Wei Qiang

4.1k total citations · 2 hit papers
65 papers, 3.1k citations indexed

About

Wei Qiang is a scholar working on Molecular Biology, Physiology and Spectroscopy. According to data from OpenAlex, Wei Qiang has authored 65 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 32 papers in Physiology and 24 papers in Spectroscopy. Recurrent topics in Wei Qiang's work include Alzheimer's disease research and treatments (31 papers), Advanced NMR Techniques and Applications (22 papers) and Supramolecular Self-Assembly in Materials (15 papers). Wei Qiang is often cited by papers focused on Alzheimer's disease research and treatments (31 papers), Advanced NMR Techniques and Applications (22 papers) and Supramolecular Self-Assembly in Materials (15 papers). Wei Qiang collaborates with scholars based in United States, China and United Kingdom. Wei Qiang's co-authors include Robert Tycko, Wai‐Ming Yau, Junxia Lu, Charles D. Schwieters, Stephen C. Meredith, John Collinge, Mark P. Mattson, Yongquan Luo, Kevin A. Kelley and Nikolaos G. Sgourakis and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Wei Qiang

63 papers receiving 3.1k citations

Hit Papers

Molecular Structure of β-Amyloid Fibrils in Alzheimer’s D... 2013 2026 2017 2021 2013 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
Wei Qiang United States 23 2.0k 2.0k 617 576 327 65 3.1k
Christiane Ritter Germany 21 1.8k 0.9× 2.2k 1.1× 565 0.9× 564 1.0× 383 1.2× 35 3.3k
Jüri Jarvet Sweden 31 1.3k 0.7× 1.7k 0.9× 357 0.6× 340 0.6× 283 0.9× 69 2.7k
Magdalena I. Ivanova United States 23 2.3k 1.1× 3.2k 1.6× 973 1.6× 291 0.5× 271 0.8× 50 4.4k
Anthony W. P. Fitzpatrick United States 19 2.2k 1.1× 2.5k 1.3× 836 1.4× 312 0.5× 201 0.6× 28 4.2k
Thorsten Lührs Switzerland 13 2.1k 1.0× 2.9k 1.4× 585 0.9× 357 0.6× 391 1.2× 21 4.0k
Marina Kirkitadze Canada 18 2.0k 1.0× 1.9k 0.9× 450 0.7× 166 0.3× 490 1.5× 37 3.1k
Johnny Habchi United Kingdom 32 1.1k 0.5× 1.9k 1.0× 270 0.4× 204 0.4× 299 0.9× 57 3.1k
Indu Kheterpal United States 29 1.5k 0.7× 1.8k 0.9× 323 0.5× 306 0.5× 178 0.5× 43 3.0k
Georg Meisl United Kingdom 38 3.6k 1.8× 3.4k 1.7× 1.2k 1.9× 219 0.4× 552 1.7× 104 5.9k
Marcin I. Apostol United States 14 1.4k 0.7× 2.2k 1.1× 641 1.0× 312 0.5× 137 0.4× 18 2.8k

Countries citing papers authored by Wei Qiang

Since Specialization
Citations

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

Fields of papers citing papers by Wei Qiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Qiang

