Publication Name	World Journal of Hepatology
Manuscript ID	105332
Country	China

Received	2025-01-20 11:09
Peer-Review Started	2025-01-20 11:09
To Make the First Decision
Return for Revision	2025-02-28 14:26
Revised	2025-03-30 08:43
Second Decision	2025-04-27 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-27 08:15
Articles in Press	2025-04-27 08:15
Publication Fee Transferred	2025-04-03 02:32
Edit the Manuscript by Language Editor	2025-05-03 20:17
Typeset the Manuscript	2025-06-16 07:40
Publish the Manuscript Online	2025-06-26 07:27

ISSN	1948-5182 (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/
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Category	Endocrinology & Metabolism
Manuscript Type	Basic Study
Article Title	Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis
Manuscript Source	Unsolicited Manuscript
All Author List	Qian-Yu Liang, Jun Wang, Yun-Feng Yang, Kai Zhao, Rui-Li Luo, Ye Tian and Feng-Xia Li
ORCID	Author(s) ORCID Number Feng-Xia Li http://orcid.org/0009-0002-6889-8817
Funding Agency and Grant Number	Funding Agency Grant Number The Medical Key Science and Technology Project of Shanxi Province No. 2020xm23
Corresponding Author	Feng-Xia Li, Department of Gastroenterology, Shanxi Provincial People's Hospital, No. 29 Shuangta Temple Street, Yingze District, Taiyuan 030000, Shanxi Province, China. doclfx@126.com
Key Words	Cirrhosis; Sarcopenia; Untargeted metabolomics; Machine learning; Biomarkers
Core Tip	This study unveiled different plasma metabolic profiles of liver cirrhosis patients with and without sarcopenia, which may deliver valuable biomarkers for the early identification and prognosis prediction of the disease.
Publish Date	2025-06-26 07:27
Citation	<p>Liang QY, Wang J, Yang YF, Zhao K, Luo RL, Tian Y, Li FX. Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis. <i>World J Hepatol</i> 2025; 17(6): 105332</p>
URL	https://www.wjgnet.com/1948-5182/full/v17/i6/105332.htm
DOI	https://dx.doi.org/10.4254/wjh.v17.i6.105332

Full Article (PDF)	WJH-17-105332-with-cover.pdf
Manuscript File	105332_Auto_Edited_074816.docx
Answering Reviewers	105332-answering-reviewers.pdf
Audio Core Tip	105332-audio.wav
Biostatistics Review Certificate	105332-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	105332-conflict-of-interest-statement.pdf
Copyright License Agreement	105332-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	105332-foundation-statement.pdf
Institutional Review Board Approval Form or Document	105332-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	105332-non-native-speakers.pdf
Supplementary Material	105332-supplementary-material.pdf
Peer-review Report	105332-peer-reviews.pdf
Scientific Misconduct Check	105332-scientific-misconduct-check.png
Scientific Editor Work List	105332-scientific-editor-work-list.pdf
CrossCheck Report	105332-crosscheck-report.pdf