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Cited by in CrossRef
For: Qu B, Li Z. Exploring non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease. World J Gastroenterol 2024; 30(28): 3447-3451 [PMID: 39091712 DOI: 10.3748/wjg.v30.i28.3447]
URL: https://www.wjgnet.com/1948-5182/full/v30/i28/3447.htm
Number Citing Articles
1
Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang. Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristicsJournal of Translational Medicine 2025; 23(1) doi: 10.1186/s12967-025-06387-5
2
Anmol Singh, Aalam Sohal, Akash Batta. Recent developments in non-invasive methods for assessing metabolic dysfunction-associated fatty liver diseaseWorld Journal of Gastroenterology 2024; 30(39): 4324-4328 doi: 10.3748/wjg.v30.i39.4324
3
Davide Ramoni, Luca Liberale, Fabrizio Montecucco. Inflammatory biomarkers as cost-effective predictive tools in metabolic dysfunction-associated fatty liver diseaseWorld Journal of Gastroenterology 2024; 30(47): 5086-5091 doi: 10.3748/wjg.v30.i47.5086
4
Toshifumi Yodoshi. Exploring non-invasive diagnostics and non-imaging approaches for pediatric metabolic dysfunction-associated steatotic liver diseaseWorld Journal of Gastroenterology 2024; 30(47): 5070-5075 doi: 10.3748/wjg.v30.i47.5070