May Win Naing

3.4k total citations
41 papers, 2.6k citations indexed

About

May Win Naing is a scholar working on Biomedical Engineering, Automotive Engineering and Molecular Biology. According to data from OpenAlex, May Win Naing has authored 41 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Biomedical Engineering, 12 papers in Automotive Engineering and 9 papers in Molecular Biology. Recurrent topics in May Win Naing's work include 3D Printing in Biomedical Research (25 papers), Additive Manufacturing and 3D Printing Technologies (12 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (5 papers). May Win Naing is often cited by papers focused on 3D Printing in Biomedical Research (25 papers), Additive Manufacturing and 3D Printing Technologies (12 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (5 papers). May Win Naing collaborates with scholars based in Singapore, United Kingdom and Russia. May Win Naing's co-authors include Wai Yee Yeong, Wei Long Ng, Deepak Choudhury, Tianyi Wang, Han W. Tun, Chee Kai Chua, Kah Fai Leong, Satnam Singh, Shuai Wang and Jia Min Lee and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

May Win Naing

41 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
May Win Naing Singapore 24 1.9k 1.1k 594 427 379 41 2.6k
Andrew C. Daly Ireland 20 3.0k 1.6× 1.1k 1.0× 809 1.4× 524 1.2× 426 1.1× 24 3.9k
Sylvain Catros France 29 3.0k 1.6× 1.5k 1.4× 707 1.2× 730 1.7× 379 1.0× 96 3.9k
Zongjie Wang Canada 25 2.9k 1.5× 1.3k 1.2× 491 0.8× 428 1.0× 722 1.9× 98 3.7k
Weitao Jia China 30 2.9k 1.5× 965 0.9× 714 1.2× 1.2k 2.8× 411 1.1× 81 4.1k
Matteo D’Este Switzerland 32 2.3k 1.2× 919 0.8× 943 1.6× 663 1.6× 410 1.1× 83 3.7k
Ugo D’Amora Italy 30 1.8k 0.9× 505 0.5× 983 1.7× 527 1.2× 224 0.6× 75 2.7k
Hyeongjin Lee South Korea 35 2.1k 1.1× 843 0.8× 1.1k 1.8× 771 1.8× 295 0.8× 94 2.9k
Carlos Mota Netherlands 33 2.7k 1.4× 1.2k 1.1× 1.2k 2.0× 638 1.5× 343 0.9× 112 3.7k
Wei Long Ng Singapore 26 2.5k 1.3× 1.7k 1.6× 522 0.9× 212 0.5× 273 0.7× 42 3.0k
Marco Costantini Poland 34 3.2k 1.7× 1.4k 1.3× 1.0k 1.8× 770 1.8× 551 1.5× 69 4.2k

Countries citing papers authored by May Win Naing

Since Specialization
Citations

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

Fields of papers citing papers by May Win Naing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by May Win Naing. 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 May Win Naing. The network helps show where May Win Naing may publish in the future.

Co-authorship network of co-authors of May Win Naing

This figure shows the co-authorship network connecting the top 25 collaborators of May Win Naing. A scholar is included among the top collaborators of May Win Naing 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 May Win Naing. May Win Naing 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.
Naing, May Win, et al.. (2025). Transition from manual to automated processes for autologous T cell therapy manufacturing using bioreactor with expandable culture area. Scientific Reports. 15(1). 15819–15819. 1 indexed citations
2.
Le, Bach Quang, et al.. (2023). Commercialization of skin substitutes for third-degree burn wounds. Trends in biotechnology. 42(4). 385–388. 5 indexed citations
3.
Naing, May Win, et al.. (2022). Rainfall and Landslide Susceptibility in Hakha Environ in Northern Chin State, Myanmar. 1–14. 3 indexed citations
4.
Wang, Who-Whong, et al.. (2022). In-situ scalable manufacturing of Epstein–Barr virus-specific T-cells using bioreactor with an expandable culture area (BECA). Scientific Reports. 12(1). 7045–7045. 2 indexed citations
5.
Russell, Tobias, et al.. (2021). Multi-pronged approach to human mesenchymal stromal cells senescence quantification with a focus on label-free methods. Scientific Reports. 11(1). 1054–1054. 19 indexed citations
6.
Liu, Dan, et al.. (2020). A review of manufacturing capabilities of cell spheroid generation technologies and future development. Biotechnology and Bioengineering. 118(2). 542–554. 67 indexed citations
7.
Choudhury, Deepak, et al.. (2020). Decellularization systems and devices: State-of-the-art. Acta Biomaterialia. 115. 51–59. 95 indexed citations
8.
Choudhury, Deepak, et al.. (2020). Commercialization of Organoids. Trends in Molecular Medicine. 26(3). 245–249. 35 indexed citations
9.
Kathawala, Mustafa Hussain, Wei Long Ng, Dan Liu, et al.. (2019). Healing of Chronic Wounds: An Update of Recent Developments and Future Possibilities. Tissue Engineering Part B Reviews. 25(5). 429–444. 79 indexed citations
10.
Singh, Satnam, Deepak Choudhury, Yu Fang, Vladimir Mironov, & May Win Naing. (2019). In situ bioprinting – Bioprinting from benchside to bedside?. Acta Biomaterialia. 101. 14–25. 211 indexed citations
11.
El‐Jawhari, Jehan J., et al.. (2019). Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions. Cytotherapy. 21(8). 803–819. 29 indexed citations
12.
Naing, May Win, et al.. (2019). Autofluorescence spectroscopy in redox monitoring across cell confluencies. PLoS ONE. 14(12). e0226757–e0226757. 10 indexed citations
13.
Choudhury, Deepak, Han W. Tun, Tianyi Wang, & May Win Naing. (2018). Organ-Derived Decellularized Extracellular Matrix: A Game Changer for Bioink Manufacturing?. Trends in biotechnology. 36(8). 787–805. 209 indexed citations
14.
Oh, Steve, et al.. (2018). Inertial-Based Filtration Method for Removal of Microcarriers from Mesenchymal Stem Cell Suspensions. Scientific Reports. 8(1). 12481–12481. 27 indexed citations
15.
Ng, Wei Long, Jia Min Lee, Wai Yee Yeong, & May Win Naing. (2017). Microvalve-based bioprinting – process, bio-inks and applications. Biomaterials Science. 5(4). 632–647. 182 indexed citations
16.
Ng, Wei Long, Wai Yee Yeong, & May Win Naing. (2016). Microvalve bioprinting of cellular droplets with high resolution and consistency. DR-NTU (Nanyang Technological University). 9 indexed citations
17.
Ng, Wei Long, Shuai Wang, Wai Yee Yeong, & May Win Naing. (2016). Skin Bioprinting: Impending Reality or Fantasy?. Trends in biotechnology. 34(9). 689–699. 196 indexed citations
18.
Ng, Wei Long, Wai Yee Yeong, & May Win Naing. (2014). Potential of Bioprinted Films for Skin Tissue Engineering. DR-NTU (Nanyang Technological University). 441–446. 21 indexed citations
19.
Naing, May Win, Daniel Gibson, Paul Hourd, et al.. (2014). Improving umbilical cord blood processing to increase total nucleated cell count yield and reduce cord input wastage by managing the consequences of input variation. Cytotherapy. 17(1). 58–67. 13 indexed citations
20.
Ratcliffe, Elizabeth, Katie Glen, May Win Naing, & David Williams. (2013). Current status and perspectives on stem cell-based therapies undergoing clinical trials for regenerative medicine: case studies. British Medical Bulletin. 108(1). 73–94. 53 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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