Fengna Chu

28 papers receiving 947 citations

Fengna Chu's Hit Papers

Impact of Anti-amyloid-β Monoclonal Antibodies on the Pathology and Clinical Profile of Alzheimer’s Disease: A Focus on Aducanumab and Lecanemab 2022 · 184 citations
1840+1+2Years since publication50100150

Peers

Fengna Chu
Comparison fields: 5 of 92
  • Biological Psychiatry 119
  • Neurology 237
  • Developmental Neuroscience 47
  • Immunology 232
  • Physiology 255
Replace Mingchao Shi with:
Mingchao Shi China
Massimo Barbierato Italy
Yuqing Yan China
Francesca La Rosa Italy
Long Wang China
Roser Gorina Spain
Manjeet K. Paintlia United States
Emily Tjon United States
Xiaomeng Ma China
Jun Tan China
Fengna Chu relative to Mingchao Shi China Mingchao Shi's profile →
Citations per field
00.5×1.7×
Mingchao Shi · 1×
Citations per year

Countries citing papers authored by Fengna Chu

Since Specialization
Citations

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

Fields of papers citing papers by Fengna Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Fengna Chu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fengna Chu Line = papers co-authored together Fengna Chu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018241
2
Impact of Anti-amyloid-β Monoclonal Antibodies on the Pathology and Clinical Profile of Alzheimer’s Disease: A Focus on Aducanumab and Lecanemab
Hit paper breakdown →
2022184
3 2018114
4 202098
5 201858
6 202043
7 201830
8 202426
9 202224
10 202121
11 202119
12 201813
13 202112
14 202112
15 202111
16 202110
17 20218
18 20187
19 20236
20 20135

About Fengna Chu

Fengna Chu is a scholar working on Molecular Biology, Physiology, Immunology, Neurology and Biological Psychiatry, having authored 28 papers that have together received 959 indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (6 papers), Tryptophan and brain disorders (6 papers), Peripheral Neuropathies and Disorders (6 papers), Neuroinflammation and Neurodegeneration Mechanisms (5 papers), Immune Response and Inflammation (4 papers), Multiple Sclerosis Research Studies (4 papers), Cytokine Signaling Pathways and Interactions (2 papers) and Protein Tyrosine Phosphatases (2 papers). The work is most often cited by research in Biological Psychiatry (119 citations), Neurology (237 citations), Developmental Neuroscience (47 citations), Immunology (232 citations) and Physiology (255 citations). Fengna Chu has collaborated with scholars based in China, Sweden and Mexico. Frequent co-authors include Jie Zhu, Mingchao Shi, Donghui Shen, Li Cui, Chao Zheng, Feiqi Zhu, Tao Jin, Xiangyu Zheng, Yue Lang and Jingtao Chen. Their work appears in journals such as Clinical & Experimental Immunology, Frontiers in Aging Neuroscience, Autoimmunity Reviews, Experimental Neurology and Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease.

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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