Xue-Chun Lu

858 total citations
39 papers, 560 citations indexed

About

Xue-Chun Lu is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Xue-Chun Lu has authored 39 papers receiving a total of 560 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 17 papers in Immunology and 7 papers in Oncology. Recurrent topics in Xue-Chun Lu's work include Immune Cell Function and Interaction (13 papers), Hematopoietic Stem Cell Transplantation (4 papers) and Cancer-related gene regulation (4 papers). Xue-Chun Lu is often cited by papers focused on Immune Cell Function and Interaction (13 papers), Hematopoietic Stem Cell Transplantation (4 papers) and Cancer-related gene regulation (4 papers). Xue-Chun Lu collaborates with scholars based in China, United States and Philippines. Xue-Chun Lu's co-authors include Weidong Han, Meixia Chen, Peige Du, Liping An, Xiao Han, Guangyu Xu, Xiao Guo, Hongli Zhu, Yu Sheng and Yao Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nano Letters and PLoS ONE.

In The Last Decade

Xue-Chun Lu

38 papers receiving 557 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xue-Chun Lu China 12 233 209 182 75 45 39 560
Yueping Sun China 12 330 1.4× 257 1.2× 192 1.1× 84 1.1× 51 1.1× 20 718
Hongtao Gu China 15 238 1.0× 231 1.1× 193 1.1× 92 1.2× 53 1.2× 32 572
Hyeong‐jun Han South Korea 14 429 1.8× 87 0.4× 173 1.0× 65 0.9× 33 0.7× 29 623
Yujia Xu China 14 421 1.8× 102 0.5× 140 0.8× 112 1.5× 126 2.8× 28 597
Yuanyuan Sun China 14 150 0.6× 104 0.5× 63 0.3× 51 0.7× 54 1.2× 28 479
Christopher P. Baran United States 9 248 1.1× 186 0.9× 70 0.4× 21 0.3× 55 1.2× 10 765
Ya-Lan Guo China 15 275 1.2× 311 1.5× 128 0.7× 38 0.5× 58 1.3× 19 747
Venkatesh V. Nemmara United States 9 555 2.4× 292 1.4× 75 0.4× 54 0.7× 47 1.0× 20 813
Youjun Xiao China 18 468 2.0× 233 1.1× 168 0.9× 86 1.1× 52 1.2× 35 919
Coral Ampurdanés Spain 14 480 2.1× 191 0.9× 534 2.9× 41 0.5× 60 1.3× 19 888

Countries citing papers authored by Xue-Chun Lu

Since Specialization
Citations

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

Fields of papers citing papers by Xue-Chun Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xue-Chun Lu. 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 Xue-Chun Lu. The network helps show where Xue-Chun Lu may publish in the future.

