myeloid leukemia. Journal of Cancer Research and Clinical Oncology. 137(6). 997–1003. 21 indexed citations
17.
Chen, Yinxia, Xiaorong Ma, Wanggang Zhang, et al.. (2008). [Efficiency of GHA priming chemotherapy on patients with refractory acute myeloid leukemia and myelodysplastic syndrome and its relationship with expression of costimulatory molecule B7.1].. PubMed. 16(5). 1002–5. 2 indexed citations
18.
Cao, Xing-Mei. (2006). ru he shen he chu fang yong yao de shi yi xing. China Medical Herald. 3(25). 146–147. 2 indexed citations
19.
Yang, Yun, Wanggang Zhang, Yinxia Chen, et al.. (2006). [Positive immunoregulation of thalidomide on human peripheral blood mononuclear cell cultures].. PubMed. 14(6). 1172–7. 2 indexed citations
20.
Chen, Gang, Wanggang Zhang, Xing-Mei Cao, et al.. (2005). Serological identification of immunogenic antigens in acute monocytic leukemia. Leukemia Research. 29(5). 503–509. 35 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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