Mengli Chang

1.2k total citations · 1 hit paper
11 papers, 914 citations indexed

About

Mengli Chang is a scholar working on Neurology, Molecular Biology and Pharmacology. According to data from OpenAlex, Mengli Chang has authored 11 papers receiving a total of 914 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Neurology, 3 papers in Molecular Biology and 3 papers in Pharmacology. Recurrent topics in Mengli Chang's work include Neurological Disease Mechanisms and Treatments (2 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers) and Atherosclerosis and Cardiovascular Diseases (1 paper). Mengli Chang is often cited by papers focused on Neurological Disease Mechanisms and Treatments (2 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers) and Atherosclerosis and Cardiovascular Diseases (1 paper). Mengli Chang collaborates with scholars based in China, United States and Switzerland. Mengli Chang's co-authors include John C. Mathison, David A. Greenwald, Richard J. Ulevitch, J D Hulmes, Bruce Beutler, Y C Pan, Anthony Cerami, Robert S. Lees, Andrew Lees and Junyuan Zhang and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Mengli Chang

8 papers receiving 852 citations

Hit Papers

Identity of tumour necrosis factor and the macrophage-sec... 1985 2026 1998 2012 1985 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
Mengli Chang China 4 394 261 183 157 88 11 914
Mingfang Lu United States 18 455 1.2× 301 1.2× 115 0.6× 111 0.7× 60 0.7× 31 863
Yonggang Sha United States 13 573 1.5× 383 1.5× 99 0.5× 119 0.8× 129 1.5× 18 1.1k
Dequina Nicholas United States 13 338 0.9× 302 1.2× 101 0.6× 98 0.6× 101 1.1× 34 878
Florence Castelli France 18 435 1.1× 539 2.1× 198 1.1× 130 0.8× 122 1.4× 54 1.1k
Joanne A. O’Donnell Australia 18 509 1.3× 664 2.5× 194 1.1× 176 1.1× 100 1.1× 29 1.3k
Daniel A. Patten United Kingdom 16 335 0.9× 467 1.8× 168 0.9× 316 2.0× 116 1.3× 35 1.1k
Leah M. Flick United States 12 358 0.9× 349 1.3× 192 1.0× 94 0.6× 119 1.4× 15 1.0k
Zhenghui Liu China 20 476 1.2× 335 1.3× 127 0.7× 128 0.8× 140 1.6× 58 1.3k
Chantal S. Colmont United Kingdom 15 461 1.2× 469 1.8× 172 0.9× 92 0.6× 143 1.6× 19 1.2k

Countries citing papers authored by Mengli Chang

Since Specialization
Citations

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

Fields of papers citing papers by Mengli Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mengli Chang

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

All Works

11 of 11 papers shown
1.
Li, Yu, et al.. (2025). Multi-omics approaches reveal the therapeutic mechanism of Naoxintong capsule against ischemic stroke. Journal of Ethnopharmacology. 343. 119435–119435. 2 indexed citations
2.
Zhang, Junyuan, et al.. (2025). An explainable machine learning method for predicting and designing crashworthiness of multi-cell tubes under oblique load. Engineering Applications of Artificial Intelligence. 147. 110396–110396. 3 indexed citations
3.
Wang, Huanhuan, Mengli Chang, Mengting Liu, et al.. (2024). Naoxintong capsules exhibited a protective effect against cerebral ischemia in the brain hub region of MCAO rats via intervention of amino acid metabolism. Arabian Journal of Chemistry. 17(4). 105659–105659.
4.
Chang, Mengli, et al.. (2024). Proteomic study of left ventricle and cortex in rats after myocardial infarction. Scientific Reports. 14(1). 6866–6866. 3 indexed citations
6.
Wei, Junying, et al.. (2024). Improvement of myocardial fibrosis injury by Shengmai injection in ischemia-induced heart failure in a rat model. SHILAP Revista de lepidopterología. 2(3). 245–253. 1 indexed citations
7.
8.
Li, Yu, et al.. (2024). [Screening and evaluation of core prescriptions for impotence based on network robustness and data mining].. PubMed. 49(15). 4230–4237. 1 indexed citations
9.
Chang, Mengli, et al.. (2023). Effect of Naoxintong Capsule on Microglia and Proteomics of Cortex After Myocardial Infarction in Rats. Molecular Neurobiology. 61(5). 2904–2920. 3 indexed citations
10.
Chang, Mengli, Andrew Lees, & Robert S. Lees. (1992). Time course of 125I-labeled LDL accumulation in the healing, balloon-deendothelialized rabbit aorta.. Arteriosclerosis and Thrombosis A Journal of Vascular Biology. 12(9). 1088–1098. 17 indexed citations
11.
Beutler, Bruce, David A. Greenwald, J D Hulmes, et al.. (1985). Identity of tumour necrosis factor and the macrophage-secreted factor cachectin. Nature. 316(6028). 552–554. 884 indexed citations breakdown →

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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