Meng Su

1.9k total citations · 1 hit paper
22 papers, 1.2k citations indexed

About

Meng Su is a scholar working on Hematology, Molecular Biology and Genetics. According to data from OpenAlex, Meng Su has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Hematology, 8 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in Meng Su's work include Acute Myeloid Leukemia Research (7 papers), Retinoids in leukemia and cellular processes (4 papers) and Drug Transport and Resistance Mechanisms (2 papers). Meng Su is often cited by papers focused on Acute Myeloid Leukemia Research (7 papers), Retinoids in leukemia and cellular processes (4 papers) and Drug Transport and Resistance Mechanisms (2 papers). Meng Su collaborates with scholars based in China, United States and Malaysia. Meng Su's co-authors include John A. Spertus, Xiaochen Wang, Jiamin Liu, Yuan Lu, Jiapeng Lu, Harlan M. Krumholz, Hongyu Zhao, George C. Linderman, Chaoqun Wu and Haibo Zhang and has published in prestigious journals such as The Lancet, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Meng Su

20 papers receiving 1.2k citations

Hit Papers

Prevalence, awareness, treatment, and control of hyperten... 2017 2026 2020 2023 2017 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Meng Su China 9 439 190 182 129 126 22 1.2k
E. B. Mathiesen Norway 11 271 0.6× 82 0.4× 119 0.7× 54 0.4× 112 0.9× 14 1.1k
Alpo Vuorio Finland 21 305 0.7× 260 1.4× 113 0.6× 46 0.4× 138 1.1× 84 2.0k
Ingrid Mattiasson Sweden 24 463 1.1× 235 1.2× 144 0.8× 138 1.1× 122 1.0× 77 1.7k
Sang‐Wook Yi South Korea 26 229 0.5× 160 0.8× 186 1.0× 121 0.9× 49 0.4× 97 1.8k
Yunxia Wang China 15 258 0.6× 293 1.5× 91 0.5× 77 0.6× 36 0.3× 48 1.5k
Muhammad Hammadah United States 30 1.5k 3.4× 201 1.1× 176 1.0× 172 1.3× 51 0.4× 90 2.4k
Vicente Pallarés‐Carratalá Spain 19 764 1.7× 67 0.4× 161 0.9× 88 0.7× 43 0.3× 139 1.5k
H. Hämäläinen Finland 19 321 0.7× 151 0.8× 121 0.7× 73 0.6× 60 0.5× 34 1.2k
Patrick F. McArdle United States 21 160 0.4× 325 1.7× 137 0.8× 60 0.5× 28 0.2× 48 1.4k
Xiaoqing Bu China 17 492 1.1× 171 0.9× 135 0.7× 57 0.4× 16 0.1× 67 1.3k

Countries citing papers authored by Meng Su

Since Specialization
Citations

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

Fields of papers citing papers by Meng Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meng Su

This figure shows the co-authorship network connecting the top 25 collaborators of Meng Su. A scholar is included among the top collaborators of Meng Su 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 Meng Su. Meng Su 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.
Su, Meng, et al.. (2025). Machine-learning for discovery of descriptors for gas-sensing: A case study of doped metal oxides. Talanta. 287. 127594–127594. 1 indexed citations
2.
Su, Meng, Zihao Zhang, Man Li, et al.. (2025). Comparative disease burden of early‐onset and late‐onset type 2 diabetes in the U.S.: Evidence from NHANES 2003–2018. Journal of Diabetes Investigation. 16(9). 1750–1757.
3.
Su, Meng, Lihua Xie, Aurore Fleurie, et al.. (2024). Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy. Frontiers in Immunology. 15. 1450173–1450173. 8 indexed citations
4.
Su, Meng, Liu Na, Zhe Wu, et al.. (2024). Blood‐based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study. CNS Neuroscience & Therapeutics. 30(6). e14811–e14811. 1 indexed citations
5.
Zhang, Meng, et al.. (2021). The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis. Journal of Clinical Neuroscience. 89. 249–257. 5 indexed citations
7.
Zhao, Yinan, Yanguo Xin, Meng Su, Zhiyi He, & Wenyu Hu. (2019). Neurofilament light chain protein in neurodegenerative dementia: A systematic review and network meta-analysis. Neuroscience & Biobehavioral Reviews. 102. 123–138. 75 indexed citations
8.
Hernandez, Daniela, et al.. (2019). Role of CYP3A4 in bone marrow microenvironment–mediated protection of FLT3/ITD AML from tyrosine kinase inhibitors. Blood Advances. 3(6). 908–916. 57 indexed citations
9.
Su, Meng, Yu‐Ting Chang, Daniela Hernandez, Richard J. Jones, & Gabriel Ghiaur. (2019). Regulation of drug metabolizing enzymes in the leukaemic bone marrow microenvironment. Journal of Cellular and Molecular Medicine. 23(6). 4111–4117. 14 indexed citations
10.
Su, Meng, et al.. (2019). Vascular endothelial growth factor gene polymorphisms and hypertensive disorder of pregnancy: A meta-analysis. Pregnancy Hypertension. 17. 191–196. 3 indexed citations
11.
Chen, Xi, Yi Huang, Ling Lu, et al.. (2019). GATA2 mutations and overexpression in pediatric acute myeloid leukemia. SHILAP Revista de lepidopterología. 4(2). 56–63.
12.
Wang, Yu, et al.. (2018). Oxytocin improves animal behaviors and ameliorates oxidative stress and inflammation in autistic mice. Biomedicine & Pharmacotherapy. 107. 262–269. 60 indexed citations
13.
Lu, Jiapeng, Yuan Lu, Xiaochen Wang, et al.. (2017). Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). The Lancet. 390(10112). 2549–2558. 783 indexed citations breakdown →
14.
Karuppiah, Karmegam, et al.. (2016). Conceptual framework for the design of a child motorcycle safety seat. Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia). 1 indexed citations
15.
Su, Meng, Jace W. Jones, Jianshi Yu, et al.. (2015). All-Trans Retinoic Acid Activity in Acute Myeloid Leukemia: Role of Cytochrome P450 Enzyme Expression by the Microenvironment. PLoS ONE. 10(6). e0127790–e0127790. 53 indexed citations
16.
Su, Meng, et al.. (2014). Abstract 4842: The stem cell niche detoxifies chemotherapy and protects malignant hematopoietic cells via expression of cytochrome P450 enzymes. Cancer Research. 74(19_Supplement). 4842–4842. 1 indexed citations
18.
Su, Meng, Richard J. Jones, & Gabriel Ghiaur. (2013). Bone Marrow (BM) Stromal Expression Of Cytochrome P450 (CYP) Enzymes Protects Acute Myeloid Leukemia (AML) From All-Trans Retinoic Acid (atRA). Blood. 122(21). 1449–1449. 1 indexed citations
19.
Zhang, Jing, et al.. (2007). Study on Hypotensive and Vasodilatory Effects of Celery Juice. Food Science. 28(1). 322. 6 indexed citations
20.
Miklyaeva, Elena I., Weijia Dong, Alexandre Bureau, et al.. (2004). Late onset Tay–Sachs disease in mice with targeted disruption of the Hexa gene: behavioral changes and pathology of the central nervous system. Brain Research. 1001(1-2). 37–50. 28 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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