Junxi Lu

4.3k total citations · 1 hit paper
112 papers, 3.3k citations indexed

About

Junxi Lu is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Epidemiology. According to data from OpenAlex, Junxi Lu has authored 112 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 32 papers in Endocrinology, Diabetes and Metabolism and 31 papers in Epidemiology. Recurrent topics in Junxi Lu's work include Liver Disease Diagnosis and Treatment (18 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (15 papers) and Metabolism, Diabetes, and Cancer (10 papers). Junxi Lu is often cited by papers focused on Liver Disease Diagnosis and Treatment (18 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (15 papers) and Metabolism, Diabetes, and Cancer (10 papers). Junxi Lu collaborates with scholars based in China, Hong Kong and India. Junxi Lu's co-authors include Weiping Jia, Yuqian Bao, Kun‐san Xiang, Huijuan Lu, Haibing Chen, Haoyong Yu, Fang Liu, Kaifeng Guo, Muyu Yu and Lianxi Li and has published in prestigious journals such as PLoS ONE, The Journal of Clinical Endocrinology & Metabolism and Diabetes Care.

In The Last Decade

Junxi Lu

112 papers receiving 3.3k citations

Hit Papers

Exosomes derived from atorvastatin-pretreated MSC acceler... 2020 2026 2022 2024 2020 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junxi Lu China 32 1.3k 905 795 581 534 112 3.3k
Loredana Bucciarelli United States 26 941 0.7× 1.5k 1.7× 451 0.6× 548 0.9× 543 1.0× 46 4.5k
Christoph H. Saely Austria 32 775 0.6× 834 0.9× 747 0.9× 815 1.4× 877 1.6× 203 3.4k
Carla Iacobini Italy 35 1.2k 0.9× 616 0.7× 440 0.6× 779 1.3× 285 0.5× 64 3.5k
Karim Gariani Switzerland 27 1.1k 0.9× 764 0.8× 730 0.9× 890 1.5× 176 0.3× 101 3.4k
Yaoming Xue China 35 1.3k 1.0× 702 0.8× 349 0.4× 642 1.1× 191 0.4× 159 3.2k
M. Hofmann Germany 23 2.0k 1.5× 1.3k 1.4× 597 0.8× 865 1.5× 334 0.6× 36 6.6k
Tatsuo Kawai Japan 26 1.2k 0.9× 534 0.6× 571 0.7× 756 1.3× 1.2k 2.3× 61 3.9k
Yoshimasa Aso Japan 35 1.0k 0.8× 1.6k 1.8× 1.5k 1.9× 800 1.4× 901 1.7× 160 4.3k
Saula Vigili de Kreutzenberg Italy 39 2.0k 1.5× 1.3k 1.4× 682 0.9× 1.0k 1.7× 1.0k 1.9× 115 5.3k
Hyun Jin Kim South Korea 29 1.3k 1.0× 517 0.6× 715 0.9× 757 1.3× 188 0.4× 166 3.3k

