Madhav C. Menon

3.2k total citations
67 papers, 1.4k citations indexed

About

Madhav C. Menon is a scholar working on Surgery, Nephrology and Transplantation. According to data from OpenAlex, Madhav C. Menon has authored 67 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Surgery, 22 papers in Nephrology and 22 papers in Transplantation. Recurrent topics in Madhav C. Menon's work include Renal Transplantation Outcomes and Treatments (22 papers), Renal Diseases and Glomerulopathies (14 papers) and Organ Transplantation Techniques and Outcomes (11 papers). Madhav C. Menon is often cited by papers focused on Renal Transplantation Outcomes and Treatments (22 papers), Renal Diseases and Glomerulopathies (14 papers) and Organ Transplantation Techniques and Outcomes (11 papers). Madhav C. Menon collaborates with scholars based in United States, United Kingdom and Australia. Madhav C. Menon's co-authors include John Cijiang He, Peter Y. Chuang, Barbara Murphy, M.S.I. Dhami, Joseph J. Pesek, Mohammad Afzal, Cijiang He, Peter S. Heeger, Yifei Zhong and Yiping Chen and has published in prestigious journals such as Journal of Clinical Investigation, Nature Communications and Scientific Reports.

In The Last Decade

Madhav C. Menon

63 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Madhav C. Menon United States 22 338 278 257 231 215 67 1.4k
Christina L. Klein United States 17 611 1.8× 419 1.5× 420 1.6× 200 0.9× 45 0.2× 31 1.6k
Núria Lloberas Spain 29 439 1.3× 666 2.4× 771 3.0× 539 2.3× 94 0.4× 97 2.3k
Simona Granata Italy 23 447 1.3× 266 1.0× 156 0.6× 634 2.7× 30 0.1× 62 1.6k
Alex B. Magil Canada 23 641 1.9× 375 1.3× 508 2.0× 240 1.0× 37 0.2× 54 1.6k
Kate Wyburn Australia 18 546 1.6× 496 1.8× 432 1.7× 424 1.8× 34 0.2× 79 2.0k
Randy L. Luciano United States 20 464 1.4× 146 0.5× 34 0.1× 307 1.3× 85 0.4× 39 1.3k
Sonoo Mizuiri Japan 19 445 1.3× 212 0.8× 110 0.4× 144 0.6× 28 0.1× 84 1.3k
Su-Kil Park South Korea 22 250 0.7× 208 0.7× 187 0.7× 159 0.7× 17 0.1× 82 1.1k
Stefan Heidenreich Germany 20 396 1.2× 286 1.0× 199 0.8× 256 1.1× 14 0.1× 52 1.6k
Ming‐Che Lee Taiwan 21 96 0.3× 479 1.7× 49 0.2× 281 1.2× 53 0.2× 104 1.4k

Countries citing papers authored by Madhav C. Menon

Since Specialization
Citations

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

Fields of papers citing papers by Madhav C. Menon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Madhav C. Menon. 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 Madhav C. Menon. The network helps show where Madhav C. Menon may publish in the future.

