Napat Leeaphorn

1.2k total citations
48 papers, 649 citations indexed

About

Napat Leeaphorn is a scholar working on Transplantation, Pulmonary and Respiratory Medicine and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Napat Leeaphorn has authored 48 papers receiving a total of 649 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Transplantation, 15 papers in Pulmonary and Respiratory Medicine and 15 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Napat Leeaphorn's work include Renal Transplantation Outcomes and Treatments (27 papers), Renal and Vascular Pathologies (14 papers) and Organ Transplantation Techniques and Outcomes (9 papers). Napat Leeaphorn is often cited by papers focused on Renal Transplantation Outcomes and Treatments (27 papers), Renal and Vascular Pathologies (14 papers) and Organ Transplantation Techniques and Outcomes (9 papers). Napat Leeaphorn collaborates with scholars based in United States, Thailand and Hungary. Napat Leeaphorn's co-authors include Patompong Ungprasert, Eliyahu V. Khankin, Francesca Cardarelli, Martha Pavlakis, Charat Thongprayoon, Wisit Cheungpasitporn, Michael A. Mao, Natanong Thamcharoen, Neetika Garg and Michael B. Stokes and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and American Journal of Kidney Diseases.

In The Last Decade

Napat Leeaphorn

44 papers receiving 640 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Napat Leeaphorn United States	13	198	182	176	113	109	48	649
Francesca Cardarelli United States	16	296 1.5×	280 1.5×	130 0.7×	223 2.0×	77 0.7×	34	798
Johnny Sayegh France	15	274 1.4×	134 0.7×	96 0.5×	205 1.8×	163 1.5×	34	661
Joshua Kausman Australia	15	208 1.1×	195 1.1×	78 0.4×	126 1.1×	93 0.9×	35	692
Roman Reindl‐Schwaighofer Austria	19	309 1.6×	160 0.9×	145 0.8×	247 2.2×	71 0.7×	54	885
M. Messina Italy	18	347 1.8×	190 1.0×	113 0.6×	223 2.0×	75 0.7×	55	850
Makoto Tsujita Japan	17	292 1.5×	227 1.2×	113 0.6×	172 1.5×	133 1.2×	64	702
Mireille El Ters United States	12	228 1.2×	219 1.2×	57 0.3×	154 1.4×	100 0.9×	24	533
Manel Perelló Spain	13	323 1.6×	114 0.6×	106 0.6×	164 1.5×	138 1.3×	31	547
Thomas Crépin France	13	162 0.8×	207 1.1×	152 0.9×	110 1.0×	90 0.8×	36	617
Berna Yelken Türkiye	14	123 0.6×	165 0.9×	57 0.3×	107 0.9×	47 0.4×	42	500

Countries citing papers authored by Napat Leeaphorn

Since Specialization

Citations

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

Fields of papers citing papers by Napat Leeaphorn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Napat Leeaphorn

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

All Works

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20 of 20 papers shown

Thongprayoon, Charat, Oscar A. Garcia Valencia, Caroline C. Jadlowiec, et al.. (2025). Impact of public versus non public insurance on hispanic kidney transplant outcomes using UNOS database. Scientific Reports. 15(1). 4879–4879. 1 indexed citations

Wadei, Hani M., Michael A. Mao, Shennen A. Mao, et al.. (2025). The Impact of Epstein–Barr Virus Serostatus Mismatch in Adult Kidney Transplant Recipients: An Analysis of the 2012–2022 OPTN Database. Clinical Transplantation. 39(3). e70117–e70117.

Leeaphorn, Napat, Hani M. Wadei, Shennen A. Mao, et al.. (2024). Regional Differences and Temporal Changes in the Utilization of HCV-Viremic Donors in Kidney Transplantation. Transplantation Proceedings. 56(6). 1513–1521.

