Dijie Li

2.0k total citations · 1 hit paper
37 papers, 1.2k citations indexed

About

Dijie Li is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Dijie Li has authored 37 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 14 papers in Cancer Research and 6 papers in Oncology. Recurrent topics in Dijie Li's work include Bone Metabolism and Diseases (17 papers), MicroRNA in disease regulation (8 papers) and Cancer-related molecular mechanisms research (8 papers). Dijie Li is often cited by papers focused on Bone Metabolism and Diseases (17 papers), MicroRNA in disease regulation (8 papers) and Cancer-related molecular mechanisms research (8 papers). Dijie Li collaborates with scholars based in China, Hong Kong and United States. Dijie Li's co-authors include Ge Zhang, Airong Qian, Chong Yin, Zhihao Chen, Aiping Lü, Bao‐Ting Zhang, Fan Zhao, Shuaijian Ni, Jin Liu and Liang Yu and has published in prestigious journals such as Nature Communications, The Journal of Clinical Endocrinology & Metabolism and ACS Applied Materials & Interfaces.

In The Last Decade

Dijie Li

35 papers receiving 1.2k citations

Hit Papers

Recent Progress in Aptamer Discoveries and Modifications ... 2020 2026 2022 2024 2020 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dijie Li China 19 931 354 236 101 93 37 1.2k
Zongkang Zhang Hong Kong 18 1.0k 1.1× 182 0.5× 254 1.1× 130 1.3× 74 0.8× 27 1.4k
Qi Guo China 23 1.2k 1.2× 584 1.6× 226 1.0× 147 1.5× 104 1.1× 65 1.8k
Mei Zhang China 25 966 1.0× 306 0.9× 265 1.1× 256 2.5× 47 0.5× 54 1.8k
Suryaji Patil China 12 611 0.7× 159 0.4× 360 1.5× 145 1.4× 105 1.1× 18 1.1k
Min Qiao China 9 760 0.8× 175 0.5× 124 0.5× 127 1.3× 119 1.3× 15 1.2k
Dexuan Xiao China 23 1.1k 1.2× 200 0.6× 407 1.7× 93 0.9× 75 0.8× 46 1.7k
Ming Cai China 17 491 0.5× 179 0.5× 450 1.9× 92 0.9× 50 0.5× 47 1.3k
Kyunghwa Baek South Korea 20 447 0.5× 125 0.4× 96 0.4× 101 1.0× 47 0.5× 46 876
Jun Xie China 21 911 1.0× 217 0.6× 60 0.3× 145 1.4× 57 0.6× 51 1.3k
Weitong Cui China 19 828 0.9× 126 0.4× 304 1.3× 70 0.7× 56 0.6× 35 1.3k

Countries citing papers authored by Dijie Li

Since Specialization
Citations

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

Fields of papers citing papers by Dijie Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dijie Li

