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Articles Published Processes
10/14/2025 7:41:44 AM | Browse: 44 | Download: 71
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Received |
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2025-07-21 05:14 |
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Peer-Review Started |
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2025-07-21 05:14 |
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First Decision by Editorial Office Director |
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2025-08-14 09:33 |
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2025-08-14 09:33 |
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Revised |
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2025-08-22 00:13 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-09-09 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-09-09 03:09 |
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Articles in Press |
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2025-09-09 03:09 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-09-18 10:41 |
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Publish the Manuscript Online |
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2025-10-14 07:41 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Letter to the Editor |
| Article Title |
Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yan-Chun Guo, Ye Hong, Li Huang, Xiao-Wei Xu, Jing-Qi Sun, Kang-Kang Ji and Chao-Nian Li |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Chao-Nian Li, Assistant Professor, Chief Physician, MD, PhD, Department of Traditional Chinese Medicine, Binhai County People's Hospital, No. 299 Haibin Avenue, Yancheng 224500, Jiangsu Province, China. lichaonian2022@126.com |
| Key Words |
Traditional Chinese medicine-machine learning integration; Hepatic steatosis prediction; Machine learning; External validation; Metabolic dysfunction-associated fatty liver disease |
| Core Tip |
Amid metabolic dysfunction-associated fatty liver disease (MAFLD’s) escalating global burden-a leading cause of chronic liver disease with significant economic strain - Tian et al pioneer an integrated traditional Chinese medicine (TCM) - machine learning model (area under the curve: 0.82) using dual-feature selection (LASSO + RFE) to predict hepatic steatosis in high metabolic risk populations. The inclusion of TCM tongue features (edge redness, greasy coating) addresses MAFLD’s heterogeneity and offers cost-saving potential over imaging. However, single-center validation and unmechanized TCM indicators limit clinical translation. Future work must prioritize multiethnic validation, subtype-specific modeling, and TCM-microbiome mechanistic studies to revolutionize early detection in resource-limited settings. |
| Publish Date |
2025-10-14 07:41 |
| Citation |
Guo YC, Hong Y, Huang L, Xu XW, Sun JQ, Ji KK, Li CN. Beyond biomarkers: An integrated traditional Chinese medicine-machine learning approach predicts hepatic steatosis in high metabolic risk populations. World J Gastroenterol 2025; 31(38): 112166 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i38/112166.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i38.112166 |
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