Wadei, Hani M., Michael A. Mao, Shennen A. Mao, et al.. (2024). The impact of induction therapy on the risk of posttransplant lymphoproliferative disorder in adult kidney transplant recipients with donor-recipient serological Epstein-Barr virus mismatch. American Journal of Transplantation. 24(8). 1486–1494. 5 indexed citations

Valencia, Oscar A. Garcia, Charat Thongprayoon, Caroline C. Jadlowiec, et al.. (2024). Evaluating Global and Temporal Trends in Pancreas and Islet Cell Transplantation: Public Awareness and Engagement. SHILAP Revista de lepidopterología. 14(2). 590–601. 3 indexed citations

Valencia, Oscar A. Garcia, Charat Thongprayoon, Jing Miao, et al.. (2024). Empowering inclusivity: improving readability of living kidney donation information with ChatGPT. Frontiers in Digital Health. 6. 1366967–1366967. 15 indexed citations

Thongprayoon, Charat, Supawit Tangpanithandee, Caroline C. Jadlowiec, et al.. (2023). Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care. Journal of Personalized Medicine. 13(8). 1273–1273. 1 indexed citations

Jadlowiec, Caroline C., Charat Thongprayoon, Supawit Tangpanithandee, et al.. (2023). Re‐assessing prolonged cold ischemia time in kidney transplantation through machine learning consensus clustering. Clinical Transplantation. 38(1). e15201–e15201. 3 indexed citations

Thongprayoon, Charat, Jing Miao, Caroline C. Jadlowiec, et al.. (2023). Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering. Medicina. 59(5). 977–977. 2 indexed citations

10.

Thongprayoon, Charat, Jing Miao, Caroline C. Jadlowiec, et al.. (2023). Differences between Kidney Transplant Recipients from Deceased Donors with Diabetes Mellitus as Identified by Machine Learning Consensus Clustering. Journal of Personalized Medicine. 13(7). 1094–1094. 2 indexed citations

11.

Thongprayoon, Charat, Pradeep Vaitla, Caroline C. Jadlowiec, et al.. (2023). Differences between kidney retransplant recipients as identified by machine learning consensus clustering. Clinical Transplantation. 37(5). e14943–e14943. 2 indexed citations

12.

Thongprayoon, Charat, Pradeep Vaitla, Caroline C. Jadlowiec, et al.. (2023). Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering. SHILAP Revista de lepidopterología. 10(4). 25–25. 1 indexed citations

13.

Thongprayoon, Charat, Caroline C. Jadlowiec, Shennen A. Mao, et al.. (2023). Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering. SHILAP Revista de lepidopterología. 5(1). e000137–e000137. 4 indexed citations

14.

Thongprayoon, Charat, Yeshwanter Radhakrishnan, Caroline C. Jadlowiec, et al.. (2022). Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering. Journal of Personalized Medicine. 12(12). 1992–1992. 3 indexed citations

15.

Tangpanithandee, Supawit, Charat Thongprayoon, Caroline C. Jadlowiec, et al.. (2022). Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering. Medicina. 58(12). 1831–1831. 2 indexed citations

16.

Thongprayoon, Charat, Shennen A. Mao, Caroline C. Jadlowiec, et al.. (2022). Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States. Journal of Clinical Medicine. 11(12). 3288–3288. 7 indexed citations

17.

Leeaphorn, Napat, Charat Thongprayoon, Pradeep Vaitla, et al.. (2021). Outcomes of Kidney Transplant Recipients with Sickle Cell Disease: An Analysis of the 2000–2019 UNOS/OPTN Database. Journal of Clinical Medicine. 10(14). 3063–3063. 6 indexed citations

18.

Thongprayoon, Charat, Javier A. Neyra, Panupong Hansrivijit, et al.. (2020). Serum Klotho in Living Kidney Donors and Kidney Transplant Recipients: A Meta-Analysis. Journal of Clinical Medicine. 9(6). 1834–1834. 16 indexed citations

19.

Leeaphorn, Napat, et al.. (2014). Prevalence of Cancer in Membranous Nephropathy: A Systematic Review and Meta-Analysis of Observational Studies. American Journal of Nephrology. 40(1). 29–35. 79 indexed citations

20.

Ungprasert, Patompong, Napat Leeaphorn, Wonngarm Kittanamongkolchai, & Wisit Cheungpasitporn. (2014). Safety profile of denosumab. 2(6). 504.

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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