Co-authorship network of co-authors of Xue-Chun Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Xue-Chun Lu. A scholar is included among the top collaborators of Xue-Chun Lu 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 Xue-Chun Lu. Xue-Chun Lu 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.
Zhang, Haojun, Peng Zhao, Jie Geng, et al.. (2025). DeepSeek R1 excels in diagnosing previously misdiagnosed cases. Array. 28. 100559–100559.
2.
Lu, Xue-Chun, et al.. (2024). Nucleation behavior investigation of cinnamic acid in pure organic solvents: Induction time, metastable zone width and molecular dynamics simulations. Journal of Molecular Liquids. 397. 124143–124143. 5 indexed citations
3.
Wang, Junyou, et al.. (2023). Nucleation‐Supersaturation Dual‐Drive Crystallization Strategy Enables Efficient Protein Crystallization. Small. 20(20). e2307924–e2307924. 5 indexed citations
4.
Wang, Haiming, Junjie Shao, Xue-Chun Lu, et al.. (2022). Potential of immune-related genes as promising biomarkers for premature coronary heart disease through high throughput sequencing and integrated bioinformatics analysis. Frontiers in Cardiovascular Medicine. 9. 893502–893502. 5 indexed citations
5.
Guo, Xiao, Yujie Ye, Xinzhu Liu, et al.. (2022). Effects of Agaricus blazei acidic polysaccharide on the aging of mice through keap1-Nrf2/ARE and MAPKs signal pathway. Electronic Journal of Biotechnology. 57. 31–41. 5 indexed citations
6.
Lu, Xue-Chun, et al.. (2021). Optical-Gain-based Sensing Using Inorganic-Ligand-Passivated Colloidal Quantum Dots. Nano Letters. 21(18). 7732–7739. 5 indexed citations
7.
Wang, Haiming, Zifan Liu, Junjie Shao, et al.. (2020). Immune and Inflammation in Acute Coronary Syndrome: Molecular Mechanisms and Therapeutic Implications. Journal of Immunology Research. 2020(1). 4904217–4904217. 64 indexed citations
8.
Zhang, Haomin, Hao Chen, Yakun Yang, et al.. (2020). Bioinformatics prediction of molecular mechanism and intervention drugs of SARS-related immune injury and their significance for COVID-19 treatment. Zhonghua weishengwuxue he mianyixue zazhi. 40(3). 165–173. 2 indexed citations
9.
Yu, Ruili, Bo Yang, Lili Cai, et al.. (2017). Efficacy of cytokine-induced killer cell infusion as an adjuvant immunotherapy for hepatocellular carcinoma: a systematic review and meta-analysis. Drug Design Development and Therapy. Volume11. 851–864. 16 indexed citations
10.
Zhang, Feng, et al.. (2016). CCND1‐BCL2Gene Network: A direct target of Amifostine in human acute megakaryocytic leukemia cells. Chemical Biology & Drug Design. 89(5). 681–693. 4 indexed citations
11.
Xia, Lei, Yan Zhang, Yao Wang, et al.. (2014). Analysis of adverse events following the treatment of autologous cytokine-induced killer cells for adoptive immunotherapy in malignant tumour sufferers. Expert Opinion on Biological Therapy. 15(4). 481–493. 9 indexed citations
12.
Zhong, Wenwen, et al.. (2014). [Clinical significance of ID4 methylation detection by quantitative methylation-specific PCR in acute leukemia].. PubMed. 22(3). 675–80. 1 indexed citations
13.
14.
15.
Li, Su‐Xia, Hui Fan, Xue-Chun Lu, et al.. (2013). Successful treatment with low-dose decitabine in acute myelogenous leukemia in elderly patients over 80 years old: Five case reports. Oncology Letters. 5(4). 1321–1324. 5 indexed citations
16.
Wang, Lixin, Jihao Zhou, Yushi Yao, et al.. (2013). Low Dose Decitabine Treatment Induces CD80 Expression in Cancer Cells and Stimulates Tumor Specific Cytotoxic T Lymphocyte Responses. PLoS ONE. 8(5). e62924–e62924. 92 indexed citations
17.
Gao, Yan, et al.. (2012). The molecular mechanism of the anticancer effect of atorvastatin: DNA microarray and bioinformatic analyses. International Journal of Molecular Medicine. 30(4). 765–774. 15 indexed citations
18.
Liu, Yang, Bo Yang, Xue-Chun Lu, et al.. (2011). [Clinical study of autologous cytokine induced killer cell infusion treating for elderly patients with myelodysplastic syndrome].. PubMed. 19(3). 787–92. 4 indexed citations
19.
Lu, Xue-Chun, et al.. (2010). [Gene expression profile analysis of T lymphocytes involved in pathogenesis of severe aplastic anemia by using bioinformatics method as a novel way of drug screening].. PubMed. 18(2). 416–20. 1 indexed citations
20.
Yu, Li, Xue-Chun Lu, Liping Dou, et al.. (2005). [Analysis of the methylation in the promoter of LRP15 gene and its expression].. PubMed. 13(2). 188–91. 3 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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