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ObjectiveAmino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH).MethodsThe liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography–tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index.ResultsEleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively.ConclusionThe KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

Abstract Background Non-coding RNAs play important roles in liver regeneration; however, their functions and mechanisms of action in the regeneration of fibrotic liver have not been elucidated. We aimed to clarify the expression patterns and regulatory functions of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration. Methods Based on a mouse model of liver fibrosis with 70% hepatectomy, whole-transcriptome profiling was performed using high-throughput sequencing on samples collected at 0, 12, 24, 48, and 72 h after hepatectomy. Hub genes were selected by weighted gene co-expression network analysis and subjected to enrichment analysis. Integrated analysis was performed to reveal the interactions of differentially expressed (DE) lncRNAs, circRNAs, miRNAs, and mRNAs, and to construct lncRNA–mRNA cis- and trans-regulatory networks and lncRNA/circRNA–miRNA–mRNA ceRNA regulatory networks. Real-Time quantitative PCR was used to validate part of the ceRNA network. Results A total of 1,329 lncRNAs, 48 circRNAs, 167 miRNAs, and 6,458 mRNAs were differentially expressed, including 812 hub genes. Based on these DE RNAs, we examined several mechanisms of ncRNA regulatory networks, including lncRNA cis and trans interactions, circRNA parental genes, and ceRNA pathways. We constructed a cis-regulatory core network consisting of 64 lncRNA–mRNA pairs (53 DE lncRNAs and 58 hub genes), a trans-regulatory core network consisting of 103 lncRNA–mRNA pairs (18 DE lncRNAs and 85 hub genes), a lncRNA–miRNA–mRNA ceRNA core regulatory network (20 DE lncRNAs, 12 DE miRNAs, and 33 mRNAs), and a circRNA–miRNA–mRNA ceRNA core regulatory network (5 DE circRNAs, 5 DE miRNAs, and 39 mRNAs). Conclusions These results reveal the expression patterns of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration, as well as core regulatory networks of mRNAs and non-coding RNAs underlying liver regeneration. The findings provide insights into molecular mechanisms that may be useful in developing new therapeutic approaches to ameliorate diseases that are characterized by liver fibrosis, which would be beneficial for the prevention of liver failure and treatment of liver cancer.

There is an increased expectation about the usefulness of electronic learning (e-learning) to complement or substitute traditional face-to-face learning. However, the growth rate of the e-learning market has not properly reflected the high expectation. Researchers began to direct their attention to the assessment of e-learning effectiveness in order to solve the issue. However, little has been known about why some users stop adopting e-learning after their initial experience. Our work focuses on investigating the continuance usage problems in the field of information technology. A theoretical framework is proposed to address the continuance issue. This integrative framework makes three major contributions. First, it integrates the frameworks of computer self-efficacy (CSE) and the expectation-confirmation model (ECM). Second, it theorizes the causal relationship between the factors of perceived usefulness, confirmation, satisfaction, and information system (IS) continuance in the e-learning context. Finally, it explains users. online learning behaviors through a field survey. The results indicate that, in the context of learning conceptual knowledge in undergraduate education, there are significant relationships among the CSE of online learners, their perceived usefulness, confirmation, and satisfaction levels. [ABSTRACT FROM AUTHOR]