Yuan‐Kun Wu

788 total citations · 1 hit paper
15 papers, 591 citations indexed

About

Yuan‐Kun Wu is a scholar working on Molecular Biology, Genetics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Yuan‐Kun Wu has authored 15 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Genetics and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Yuan‐Kun Wu's work include Amyloidosis: Diagnosis, Treatment, Outcomes (5 papers), Mesenchymal stem cell research (4 papers) and Tissue Engineering and Regenerative Medicine (3 papers). Yuan‐Kun Wu is often cited by papers focused on Amyloidosis: Diagnosis, Treatment, Outcomes (5 papers), Mesenchymal stem cell research (4 papers) and Tissue Engineering and Regenerative Medicine (3 papers). Yuan‐Kun Wu collaborates with scholars based in Taiwan, United States and United Kingdom. Yuan‐Kun Wu's co-authors include Nai‐Chen Cheng, Chao‐Min Cheng, Jiashing Yu, Wei‐Han Lin, Wei‐Bor Tsai, Che-Wei Lin, Yuan‐Kun Tu, Hui-Chun Ku, Shih-Yi Lee and Kai‐Chien Yang and has published in prestigious journals such as Scientific Reports, Trends in biotechnology and Heart.

In The Last Decade

Yuan‐Kun Wu

14 papers receiving 585 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Biofilms in Chronic Wounds: Pathogenesis and Diagnosis

2018 359 citations Yuan‐Kun Wu, Nai‐Chen Cheng et al. Trends in biotechnology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuan‐Kun Wu Taiwan	9	192	173	166	159	120	15	591
Kamila Raziyeva Kazakhstan	8	443 2.3×	223 1.3×	347 2.1×	191 1.2×	191 1.6×	9	1.1k
Chuan Tian China	13	73 0.4×	132 0.8×	168 1.0×	144 0.9×	99 0.8×	40	572
Zharylkasyn Zharkinbekov Kazakhstan	8	437 2.3×	263 1.5×	346 2.1×	207 1.3×	177 1.5×	9	1.1k
Chenghao Gao China	10	186 1.0×	114 0.7×	188 1.1×	257 1.6×	103 0.9×	22	637
Wenting Chen China	14	113 0.6×	182 1.1×	133 0.8×	86 0.5×	37 0.3×	41	648
Yanxin Chen China	12	211 1.1×	95 0.5×	178 1.1×	145 0.9×	120 1.0×	35	628
Aimee E. Krausz United States	12	166 0.9×	104 0.6×	143 0.9×	76 0.5×	54 0.5×	25	754
Changqing Yuan China	13	135 0.7×	145 0.8×	143 0.9×	102 0.6×	87 0.7×	25	580
Sujing Qiang China	8	105 0.5×	142 0.8×	87 0.5×	162 1.0×	43 0.4×	12	461

Countries citing papers authored by Yuan‐Kun Wu

Since Specialization

Citations

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

Fields of papers citing papers by Yuan‐Kun Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan‐Kun Wu. 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 Yuan‐Kun Wu. The network helps show where Yuan‐Kun Wu may publish in the future.

Co-authorship network of co-authors of Yuan‐Kun Wu

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

1.

Qu, Fan, Yuan‐Kun Wu, Lin Yan, et al.. (2024). The CCL5/CCR5 axis in ulcerative colitis. Cellular Immunology. 407. 104891–104891. 3 indexed citations

2.

Wu, Yuan‐Kun, Mei‐Fang Cheng, Lung‐Chun Lin, et al.. (2024). Tafamidis improves myocardial longitudinal strain in A97S transthyretin cardiac amyloidosis. Therapeutic Advances in Chronic Disease. 15. 384271404–384271404. 2 indexed citations

3.

