Haiyan Zhou

3.8k total citations
74 papers, 2.3k citations indexed

About

Haiyan Zhou is a scholar working on Molecular Biology, Genetics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Haiyan Zhou has authored 74 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 21 papers in Genetics and 13 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Haiyan Zhou's work include Neurogenetic and Muscular Disorders Research (21 papers), RNA modifications and cancer (13 papers) and RNA Research and Splicing (13 papers). Haiyan Zhou is often cited by papers focused on Neurogenetic and Muscular Disorders Research (21 papers), RNA modifications and cancer (13 papers) and RNA Research and Splicing (13 papers). Haiyan Zhou collaborates with scholars based in United Kingdom, China and United States. Haiyan Zhou's co-authors include Francesco Muntoni, Heinz Jungbluth, Caroline A. Sewry, Jennifer E. Morgan, Yoshinori Kuboki, Erdjan Salih, Melvin J. Glimcher, Susan Treves, Francesco Muntoni and Simon H. Parson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Haiyan Zhou

73 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haiyan Zhou United Kingdom 32 1.8k 764 435 285 250 74 2.3k
Erja Kerkelä Finland 27 1.3k 0.7× 481 0.6× 160 0.4× 400 1.4× 152 0.6× 52 2.5k
Sebahattin Çirak United Kingdom 22 2.1k 1.2× 392 0.5× 405 0.9× 118 0.4× 333 1.3× 41 2.3k
Anna Sárközy United Kingdom 30 2.4k 1.3× 192 0.3× 503 1.2× 273 1.0× 449 1.8× 91 2.9k
Gary R. Coulton United Kingdom 20 1.7k 0.9× 374 0.5× 332 0.8× 500 1.8× 247 1.0× 41 2.1k
Ieke B. Ginjaar Netherlands 21 2.2k 1.2× 477 0.6× 593 1.4× 142 0.5× 446 1.8× 37 2.6k
Satomi Mitsuhashi Japan 28 1.8k 1.0× 299 0.4× 333 0.8× 119 0.4× 454 1.8× 99 2.4k
Sasha Bogdanovich United States 21 1.9k 1.0× 252 0.3× 295 0.7× 217 0.8× 319 1.3× 33 2.3k
C. Jimenez‐Mallebrera Spain 25 1.5k 0.8× 295 0.4× 273 0.6× 168 0.6× 177 0.7× 78 1.9k
Graziella Messina Italy 24 2.1k 1.2× 662 0.9× 130 0.3× 778 2.7× 315 1.3× 45 2.6k
Techung Lee United States 24 809 0.4× 722 0.9× 468 1.1× 677 2.4× 97 0.4× 33 1.9k

Countries citing papers authored by Haiyan Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Haiyan Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haiyan Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Haiyan Zhou. A scholar is included among the top collaborators of Haiyan Zhou 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 Haiyan Zhou. Haiyan Zhou 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.
Zhang, Qiang, Ying Hong, Mariacristina Scoto, et al.. (2024). Profiling neuroinflammatory markers and response to nusinersen in paediatric spinal muscular atrophy. Scientific Reports. 14(1). 23491–23491. 4 indexed citations
2.
Xu, Tao, et al.. (2024). Effect of inflammatory factors on myocardial infarction. BMC Cardiovascular Disorders. 24(1). 538–538. 1 indexed citations
3.
Muntoni, Francesco, et al.. (2024). Strategies to improve the design of gapmer antisense oligonucleotide on allele-specific silencing. Molecular Therapy — Nucleic Acids. 35(3). 102237–102237. 6 indexed citations
4.
Chen, Lu, Yan He, Shijing Liu, et al.. (2024). Association of Angina, Myocardial Infarction and Atrial Fibrillation-A Bidirectional Mendelian Randomization Study. British Journal of Hospital Medicine. 85(9). 1–13. 3 indexed citations
5.
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7.
Zhou, Haiyan, et al.. (2022). PCSK9 inhibition protects against myocardial ischemia-reperfusion injury via suppressing autophagy. Microvascular Research. 142. 104371–104371. 27 indexed citations
8.
Gao, Xia, Tianzhu Lu, Yan Zhao, et al.. (2021). A reassessment of several erstwhile methods for isolating DNA fragments from agarose gels. 3 Biotech. 11(3). 138–138. 5 indexed citations
9.
Zhou, Haiyan & Francesco Muntoni. (2018). Morpholino-Mediated Exon Inclusion for SMA. Methods in molecular biology. 1828. 467–477. 2 indexed citations
10.
Bernabò, Paola, Toma Tebaldi, Ewout J. N. Groen, et al.. (2017). In Vivo Translatome Profiling in Spinal Muscular Atrophy Reveals a Role for SMN Protein in Ribosome Biology. Cell Reports. 21(4). 953–965. 85 indexed citations
11.
Voermans, Nicol C., Jo M. Wilmshurst, Komala Pillay, et al.. (2015). Epigenetic changes as a common trigger of muscle weakness in congenital myopathies. Human Molecular Genetics. 24(16). 4636–4647. 39 indexed citations
12.
Tagalakis, Aristides D., Dani Do Hyang Lee, Alison Bienemann, et al.. (2014). Multifunctional, self-assembling anionic peptide-lipid nanocomplexes for targeted siRNA delivery. Biomaterials. 35(29). 8406–8415. 71 indexed citations
13.
Zhou, Haiyan, Narinder Janghra, Chalermchai Mitrpant, et al.. (2013). A Novel Morpholino Oligomer Targeting ISS-N1 Improves Rescue of Severe Spinal Muscular Atrophy Transgenic Mice. Human Gene Therapy. 24(3). 331–342. 100 indexed citations
14.
Ullrich, Nina D., Martin Rausch, Vincent Mouly, et al.. (2013). Establishment of a human skeletal muscle-derived cell line: biochemical, cellular and electrophysiological characterization. Biochemical Journal. 455(2). 169–177. 18 indexed citations
15.
Jungbluth, Heinz, Tom Cullup, Suzanne Lillis, et al.. (2009). Centronuclear myopathy with cataracts due to a novel dynamin 2 (DNM2) mutation. Neuromuscular Disorders. 20(1). 49–52. 28 indexed citations
16.
Ghassemi, Farshid, Mirko Vukcevic, Le Xu, et al.. (2008). A recessive ryanodine receptor 1 mutation in a CCD patient increases channel activity. Cell Calcium. 45(2). 192–197. 12 indexed citations
17.
Jimenez‐Mallebrera, C., Haiyan Zhou, A. Manzur, et al.. (2007). C.P.4.16 Core myopathy without mutations in RYR1 or SEPN1 genes. Neuromuscular Disorders. 17(9-10). 883–883. 1 indexed citations
18.
Jungbluth, Heinz, Haiyan Zhou, Caroline A. Sewry, et al.. (2007). Centronuclear myopathy due to a de novo dominant mutation in the skeletal muscle ryanodine receptor (RYR1) gene. Neuromuscular Disorders. 17(4). 338–345. 82 indexed citations
19.
Jungbluth, Heinz, Haiyan Zhou, Louise Hartley, et al.. (2005). Minicore myopathy with ophthalmoplegia caused by mutations in the ryanodine receptor type 1 gene. Neurology. 65(12). 1930–1935. 100 indexed citations
20.
Zhou, Haiyan, Hiroko Takita, Ryuichi Fujisawa, Morimichi Mizuno, & Yoshinori Kuboki. (1995). Stimulation by bone sialoprotein of calcification in osteoblast-like MC3T3-E1 cells. Calcified Tissue International. 56(5). 403–407. 64 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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