Countries citing papers authored by Junxi Lu

Since Specialization
Citations

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

Fields of papers citing papers by Junxi Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junxi Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Junxi Lu. A scholar is included among the top collaborators of Junxi 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 Junxi Lu. Junxi 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.
2.
Li, Junxian, Lei Xu, Weijing Zhao, et al.. (2022). Serum IL‐17A concentration and a IL17RA single nucleotide polymorphism contribute to the risk of autoimmune type 1 diabetes. Diabetes/Metabolism Research and Reviews. 38(6). e3547–e3547. 10 indexed citations
3.
Chen, Mingyun, Zhihui Zhang, Jiang-Feng Ke, et al.. (2022). Chaetocin attenuates atherosclerosis progression and inhibits vascular smooth muscle cell phenotype switching. Journal of Cardiovascular Translational Research. 15(6). 1270–1282. 10 indexed citations
4.
Wang, Junwei, Chunhua Jin, Jiang-Feng Ke, et al.. (2022). GA/HbA1c ratio is a simple and practical indicator to evaluate the risk of metabolic dysfunction-associated fatty liver disease in type 2 diabetes: an observational study. Diabetology & Metabolic Syndrome. 14(1). 167–167. 3 indexed citations
5.
Li, Junxian, Ye Ji, Junxi Lu, et al.. (2021). High level of complement factor Ba within first prenatal test of gestation increases the risk of subsequent gestational diabetes: a propensity score-matched study. Gynecological Endocrinology. 38(2). 158–163. 4 indexed citations
6.
Wei, Gang, Honglin Sun, Kai Dong, et al.. (2021). The thermogenic activity of adjacent adipocytes fuels the progression of ccRCC and compromises anti-tumor therapeutic efficacy. Cell Metabolism. 33(10). 2021–2039.e8. 71 indexed citations
8.
Wang, Junwei, Aiping Wang, Mingyun Chen, et al.. (2019). Prevalence and clinical characteristics of hypertension and metabolic syndrome in newly diagnosed patients with ketosis-onset diabetes: a cross-sectional study. Diabetology & Metabolic Syndrome. 11(1). 31–31. 9 indexed citations
9.
Wu, Mian, Mingliang Zhang, Fengjing Liu, et al.. (2019). Chaetocin attenuates gout in mice through inhibiting HIF-1α and NLRP3 inflammasome-dependent IL-1β secretion in macrophages. Archives of Biochemistry and Biophysics. 670. 94–103. 37 indexed citations
10.
Li, Meifang, Rong Zhang, Tingting Li, et al.. (2015). High Glucose Increases the Expression of Inflammatory Cytokine Genes in Macrophages Through H3K9 Methyltransferase Mechanism. Journal of Interferon & Cytokine Research. 36(1). 48–61. 31 indexed citations
13.
Chen, Haibing, Zhi Zheng, Yan Huang, et al.. (2012). A Microalbuminuria Threshold to Predict the Risk for the Development of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients. PLoS ONE. 7(5). e36718–e36718. 40 indexed citations
14.
Zhu, Yunxia, Mingliang Zhang, Xuhong Hou, et al.. (2011). Cigarette smoking increases risk for incident metabolic syndrome in Chinese men-Shanghai diabetes study.. PubMed. 24(5). 475–82. 23 indexed citations
15.
Yu, Weihui, Cheng Hu, Wen Qin, et al.. (2011). Effects of KCNQ1 Polymorphisms on the Therapeutic Efficacy of Oral Antidiabetic Drugs in Chinese Patients With Type 2 Diabetes. Clinical Pharmacology & Therapeutics. 89(3). 437–442. 31 indexed citations
16.
Chen, Haibing, Junxi Lu, Qing Li, et al.. (2009). The protective effect of the RAS inhibitor on diabetic patients with nephropathy in the context of VEGF suppression. Acta Pharmacologica Sinica. 30(2). 242–250. 8 indexed citations
17.
Wang, Jie, Yuqian Bao, Cheng Hu, et al.. (2008). Effects of ABCA1 variants on rosiglitazone monotherapy in newly diagnosed type 2 diabetes patients. Acta Pharmacologica Sinica. 29(2). 252–258. 24 indexed citations
18.
Chen, Haibing, Weiping Jia, Junxi Lu, et al.. (2007). [Change and significance of serum pigment epithelium-derived factor in type 2 diabetic nephropathy].. PubMed. 87(18). 1230–3. 13 indexed citations
19.
Bao, Yuqian, Weiping Jia, Lei Chen, Junxi Lu, & Kun‐san Xiang. (2006). [Association between C-reactive protein level and metabolic syndrome and components thereof].. PubMed. 86(30). 2105–9. 7 indexed citations
20.
Jia, Weiping, et al.. (2004). [A comparison of the application of two working definitions of metabolic syndrome in Chinese population].. PubMed. 84(7). 534–8. 10 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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