Q Li

1.0k total citations
9 papers, 822 citations indexed

About

Q Li is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Q Li has authored 9 papers receiving a total of 822 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Cardiology and Cardiovascular Medicine and 2 papers in Surgery. Recurrent topics in Q Li's work include Cardiac Fibrosis and Remodeling (2 papers), Cardiovascular, Neuropeptides, and Oxidative Stress Research (1 paper) and Coagulation, Bradykinin, Polyphosphates, and Angioedema (1 paper). Q Li is often cited by papers focused on Cardiac Fibrosis and Remodeling (2 papers), Cardiovascular, Neuropeptides, and Oxidative Stress Research (1 paper) and Coagulation, Bradykinin, Polyphosphates, and Angioedema (1 paper). Q Li collaborates with scholars based in China, United States and Netherlands. Q Li's co-authors include Annarosa Leri, Piero Anversa, Jan Kajstura, B Li, Xiaohui Wang, Renato Baserga, Krzysztof Reiss, Shanshan Wang, Xiaodong Wang and Pier Paolo Claudio and has published in prestigious journals such as Journal of Clinical Investigation, Clinical and Experimental Pharmacology and Physiology and International Journal of Clinical Practice.

In The Last Decade

Q Li

6 papers receiving 807 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Q Li China	6	464	395	151	136	110	9	822
Rosemeire M. Kanashiro‐Takeuchi United States	18	429 0.9×	418 1.1×	202 1.3×	168 1.2×	60 0.5×	41	947
Nelly Cemerlang Australia	10	375 0.8×	390 1.0×	80 0.5×	54 0.4×	66 0.6×	10	702
Jingzang Tao United States	5	545 1.2×	341 0.9×	159 1.1×	69 0.5×	209 1.9×	7	970
Pamela C. Powell United States	19	438 0.9×	635 1.6×	239 1.6×	82 0.6×	60 0.5×	32	1.1k
Hugues Gosselin Canada	19	409 0.9×	582 1.5×	178 1.2×	75 0.6×	73 0.7×	33	983
Marta Paiva United Kingdom	11	456 1.0×	163 0.4×	216 1.4×	89 0.7×	124 1.1×	12	691
Frank Diet Germany	11	364 0.8×	566 1.4×	172 1.1×	149 1.1×	53 0.5×	18	872
Robert Clément Canada	16	286 0.6×	324 0.8×	122 0.8×	63 0.5×	75 0.7×	29	739
Masaki Shimoyama Japan	16	513 1.1×	506 1.3×	117 0.8×	57 0.4×	78 0.7×	30	856
Ryuichiro Murakami Japan	19	302 0.7×	271 0.7×	303 2.0×	104 0.8×	60 0.5×	33	974

Countries citing papers authored by Q Li

Since Specialization

Citations

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

Fields of papers citing papers by Q Li

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Q Li

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

All Works

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9 of 9 papers shown

Li, Q, et al.. (2025). Protective Effects of Polygonatum sibiricum Polysaccharides Against Type 2 Diabetic Mice Induced by High-Fat Diet and Low-Dose Streptozotocin. Toxics. 13(4). 255–255.

Miao, Miao, et al.. (2025). Evaluation of the Effects of High Uric Acid on Glucolipid Metabolism, Renal Injury and the Gut Microbiota in Diabetic Male Hamsters with Dyslipidemia. Toxics. 13(9). 751–751.

Cheng, Jin-Wei, et al.. (2012). Combination therapy an ACE inhibitor and an angiotensin receptor blocker for IgA nephropathy: a meta-analysis. International Journal of Clinical Practice. 66(10). 917–923. 32 indexed citations

Li, Hong, Sa Shi, Yihua Sun, et al.. (2008). DOPAMINE D2 RECEPTOR STIMULATION INHIBITS ANGIOTENSIN II‐INDUCED HYPERTROPHY IN CULTURED NEONATAL RAT VENTRICULAR MYOCYTES. Clinical and Experimental Pharmacology and Physiology. 36(3). 312–318. 13 indexed citations

Feng, Yi, et al.. (2002). INSERTION/DELETION POLYMORPHISM OF THE ACE GENE IS ASSOCIATED WITH T2D. 51(6). 1986–1988. 1 indexed citations

Li, Q, Martin Pfaffendorf, & Pieter A. van Zwieten. (2001). Different types of antagonism by losartan and irbesartan on the effects of angiotensin II and its degradation products in rabbit arteries. Fundamental and Clinical Pharmacology. 15(2). 143–150. 5 indexed citations

Leri, Annarosa, Pier Paolo Claudio, Q Li, et al.. (1998). Stretch-mediated release of angiotensin II induces myocyte apoptosis by activating p53 that enhances the local renin-angiotensin system and decreases the Bcl-2-to-Bax protein ratio in the cell.. Journal of Clinical Investigation. 101(7). 1326–1342. 375 indexed citations

Wu, Shengbiao, K. Xiang, Xinyi Lin, et al.. (1998). Relationship between angiotensin I converting enzyme gene polymorphism and diabetic nephropathy.. PubMed. 111(5). 478–9. 6 indexed citations

Li, Q, B Li, Xiaohui Wang, et al.. (1997). Overexpression of insulin-like growth factor-1 in mice protects from myocyte death after infarction, attenuating ventricular dilation, wall stress, and cardiac hypertrophy.. Journal of Clinical Investigation. 100(8). 1991–1999. 390 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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