Jingyi Sheng

1.6k total citations · 1 hit paper
31 papers, 1.2k citations indexed

About

Jingyi Sheng is a scholar working on Molecular Biology, Immunology and Surgery. According to data from OpenAlex, Jingyi Sheng has authored 31 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 7 papers in Immunology and 6 papers in Surgery. Recurrent topics in Jingyi Sheng's work include RNA Interference and Gene Delivery (5 papers), MicroRNA in disease regulation (4 papers) and Nanoplatforms for cancer theranostics (3 papers). Jingyi Sheng is often cited by papers focused on RNA Interference and Gene Delivery (5 papers), MicroRNA in disease regulation (4 papers) and Nanoplatforms for cancer theranostics (3 papers). Jingyi Sheng collaborates with scholars based in China, Hong Kong and Australia. Jingyi Sheng's co-authors include Ning Gu, He Ding, Hong‐Jian Zhu, Kaizheng Feng, Yu Zhang, Yan Shen, Yuehuang Wu, Yuepu Pu, Ran Liu and Miao Yang and has published in prestigious journals such as Advanced Materials, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Jingyi Sheng

29 papers receiving 1.2k citations

Hit Papers

Multienzyme‐Like Nanozymes: Regulation, Rational Design, ... 2023 2026 2024 2025 2023 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jingyi Sheng China 16 694 395 316 223 105 31 1.2k
Chun Luo China 24 891 1.3× 361 0.9× 184 0.6× 73 0.3× 142 1.4× 88 1.6k
Su Jiang China 18 585 0.8× 288 0.7× 93 0.3× 100 0.4× 87 0.8× 61 939
Qiyun Tang China 23 625 0.9× 366 0.9× 118 0.4× 143 0.6× 179 1.7× 65 1.3k
Wenmin Cao China 19 522 0.8× 295 0.7× 234 0.7× 458 2.1× 140 1.3× 35 1.3k
Jindao Wu China 24 1.3k 1.9× 757 1.9× 67 0.2× 87 0.4× 177 1.7× 47 1.9k
Zhongquan Sun China 17 360 0.5× 191 0.5× 295 0.9× 460 2.1× 179 1.7× 64 1.3k
Yi Qing China 24 887 1.3× 214 0.5× 132 0.4× 174 0.8× 97 0.9× 57 1.4k
Tsang-Pai Liu Taiwan 15 348 0.5× 95 0.2× 132 0.4× 147 0.7× 38 0.4× 23 747
Zifang Song China 20 544 0.8× 252 0.6× 209 0.7× 359 1.6× 162 1.5× 47 1.5k
Chenying Lu China 21 530 0.8× 326 0.8× 141 0.4× 339 1.5× 86 0.8× 75 1.3k

Countries citing papers authored by Jingyi Sheng

Since Specialization
Citations

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

Fields of papers citing papers by Jingyi Sheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingyi Sheng