Tsai, Cheng‐Hsuan, Yuan‐Kun Wu, Mao‐Yuan Su, et al.. (2024). Use of Technetium‐99m‐Pyrophosphate Single‐Photon Emission Computed Tomography/Computed Tomography in Monitoring Therapeutic Changes of Eplontersen in Patients With Hereditary Transthyretin Amyloid Cardiomyopathy. Journal of the American Heart Association. 13(2). e030512–e030512. 12 indexed citations

4.

Tsai, Cheng‐Hsuan, Chi‐Chao Chao, Sung‐Tsang Hsieh, et al.. (2023). Tafamidis decreased cardiac amyloidosis deposition in patients with Ala97Ser hereditary transthyretin cardiomyopathy: a 12-month follow-up cohort study. Orphanet Journal of Rare Diseases. 18(1). 289–289. 7 indexed citations

5.

Tsai, Cheng‐Hsuan, Yuan‐Kun Wu, Mao‐Yuan Su, et al.. (2023). Efficacy of Tafamidis in Patients with Ala97Ser Hereditary Transthyretin Cardiac Amyloidosis: A Six-Month Follow-Up Study.. PubMed. 39(4). 619–627.

6.

Wu, Yuan‐Kun, Cheng‐Hsuan Tsai, Mao‐Yuan Su, et al.. (2022). Reverse Cardiac Remodelling and Dysfunction in A97S Transthyretin Cardiac Amyloidosis After Tafamidis Treatment. ESC Heart Failure. 9(6). 4335–4339. 6 indexed citations

7.

Wu, Yuan‐Kun, Yuan‐Kun Tu, Jiashing Yu, & Nai‐Chen Cheng. (2020). The Influence of Cell Culture Density on the Cytotoxicity of Adipose-Derived Stem Cells Induced by L-Ascorbic Acid-2-Phosphate. Scientific Reports. 10(1). 104–104. 29 indexed citations

8.

Chiang, Cho‐Han, Cho‐Hung Chiang, Weng‐Tein Gi, et al.. (2020). Safety and efficacy of the European Society of Cardiology 0/1-hour algorithm for diagnosis of myocardial infarction: systematic review and meta-analysis. Heart. 106(13). 985–991. 30 indexed citations

9.

Wu, Yuan‐Kun, Nai‐Chen Cheng, & Chao‐Min Cheng. (2018). Biofilms in Chronic Wounds: Pathogenesis and Diagnosis. Trends in biotechnology. 37(5). 505–517. 359 indexed citations breakdown →

10.

Ku, Hui-Chun, Shih-Yi Lee, Yuan‐Kun Wu, Kai‐Chien Yang, & Ming‐Jai Su. (2016). A Model of Cardiac Remodeling Through Constriction of the Abdominal Aorta in Rats. Journal of Visualized Experiments. 15 indexed citations

11.

Ku, Hui-Chun, Shih-Yi Lee, Yuan‐Kun Wu, Kai‐Chien Yang, & Ming‐Jai Su. (2016). A Model of Cardiac Remodeling Through Constriction of the Abdominal Aorta in Rats. Journal of Visualized Experiments. 9 indexed citations

12.

Yu, Jiashing, Yuan‐Kun Wu, Yiping Gu, et al.. (2015). Immuno‐modification of enhancing stem cells targeting for myocardial repair. Journal of Cellular and Molecular Medicine. 19(7). 1483–1491. 6 indexed citations

13.

Wu, Yuan‐Kun & Jiashing Yu. (2015). The role of tissue engineering in cellular therapies for myocardial infarction: a review. Journal of Materials Chemistry B. 3(31). 6401–6410. 11 indexed citations

14.

Yu, Jiashing, et al.. (2014). Electrospun PLGA Fibers Incorporated with Functionalized Biomolecules for Cardiac Tissue Engineering. Tissue Engineering Part A. 20(13-14). 1896–1907. 89 indexed citations

15.

Lin, Ching‐Yu, et al.. (2014). Maintenance of human adipose derived stem cell (hASC) differentiation capabilities using a 3D culture. Biotechnology Letters. 36(7). 1529–1537. 13 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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