This figure shows the co-authorship network connecting the top 25 collaborators of Dijie Li. A scholar is included among the top collaborators of Dijie Li 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 Dijie Li. Dijie Li 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, Luyao, Jin Liu, Xin Yang, et al.. (2023). Macrophagic Sclerostin Loop2-ApoER2 Interaction Required by Sclerostin for Suppressing Inflammatory Responses. Metabolism. 142. 155427–155427. 1 indexed citations
2.
Li, Dijie, Luyao Wang, Ning Zhang, et al.. (2023). Sclerostin loop3-LRP4 Interaction Required by Sclerostin for Lipid and Glucose Metabolism Impairment in Adipocyte. Metabolism. 142. 155432–155432.
3.
Li, Dijie, Ying Han, Xiaohui Tao, et al.. (2023). The role of sclerostin in lipid and glucose metabolism disorders. Biochemical Pharmacology. 215. 115694–115694. 4 indexed citations
4.
Li, Dijie, Luyao Wang, Ning Zhang, et al.. (2023). Sclerostin Loop3 Participates in Whole-body Lipid and Glucose Metabolism Impairment Effects of Sclerostin. Metabolism. 142. 155439–155439. 1 indexed citations
5.
Wang, Yang, Shuhao Wang, Zhen Liu, et al.. (2022). Pyro-catalysis for tooth whitening via oral temperature fluctuation. Nature Communications. 13(1). 4419–4419. 59 indexed citations
6.
Yu, Sifan, Dijie Li, Ning Zhang, et al.. (2022). Drug discovery of sclerostin inhibitors. Acta Pharmaceutica Sinica B. 12(5). 2150–2170. 36 indexed citations
7.
Dang, Lei, Xiaohao Wu, Dijie Li, et al.. (2022). A Rapid Protocol for Direct Isolation of Osteoclast Lineage Cells from Mouse Bone Marrow. BIO-PROTOCOL. 12(3). e4338–e4338.
8.
Li, Dijie, Jin Liu, Chaofei Yang, et al.. (2021). Targeting long noncoding RNA PMIF facilitates osteoprogenitor cells migrating to bone formation surface to promote bone formation during aging. Theranostics. 11(11). 5585–5604. 16 indexed citations
9.
Su, Peihong, Ye Tian, Chong Yin, et al.. (2021). MACF1 promotes osteoblastic cell migration by regulating MAP1B through the GSK3beta/TCF7 pathway. Bone. 154. 116238–116238. 7 indexed citations
10.
Ni, Shuaijian, Zhenjian Zhuo, Yufei Pan, et al.. (2020). Recent Progress in Aptamer Discoveries and Modifications for Therapeutic Applications. ACS Applied Materials & Interfaces. 13(8). 9500–9519. 415 indexed citations breakdown →
11.
Yin, Chong, Ye Tian, Yang Yu, et al.. (2020). Long noncoding RNA AK039312 and AK079370 inhibits bone formation via miR-199b-5p. Pharmacological Research. 163. 105230–105230. 18 indexed citations
12.
Chen, Zhihao, Fan Zhao, Chao Liang, et al.. (2020). Silencing of miR-138-5p sensitizes bone anabolic action to mechanical stimuli. Theranostics. 10(26). 12263–12278. 37 indexed citations
13.
Yin, Chong, Ye Tian, Yang Yu, et al.. (2020). miR-129-5p Inhibits Bone Formation Through TCF4. Frontiers in Cell and Developmental Biology. 8. 600641–600641. 26 indexed citations
14.
Ma, Jianhua, Xiao Lin, Chen Chu, et al.. (2019). Circulating miR-181c-5p and miR-497-5p Are Potential Biomarkers for Prognosis and Diagnosis of Osteoporosis. The Journal of Clinical Endocrinology & Metabolism. 105(5). 1445–1460. 61 indexed citations
15.
Zhang, Yan, Chong Yin, Lifang Hu, et al.. (2018). MACF1 Overexpression by Transfecting the 21 kbp Large Plasmid PEGFP-C1A-ACF7 Promotes Osteoblast Differentiation and Bone Formation. Human Gene Therapy. 29(2). 259–270. 22 indexed citations
16.
Hu, Lifang, Yunyun Xiao, Fan Zhao, et al.. (2017). MACF1, versatility in tissue-specific function and in human disease. Seminars in Cell and Developmental Biology. 69. 3–8. 30 indexed citations
17.
Yin, Chong, Yan Zhang, Lifang Hu, et al.. (2017). Mechanical unloading reduces microtubule actin crosslinking factor 1 expression to inhibit β‐catenin signaling and osteoblast proliferation. Journal of Cellular Physiology. 233(7). 5405–5419. 42 indexed citations
18.
Li, Dijie, Zhihao Chen, Chong Ding, et al.. (2016). Qiang Gu Kang Wei Prescription Increases Bone Mineral Density of Load-bearing Bone in Tail Suspended Rats. Hangtian yixue yu yixue gongcheng. 1–8. 1 indexed citations
19.
Sun, Yulong, Xiaohu Chen, Zhihao Chen, et al.. (2014). Neuropeptide FF attenuates RANKL-induced differentiation of macrophage-like cells into osteoclast-like cells. Archives of Oral Biology. 60(2). 282–292. 11 indexed citations
20.
Zhao, Fan, Dijie Li, Yasir Arfat, et al.. (2014). Reloading partly recovers bone mineral density and mechanical properties in hind limb unloaded rats. Acta Astronautica. 105(1). 57–65. 4 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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