Ryan S. Huss

1.5k total citations · 1 hit paper
16 papers, 523 citations indexed

About

Ryan S. Huss is a scholar working on Epidemiology, Hepatology and Surgery. According to data from OpenAlex, Ryan S. Huss has authored 16 papers receiving a total of 523 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Epidemiology, 6 papers in Hepatology and 5 papers in Surgery. Recurrent topics in Ryan S. Huss's work include Liver Disease Diagnosis and Treatment (10 papers), Liver Disease and Transplantation (4 papers) and Liver Diseases and Immunity (4 papers). Ryan S. Huss is often cited by papers focused on Liver Disease Diagnosis and Treatment (10 papers), Liver Disease and Transplantation (4 papers) and Liver Diseases and Immunity (4 papers). Ryan S. Huss collaborates with scholars based in United States, Switzerland and New Zealand. Ryan S. Huss's co-authors include Robert P. Myers, Andrew N. Billin, Stuart B. Goodman, Rohit Loomba, James I. Huddleston, Anita Kohli, Eugene C. Butcher, Brian A. Zabel, Mazen Noureddin and Naim Alkhouri and has published in prestigious journals such as Circulation, Nature Medicine and Hepatology.

In The Last Decade

Ryan S. Huss

15 papers receiving 509 citations

Hit Papers

Safety and efficacy of combination therapy with semagluti... 2022 2026 2023 2024 2022 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ryan S. Huss United States 9 368 160 156 126 85 16 523
H Wobser Germany 11 345 0.9× 170 1.1× 119 0.8× 131 1.0× 142 1.7× 22 550
Jacqueline Córdova‐Gallardo Mexico 9 256 0.7× 87 0.5× 77 0.5× 66 0.5× 48 0.6× 30 376
Takuya Kuwashiro Japan 11 289 0.8× 214 1.3× 85 0.5× 92 0.7× 58 0.7× 22 485
Sebastian K. Eder Austria 9 333 0.9× 120 0.8× 162 1.0× 83 0.7× 56 0.7× 21 486
Richard J. Milton United States 7 301 0.8× 206 1.3× 51 0.3× 155 1.2× 115 1.4× 7 527
Ida Falk Villesen Denmark 10 264 0.7× 186 1.2× 72 0.5× 58 0.5× 66 0.8× 24 427
Akira Sakamaki Japan 12 167 0.5× 139 0.9× 37 0.2× 96 0.8× 107 1.3× 63 477
Dávid Tornai Hungary 14 364 1.0× 233 1.5× 65 0.4× 115 0.9× 74 0.9× 32 607
Roman Liebe Germany 11 260 0.7× 192 1.2× 72 0.5× 135 1.1× 105 1.2× 36 468
Shinji Iwane Japan 11 241 0.7× 237 1.5× 33 0.2× 65 0.5× 76 0.9× 34 425

Countries citing papers authored by Ryan S. Huss

Since Specialization
Citations

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

Fields of papers citing papers by Ryan S. Huss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan S. Huss. 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 Ryan S. Huss. The network helps show where Ryan S. Huss may publish in the future.

Co-authorship network of co-authors of Ryan S. Huss

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

All Works

16 of 16 papers shown
1.
Benegiamo, Giorgia, Archana Vijayakumar, Masaki Kimura, et al.. (2026). An oral, liver-restricted LXR inverse agonist for dyslipidemia: preclinical development and phase 1 trial. Nature Medicine. 32(3). 883–893.
2.
3.
Conway, Jake R., Yevgeniy Gindin, David Z. Pan, et al.. (2023). Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH. Cell Reports Medicine. 4(4). 101016–101016. 12 indexed citations
4.
Mann, Sabrina A, Kelvin W. Li, Jen‐Chieh Chuang, et al.. (2023). Acetyl-CoA carboxylase inhibitor increases LDL-apoB production rate in NASH with cirrhosis: prevention by fenofibrate. Journal of Lipid Research. 64(3). 100339–100339. 9 indexed citations
5.
Vijayakumar, Archana, Eisuke Murakami, Ryan S. Huss, et al.. (2023). 849-P: Antidiabetic Effects of TLC-3595, a Selective ACC2 Inhibitor, in ZDF Rats. Diabetes. 72(Supplement_1). 1 indexed citations
6.
Alkhouri, Naim, Robert Herring, Heidi Kabler, et al.. (2022). Safety and efficacy of combination therapy with semaglutide, cilofexor and firsocostat in patients with non-alcoholic steatohepatitis: A randomised, open-label phase II trial. Journal of Hepatology. 77(3). 607–618. 157 indexed citations breakdown →
7.
Lawitz, Eric, Kelvin W. Li, Edna Nyangau, et al.. (2022). Elevated de novo lipogenesis, slow liver triglyceride turnover, and clinical correlations in nonalcoholic steatohepatitis patients. Journal of Lipid Research. 63(9). 100250–100250. 19 indexed citations
8.
Lawitz, Eric, Bal Raj Bhandari, Peter Ruane, et al.. (2022). Fenofibrate Mitigates Hypertriglyceridemia in Nonalcoholic Steatohepatitis Patients Treated With Cilofexor/Firsocostat. Clinical Gastroenterology and Hepatology. 21(1). 143–152.e3. 34 indexed citations
9.
Smirnova, Ekaterina, Mark Muthiah, Nicole Narayan, et al.. (2022). Metabolic reprogramming of the intestinal microbiome with functional bile acid changes underlie the development of NAFLD. Hepatology. 76(6). 1811–1824. 89 indexed citations
10.
Younossi, Zobair M., Maria Stepanova, Mazen Noureddin, et al.. (2021). Improvements of Fibrosis and Disease Activity Are Associated With Improvement of Patient‐Reported Outcomes in Patients With Advanced Fibrosis Due to Nonalcoholic Steatohepatitis. Hepatology Communications. 5(7). 1201–1211. 24 indexed citations
11.
Sanyal, Arun J., Quentin M. Anstee, Michael Trauner, et al.. (2021). Cirrhosis regression is associated with improved clinical outcomes in patients with nonalcoholic steatohepatitis. Hepatology. 75(5). 1235–1246. 89 indexed citations
12.
Younossi, Zobair M., Stephen A. Harrison, Philip N. Newsome, et al.. (2021). Development and validation of Agile 3+: novel FibroScan based score for the diagnosis of advanced fibrosis in patients with nonalcoholic fatty liver disease. 6 indexed citations
13.
Glass, Benjamin, Hunter Elliott, Ling Han, et al.. (2020). Machine learning models identify novel histologic features predictive of clinical disease progression in patients with advanced fibrosis due to non-alcoholic steatohepatitis. Journal of Hepatology. 73. S402–S402. 1 indexed citations
14.
Huss, Ryan S., James I. Huddleston, Stuart B. Goodman, Eugene C. Butcher, & Brian A. Zabel. (2010). Synovial tissue–infiltrating natural killer cells in osteoarthritis and periprosthetic inflammation. Arthritis & Rheumatism. 62(12). 3799–3805. 72 indexed citations
15.
Fischer, Marcellus, C Spes, Ryan S. Huss, & Roland Gärtner. (2008). Immunogene Hyperthyreose mit hyperdynamischem Herzversagen und beginnendem zirrhotischem Umbau der Leber. DMW - Deutsche Medizinische Wochenschrift. 122(11). 323–327. 2 indexed citations
16.
Huss, Ryan S., et al.. (1990). Individualizing antihypertensive therapy with enalapril versus atenolol: the Zurich experience.. PubMed. 8(4). S49–52. 6 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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