This figure shows the co-authorship network connecting the top 25 collaborators of Jingyi Sheng. A scholar is included among the top collaborators of Jingyi Sheng 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 Jingyi Sheng. Jingyi Sheng 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.
Huang, Yubo, Xudong Cai, Lifeng Yan, et al.. (2025). The metabolic fate of iron-based magnetic nanomaterials and their impact on macrophage function. 1(1). 100002–100002.
2.
Liu, Yihai, Sen Yan, Mingyue Wu, et al.. (2025). Subaxillary implantation of mesenchymal stem cell spheroids is a novel therapy for myocardial ischemia reperfusion injury. Chemical Engineering Journal. 508. 160906–160906.
4.
Sheng, Jingyi, Yuehuang Wu, He Ding, et al.. (2023). Multienzyme‐Like Nanozymes: Regulation, Rational Design, and Application. Advanced Materials. 36(10). e2211210–e2211210. 304 indexed citations breakdown →
5.
Ding, He, Yuxin Zhang, Yu Mao, et al.. (2023). Modulation of macrophage polarization by iron-based nanoparticles. SHILAP Revista de lepidopterología. 3(2). 105–122. 22 indexed citations
6.
Sheng, Jingyi, Yang Liu, He Ding, et al.. (2023). Magnetic Delivery of Antigen‐Loaded Magnetic Liposomes for Active Lymph Node Targeting and Enhanced Anti‐Tumor Immunity. Advanced Healthcare Materials. 12(32). e2301232–e2301232. 12 indexed citations
7.
Cao, Rong, Wen Su, Jingyi Sheng, et al.. (2023). Estrogen receptor β attenuates renal fibrosis by suppressing the transcriptional activity of Smad3. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1869(6). 166755–166755. 11 indexed citations
8.
Yan, Sen, Ke Hu, Miao Zhang, et al.. (2022). Extracellular magnetic labeling of biomimetic hydrogel-induced human mesenchymal stem cell spheroids with ferumoxytol for MRI tracking. Bioactive Materials. 19. 418–428. 23 indexed citations
9.
Yang, Li, Bin Huang, Shiqi Hu, et al.. (2022). Indocyanine green assembled free oxygen-nanobubbles towards enhanced near-infrared induced photodynamic therapy. Nano Research. 15(5). 4285–4293. 64 indexed citations
10.
Wang, Honglian, Biao Wei, Huijun He, et al.. (2021). Smad3 deficiency improves islet-based therapy for diabetes and diabetic kidney injury by promoting β cell proliferation via the E2F3-dependent mechanism. Theranostics. 12(1). 379–395. 19 indexed citations
11.
Xu, Bihua, Jingyi Sheng, Yong‐Ke You, et al.. (2019). Deletion of Smad3 prevents renal fibrosis and inflammation in type 2 diabetic nephropathy. Metabolism. 103. 154013–154013. 98 indexed citations
12.
Wei, Jing, et al.. (2018). Availability, prices and affordability of essential medicines for children: a cross-sectional survey in Jiangsu Province, China. BMJ Open. 8(10). e023646–e023646. 21 indexed citations
13.
Tang, Patrick Ming‐Kuen, Philip Chiu‐Tsun Tang, Jeff Yat‐Fai Chung, et al.. (2018). A Novel Feeder-free System for Mass Production of Murine Natural Killer Cells <em>In Vitro</em>. Journal of Visualized Experiments. 9 indexed citations
14.
Sheng, Jingyi, Di Xiang, Jiao Yang, et al.. (2018). Epigenetic alterations of a novel antioxidant gene SLC22A3 predispose susceptible individuals to increased risk of esophageal cancer. International Journal of Biological Sciences. 14(12). 1658–1668. 18 indexed citations
15.
Greening, David W., Shashi K. Gopal, Rommel A. Mathias, et al.. (2015). Emerging roles of exosomes during epithelial–mesenchymal transition and cancer progression. Seminars in Cell and Developmental Biology. 40. 60–71. 186 indexed citations
16.
Sheng, Jingyi, Weisan Chen, & Hong‐Jian Zhu. (2015). The immune suppressive function of transforming growth factor-β(TGF-β) in human diseases. Growth Factors. 33(2). 92–101. 62 indexed citations
18.
Liu, Ran, Juan Liao, Miao Yang, et al.. (2012). The Cluster of miR-143 and miR-145 Affects the Risk for Esophageal Squamous Cell Carcinoma through Co-Regulating Fascin Homolog 1. PLoS ONE. 7(3). e33987–e33987. 91 indexed citations
19.
Yang, Miao, Ran Liu, Jingyi Sheng, et al.. (2012). Differential expression profiles of microRNAs as potential biomarkers for the early diagnosis of esophageal squamous cell carcinoma. Oncology Reports. 29(1). 169–176. 90 indexed citations
20.
Liu, Ran, Jingyi Sheng, Hui Liu, et al.. (2011). Malignant Progression in O 6 -Methylguanine-DNA Methyltransferase-Deficient Esophageal Cancer Cells Is Associated with Ezrin Protein. DNA and Cell Biology. 31(5). 856–866. 7 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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