Search

Your search keyword '"Xu, Yuezheng"' showing total 2 results
Start Over "Xu, Yuezheng" Topic machine learning Publication Year Range Last 10 years
2 results on '"Xu, Yuezheng"'

1. Impact of Aging Microbiome on Metabolic Profile of Natural Aging Huangjiu through Machine Learning

Author

Yu, Huakun, Liu, Shuangping, Zhou, Zhilei, Zhao, Hongyuan, Xu, Yuezheng, and Mao, Jian

Subjects
aging microbiome, aging regulation, machine learning, Health (social science), metabolic profiling, fermented alcoholic beverages, Plant Science, multi-omics, Health Professions (miscellaneous), Microbiology, Food Science
Abstract

Aging is a time-consuming step in the manufacturing of fermented alcoholic beverages. Natural-aging huangjiu sealed in pottery jars was taken as an example to investigate the changes of physiochemical indexes during aging and to quantify intercorrelations between aging-related factors and metabolites through machine learning methods. Machine learning models provided significant predictions for 86% of metabolites. Physiochemical indexes well reflected the metabolic profile, and total acid was the most important index that needed to be controlled. For aging-related factors, several aging biomarkers of huangjiu were also well predicted. Feature attribution analysis showed aging year was the most powerful predictive factor, and several microbial species were significantly associated with aging biomarkers. Some of the correlations, mostly connected to environmental microorganisms, were newly found, showing considerable microbial influence on aging. Overall, our results reveal the potential determinants that affect the metabolic profile of aged huangjiu, paving the way for a systematical understanding of changes in metabolites of fermented alcoholic beverages.

Published
2023

2. Identification and validation of SHC1 and FGFR1 as novel immune-related oxidative stress biomarkers of nonobstructive azoospermia.

Author

Yang Pan, Xiangyu Chen, Hang Zhou, Mingming Xu, Yuezheng Li, Qihua Wang, Zhunan Xu, Congzhe Ren, Li Liu, and Xiaoqiang Liu

Subjects
LEYDIG cells, MALE infertility, GENE expression, RECEIVER operating characteristic curves, AZOOSPERMIA
Abstract

Background: Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility. Methods: NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression. Results: Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in tolllike receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data. Conclusion: It appears that SHC1 and FGFR1 could be significant immunerelated oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results