Ming-Mei Shang

1.1k total citations
18 papers, 401 citations indexed

About

Ming-Mei Shang is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Ming-Mei Shang has authored 18 papers receiving a total of 401 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology, 5 papers in Molecular Biology and 4 papers in Oncology. Recurrent topics in Ming-Mei Shang's work include Immune Cell Function and Interaction (3 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers) and Atherosclerosis and Cardiovascular Diseases (2 papers). Ming-Mei Shang is often cited by papers focused on Immune Cell Function and Interaction (3 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers) and Atherosclerosis and Cardiovascular Diseases (2 papers). Ming-Mei Shang collaborates with scholars based in Sweden, China and United States. Ming-Mei Shang's co-authors include Jesper Tegnér, Angelika Schmidt, Heiko Weyd, Bertil B. Fredholm, Ilaria Tonazzini, Elisabetta Daré, Narsis A. Kiani, Josefin Skogsberg, Johan Björkegren and Husain A. Talukdar and has published in prestigious journals such as PLoS ONE, Scientific Reports and Genome Research.

In The Last Decade

Ming-Mei Shang

15 papers receiving 397 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming-Mei Shang Sweden	9	158	136	57	50	36	18	401
Anna S. Wenning Switzerland	8	191 1.2×	221 1.6×	77 1.4×	52 1.0×	44 1.2×	17	556
Julia Leemput France	10	270 1.7×	167 1.2×	82 1.4×	15 0.3×	64 1.8×	16	553
Fei Qian China	12	91 0.6×	216 1.6×	63 1.1×	38 0.8×	31 0.9×	26	474
Tongju Guan United States	11	77 0.5×	319 2.3×	34 0.6×	30 0.6×	29 0.8×	19	571
Lerna Gulluyan Australia	9	126 0.8×	151 1.1×	35 0.6×	15 0.3×	35 1.0×	9	389
Damien Maggiorani France	11	110 0.7×	226 1.7×	37 0.6×	10 0.2×	28 0.8×	13	497
Shuilong Leng China	11	80 0.5×	294 2.2×	56 1.0×	18 0.4×	15 0.4×	18	492
Maria Rosa Accomazzo Italy	14	80 0.5×	218 1.6×	26 0.5×	51 1.0×	17 0.5×	23	455
Manini Bhatt India	3	55 0.3×	236 1.7×	44 0.8×	17 0.3×	14 0.4×	4	448
Christie‐Ann McCarl United States	7	236 1.5×	239 1.8×	51 0.9×	62 1.2×	13 0.4×	9	693

Countries citing papers authored by Ming-Mei Shang

Since Specialization

Citations

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

Fields of papers citing papers by Ming-Mei Shang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Mei Shang. 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 Ming-Mei Shang. The network helps show where Ming-Mei Shang may publish in the future.

Co-authorship network of co-authors of Ming-Mei Shang

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Mei Shang. A scholar is included among the top collaborators of Ming-Mei Shang 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 Ming-Mei Shang. Ming-Mei Shang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Chen, Yujie, Yiran Hou, Qi Zeng, et al.. (2025). Common and specific gene regulatory programs in zebrafish caudal fin regeneration at single-cell resolution. Genome Research. 35(1). 202–218.

Gupta, Neeti, et al.. (2024). Patient Reported Quality of Life after Chemoradiation for Esophageal Cancer: Analysis of Clinical and Dosimetric Predictors. International Journal of Radiation Oncology*Biology*Physics. 120(2). e364–e365.

Li, Zhiqiang, et al.. (2024). Magnetic resonance cholangiopancreatography at 5.0 T: quantitative and qualitative comparison with 3.0 T. BMC Medical Imaging. 24(1). 331–331.

Li, Yiquan, Mengting Wu, Lu Yu, et al.. (2023). OTUD1 ameliorates cerebral ischemic injury through inhibiting inflammation by disrupting K63-linked deubiquitination of RIP2. Journal of Neuroinflammation. 20(1). 281–281. 15 indexed citations

Jiang, Long, Ming-Mei Shang, Fredrik Wermeling, et al.. (2023). High‐content screening of drug combinations of an mPGES ‐1 inhibitor in multicellular tumor spheroids leads to mechanistic insights into neuroblastoma chemoresistance. Molecular Oncology. 18(2). 317–335. 5 indexed citations

Bergqvist, Filip, Yvonne Sundström, Ming-Mei Shang, et al.. (2020). Anti-Inflammatory Properties of Chemical Probes in Human Whole Blood: Focus on Prostaglandin E2 Production. Frontiers in Pharmacology. 11. 613–613. 6 indexed citations

Sundström, Yvonne, Ming-Mei Shang, Sudeepta Kumar Panda, et al.. (2020). Identifying novel B-cell targets for chronic inflammatory autoimmune disease by screening of chemical probes in a patient-derived cell assay. Translational research. 229. 69–82. 2 indexed citations

Morikawa, Hiromasa, Narsis A. Kiani, Muyi Yang, et al.. (2019). Exhaustion of CD4+ T-cells mediated by the Kynurenine Pathway in Melanoma. Scientific Reports. 9(1). 12150–12150. 62 indexed citations

Shang, Ming-Mei, et al.. (2019). Phosphatase inhibitor PPP1R11 modulates resistance of human T cells toward Treg-mediated suppression of cytokine expression. Journal of Leukocyte Biology. 106(2). 413–430. 14 indexed citations

10.

Vasaikar, Suhas, Natalia Landázuri, Helena Costa, et al.. (2018). Overexpression of endothelin B receptor in glioblastoma: a prognostic marker and therapeutic target?. BMC Cancer. 18(1). 154–154. 21 indexed citations

11.

Shang, Ming-Mei, Christine Moessinger, Hassan Foroughi Asl, et al.. (2017). Poliovirus Receptor–Related 2. Arteriosclerosis Thrombosis and Vascular Biology. 37(3). 534–542. 21 indexed citations

12.

Schmidt, Angelika, et al.. (2016). Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate. PLoS ONE. 11(2). e0148474–e0148474. 89 indexed citations

13.

Kiani, Narsis A., Ming-Mei Shang, & Jesper Tegnér. (2016). Systems Toxicology: Systematic Approach to Predict Toxicity. Current Pharmaceutical Design. 22(46). 6911–6917. 5 indexed citations

14.

Björkegren, Johan, Sara Hägg, Husain A. Talukdar, et al.. (2014). Plasma Cholesterol–Induced Lesion Networks Activated before Regression of Early, Mature, and Advanced Atherosclerosis. PLoS Genetics. 10(2). e1004201–e1004201. 50 indexed citations

15.

Wang, Qingqing, Xiaoshuang Li, Shujia Dai, et al.. (2008). Quantification of puerarin in plasma by on-line solid-phase extraction column switching liquid chromatography–tandem mass spectrometry and its applications to a pharmacokinetic study. Journal of Chromatography B. 863(1). 55–63. 26 indexed citations

16.

Shang, Ming-Mei, et al.. (2008). Adenosine A1 and A3 receptors protect astrocytes from hypoxic damage. European Journal of Pharmacology. 596(1-3). 6–13. 76 indexed citations

17.

Du, Haiyan, Haifeng Song, Na Li, et al.. (2007). Structure-efficacy relationships of immunostimulatory activity of CpG-containing oligodeoxynucleotides on mouse spleen cells. Acta Pharmacologica Sinica. 28(10). 1637–1644. 7 indexed citations

18.

Shang, Ming-Mei, et al.. (2004). Detection and quantitation of PS20, a phosphorothioate oligodeoxynucleotides in monkey plasma.. PubMed. 25(6). 801–6. 2 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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