Baijun Fang

1.6k total citations
42 papers, 1.1k citations indexed

About

Baijun Fang is a scholar working on Hematology, Genetics and Molecular Biology. According to data from OpenAlex, Baijun Fang has authored 42 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Hematology, 28 papers in Genetics and 13 papers in Molecular Biology. Recurrent topics in Baijun Fang's work include Mesenchymal stem cell research (21 papers), Hematopoietic Stem Cell Transplantation (13 papers) and Multiple Myeloma Research and Treatments (9 papers). Baijun Fang is often cited by papers focused on Mesenchymal stem cell research (21 papers), Hematopoietic Stem Cell Transplantation (13 papers) and Multiple Myeloma Research and Treatments (9 papers). Baijun Fang collaborates with scholars based in China and United States. Baijun Fang's co-authors include Robert Chunhua Zhao, Lianming Liao, Mingxia Shi, Shaoguang Yang, Yongping Song, Yuhao Liu, Robert Chunhua Zhao, Y. Zhang, Yaqi Song and Yuanfang Ma and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Biochemical and Biophysical Research Communications.

In The Last Decade

Baijun Fang

41 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
Baijun Fang China 17 710 403 334 296 167 42 1.1k
Qinjun Zhao China 16 1.1k 1.5× 619 1.5× 458 1.4× 172 0.6× 99 0.6× 28 1.5k
Chantal Lechanteur Belgium 21 802 1.1× 515 1.3× 410 1.2× 245 0.8× 50 0.3× 50 1.4k
Alexandra Briquet Belgium 16 683 1.0× 438 1.1× 348 1.0× 173 0.6× 48 0.3× 31 1.1k
Lutz Peter Mueller Germany 14 383 0.5× 332 0.8× 342 1.0× 72 0.2× 188 1.1× 25 935
Yumi Torimaru United States 6 554 0.8× 653 1.6× 708 2.1× 89 0.3× 427 2.6× 7 1.4k
Evan Colletti United States 14 343 0.5× 337 0.8× 337 1.0× 105 0.4× 67 0.4× 30 717
Raghavan Chinnadurai United States 11 482 0.7× 227 0.6× 172 0.5× 65 0.2× 80 0.5× 18 758
Shihong Lu China 19 387 0.5× 183 0.5× 440 1.3× 270 0.9× 28 0.2× 59 1.1k
Yukari Muguruma Japan 17 838 1.2× 430 1.1× 724 2.2× 496 1.7× 28 0.2× 28 1.7k
Brigitta Omazic Sweden 13 773 1.1× 357 0.9× 258 0.8× 585 2.0× 25 0.1× 19 1.5k

Countries citing papers authored by Baijun Fang

Since Specialization
Citations

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

Fields of papers citing papers by Baijun Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baijun Fang