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Qiang. A scholar is included among the top collaborators of Wei Qiang 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 Wei Qiang. Wei Qiang 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.
Scott, Faith J., James Z. Wang, Yan Sun, et al.. (2025). Structural convergence and membrane interactions of Aβ1-42 along the primary nucleation process studied by solid state NMR. Communications Chemistry. 8(1). 131–131. 1 indexed citations
2.
Qiang, Wei, et al.. (2024). Modulation of Lipid Dynamics in the β-Amyloid Aggregates Induced Membrane Fragmentation. The Journal of Physical Chemistry B. 128(23). 5667–5675. 3 indexed citations
4.
Vugmeyster, Liliya, et al.. (2024). Rigidifying of the internal dynamics of amyloid-beta fibrils generated in the presence of synaptic plasma vesicles. Physical Chemistry Chemical Physics. 26(6). 5466–5478. 1 indexed citations
5.
Qiang, Wei, et al.. (2023). Heterotypic Interactions between the 40- and 42-Residue Isoforms of β-Amyloid Peptides on Lipid Bilayer Surfaces. ACS Chemical Neuroscience. 14(23). 4153–4162. 2 indexed citations
6.
Li, Junxing, Xuemei Cui, Xuefeng Li, et al.. (2023). Isolation, Identification and Drug Resistance Rates of Bacteria from Pigs in Zhejiang and Surrounding Areas during 2019–2021. Veterinary Sciences. 10(8). 502–502. 6 indexed citations
7.
Qiang, Wei, et al.. (2021). Roles of key residues and lipid dynamics reveal pHLIP-membrane interactions at intermediate pH. Biophysical Journal. 120(21). 4649–4662. 12 indexed citations
8.
Wang, Peng, Jing Zhao, Fuming Zhang, et al.. (2021). Probing Amyloid β Interactions with Synthetic Heparan Sulfate Oligosaccharides. ACS Chemical Biology. 16(10). 1894–1899. 5 indexed citations
9.
Paul, Subhradip, et al.. (2021). Application of DNP-enhanced solid-state NMR to studies of amyloid-β peptide interaction with lipid membranes. Chemistry and Physics of Lipids. 236. 105071–105071. 9 indexed citations
10.
11.
Qiang, Wei, et al.. (2020). Time-Dependent Lipid Dynamics, Organization and Peptide-Lipid Interaction in Phospholipid Bilayers with Incorporated β-Amyloid Oligomers. The Journal of Physical Chemistry Letters. 11(19). 8329–8336. 13 indexed citations
12.
Hu, Zhiwen, et al.. (2020). N-Terminal Modified Aβ Variants Enable Modulations to the Structures and Cytotoxicity Levels of Wild-Type Aβ Fibrils through Cross-Seeding. ACS Chemical Neuroscience. 11(14). 2058–2065. 10 indexed citations
13.
Vugmeyster, Liliya, et al.. (2019). Effect of Post-Translational Modifications and Mutations on Amyloid-β Fibrils Dynamics at N Terminus. Biophysical Journal. 117(8). 1524–1535. 16 indexed citations
14.
Xu, Dawei, Weike Chen, Carolyn R. Sturge, et al.. (2018). Fabrication and Microscopic and Spectroscopic Characterization of Cytocompatible Self-Assembling Antimicrobial Nanofibers. ACS Infectious Diseases. 4(9). 1327–1335. 41 indexed citations
15.
Qiang, Wei, et al.. (2018). Model Phospholipid Liposomes to Study the β-Amyloid-Peptide-Induced Membrane Disruption. Methods in molecular biology. 1777. 355–367. 6 indexed citations
16.
Hu, Zhiwen, et al.. (2018). The on-fibrillation-pathway membrane content leakage and off-fibrillation-pathway lipid mixing induced by 40-residue β-amyloid peptides in biologically relevant model liposomes. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1860(9). 1670–1680. 20 indexed citations
17.
18.
Vugmeyster, Liliya, et al.. (2017). Solvent-Driven Dynamical Crossover in the Phenylalanine Side-Chain from the Hydrophobic Core of Amyloid Fibrils Detected by 2H NMR Relaxation. The Journal of Physical Chemistry B. 121(30). 7267–7275. 13 indexed citations
19.
Xu, Dawei, et al.. (2016). Membrane activity of a supramolecular peptide-based chemotherapeutic enhancer. Molecular BioSystems. 12(9). 2695–2699. 8 indexed citations
20.
Xu, Dawei, et al.. (2016). Distinct Membrane Disruption Pathways Are Induced by 40-Residue β-Amyloid Peptides. Journal of Biological Chemistry. 291(23). 12233–12244. 50 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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