Co-authorship network of co-authors of Madhav C. Menon

This figure shows the co-authorship network connecting the top 25 collaborators of Madhav C. Menon. A scholar is included among the top collaborators of Madhav C. Menon 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 Madhav C. Menon. Madhav C. Menon 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.
Cusick, Matthew F., Viji Nair, Damian Fermin, et al.. (2025). The growth hormone/IGF-1 axis is a risk factor for long-term kidney allograft failure. JCI Insight. 10(11). 1 indexed citations
2.
Menon, Madhav C., et al.. (2024). Re-Evaluating the Transplant Glomerulopathy Lesion—Beyond Donor-Specific Antibodies. Transplant International. 37. 13365–13365. 1 indexed citations
3.
Chen, Man, Madhav C. Menon, Wenlin Wang, et al.. (2023). HCK induces macrophage activation to promote renal inflammation and fibrosis via suppression of autophagy. Nature Communications. 14(1). 4297–4297. 55 indexed citations
4.
Sun, Zeguo, Zhongyang Zhang, Khadija Banu, et al.. (2023). Multiscale genetic architecture of donor-recipient differences reveals intronic LIMS1 mismatches associated with kidney transplant survival. Journal of Clinical Investigation. 133(21). 5 indexed citations
5.
Zhang, Zhongyang, Madhav C. Menon, Weijia Zhang, et al.. (2020). Genome-wide non-HLA donor-recipient genetic differences influence renal allograft survival via early allograft fibrosis. Kidney International. 98(3). 758–768. 22 indexed citations
6.
Yi, Zhengzi, Karen Keung, Li Li, et al.. (2020). Key driver genes as potential therapeutic targets in renal allograft rejection. JCI Insight. 5(15). 6 indexed citations
7.
Kennedy, Paul, Octavia Bane, Stefanie J. Hectors, et al.. (2020). Magnetic resonance elastography vs. point shear wave ultrasound elastography for the assessment of renal allograft dysfunction. European Journal of Radiology. 126. 108949–108949. 23 indexed citations
8.
Nair, Vinay, et al.. (2019). Outcomes of renal transplantation in patients with previous hematologic malignancies. 3(3). 124–130. 3 indexed citations
9.
Zhu, Bingbing, Aili Cao, Jianhua Li, et al.. (2019). Disruption of MAGI2-RapGEF2-Rap1 signaling contributes to podocyte dysfunction in congenital nephrotic syndrome caused by mutations in MAGI2. Kidney International. 96(3). 642–655. 15 indexed citations
10.
11.
Nair, Vinay, Luz Liriano‐Ward, Rebecca Kent, et al.. (2017). Early conversion to belatacept after renal transplantation. Clinical Transplantation. 31(5). e12951–e12951. 24 indexed citations
12.
Yacoub, Rabi, Girish N. Nadkarni, Paolo Cravedi, et al.. (2017). Analysis of OPTN/UNOS registry suggests the number of HLA matches and not mismatches is a stronger independent predictor of kidney transplant survival. Kidney International. 93(2). 482–490. 19 indexed citations
13.
Rein, Joshua L., et al.. (2017). Evaluation of iron status in patients with end stage renal disease. International Journal of Advances in Medicine. 4(5). 1415–1415. 1 indexed citations
14.
Nadkarni, Girish N., Nancy D. Bridges, Jens Goebel, et al.. (2017). Analysis of Biomarkers Within the Initial 2 Years Posttransplant and 5-Year Kidney Transplant Outcomes. Transplantation. 102(4). 673–680. 31 indexed citations
15.
Zhong, Fang, Sandeep K. Mallipattu, Chelsea C. Estrada, et al.. (2016). Reduced Krüppel-Like Factor 2 Aggravates Glomerular Endothelial Cell Injury and Kidney Disease in Mice with Unilateral Nephrectomy. American Journal Of Pathology. 186(8). 2021–2031. 29 indexed citations
16.
Menon, Madhav C. & Michael J. Ross. (2016). Epithelial-to-mesenchymal transition of tubular epithelial cells in renal fibrosis: a new twist on an old tale. Kidney International. 89(2). 263–266. 26 indexed citations
17.
Parker, R. A., et al.. (2014). The yield of colorectal cancer among fast track patients with normocytic and microcytic anaemia. Annals of The Royal College of Surgeons of England. 96(4). 289–293. 6 indexed citations
18.
Benyon, David, et al.. (2014). A Blended Space for Heritage Storytelling. Electronic workshops in computing. 5 indexed citations
19.
Malhotra, Pankaj, et al.. (2008). Haemopericardium in blue rubber bleb naevus syndrome (Bean syndrome). The Medical Journal of Australia. 188(7). 416–416.
20.
Afzal, Mohammad, et al.. (2001). Ginger: An Ethnomedical, Chemical and Pharmacological Review. Drug metabolism and drug interactions. 18(3-4). 159–190. 224 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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