This figure shows the co-authorship network connecting the top 25 collaborators of Baijun Fang. A scholar is included among the top collaborators of Baijun Fang 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 Baijun Fang. Baijun Fang 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.
Jin, Song, Junling Zhuang, Xin Zhou, et al.. (2024). Ixazomib Based Regimens As Front-Line Treatment in Newly Diagnosed Multiple Myeloma Patients in China: A Real-World Data from Infinite Study. Blood. 144(Supplement 1). 6970–6970.
2.
Chen, Wei, et al.. (2023). Regulatory Effect and Mechanism of Erythroblastic Island Macrophages on Anemia in Patients with Newly Diagnosed Multiple Myeloma. Journal of Inflammation Research. Volume 16. 2585–2594. 1 indexed citations
3.
Fang, Baijun, et al.. (2023). Mitochondrial dysfunction and drug targets in multiple myeloma. Journal of Cancer Research and Clinical Oncology. 149(10). 8007–8016. 2 indexed citations
4.
Zhao, Min, Min Hu, Yong Chen, et al.. (2021). Cereblon modulator CC-885 induces CRBN-dependent ubiquitination and degradation of CDK4 in multiple myeloma. Biochemical and Biophysical Research Communications. 549. 150–156. 21 indexed citations
5.
Liu, Xi-Yang, Yuzhang Liu, Lina Liu, et al.. (2019). <p>Analysis of the efficacy and safety of bortezomib for treating newly diagnosed multiple myeloma through different administration methods</p>. Cancer Management and Research. Volume 11. 8295–8302. 3 indexed citations
6.
Li, Ning, et al.. (2019). miR-144-3p Suppresses Osteogenic Differentiation of BMSCs from Patients with Aplastic Anemia through Repression of TET2. Molecular Therapy — Nucleic Acids. 19. 619–626. 33 indexed citations
7.
Fang, Baijun, et al.. (2015). Treatment of refractory/relapsed adult acute lymphoblastic leukemia with bortezomib- based chemotherapy. International Journal of General Medicine. 8. 211–211. 5 indexed citations
8.
9.
Fang, Baijun, Ling Mai, Ning Li, & Yongping Song. (2011). Favorable Response of Chronic Refractory Immune Thrombocytopenic Purpura to Mesenchymal Stem Cells. Stem Cells and Development. 21(3). 497–502. 23 indexed citations
10.
Fang, Baijun, Yuzhang Liu, Jian Zhou, Yanan Li, & Yongping Song. (2011). Salvage therapy with endostatin, low‐dose homoharringtonine, and cytarabine in combination with granulocyte‐colony stimulating factor for elderly patients with primary refractory acute myeloid leukemia. American Journal of Hematology. 87(1). 126–127. 1 indexed citations
11.
Fang, Baijun, Yongping Song, Ningning Li, et al.. (2010). Human Adipose Tissue–Derived Adult Stem Cells Can Lead to Multiorgan Engraftment. Transplantation Proceedings. 42(5). 1849–1856. 7 indexed citations
12.
13.
Fang, Baijun, Yongping Song, Ningning Li, et al.. (2009). Resolution of Refractory Chronic Autoimmune Thrombocytopenic Purpura Following Mesenchymal Stem Cell Transplantation: A Case Report. Transplantation Proceedings. 41(5). 1827–1830. 20 indexed citations
14.
Fang, Baijun, Ning Li, Yongping Song, Quande Lin, & Robert Chunhua Zhao. (2009). Comparison of human post-embryonic, multipotent stem cells derived from various tissues. Biotechnology Letters. 31(7). 929–938. 9 indexed citations
15.
Fang, Baijun, Suxia Luo, Yongping Song, et al.. (2009). Intermittent dosing of G-CSF to ameliorate carbon tetrachloride-induced liver fibrosis in mice. Toxicology. 270(1). 43–48. 5 indexed citations
16.
Fang, Baijun, Yongping Song, Ning Li, Jing Li, & Robert Chunhua Zhao. (2008). Cotransplantation of Haploidentical Mesenchymal Stem Cells to Reduce the Risk of Graft Failure in a Patient with Refractory Severe Aplastic Anemia. Acta Haematologica. 119(3). 162–165. 7 indexed citations
17.
18.
Fang, Baijun, Yun Seob Song, Robert Chunhua Zhao, Qian Han, & Qiaoli Lin. (2007). Using Human Adipose Tissue-Derived Mesenchymal Stem Cells as Salvage Therapy for Hepatic Graft-Versus-Host Disease Resembling Acute Hepatitis. Transplantation Proceedings. 39(5). 1710–1713. 71 indexed citations
19.
Fang, Baijun, Mingxia Shi, Lianming Liao, et al.. (2004). Systemic Infusion of FLK1+ Mesenchymal Stem Cells Ameliorate Carbon Tetrachloride-Induced Liver Fibrosis in Mice. Transplantation. 78(1). 83–88. 225 indexed citations
20.
Fang, Baijun, Mingxia Shi, Lianming Liao, et al.. (2003). Multiorgan Engraftment and Multilineage Differentiation by Human Fetal Bone Marrow Flk1 + /CD31 - /CD34 - Progenitors. Journal of Hematotherapy & Stem Cell Research. 12(6). 603–613. 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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