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2,562 results on '"Li, XiaoNa"'

2. Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach

Author

Li, Daowei, Zhang, Qiang, Tan, Yue, Feng, Xinghuo, Yue, Yuanyi, Bai, Yuhan, Li, Jimeng, Li, Jiahang, Xu, Youjun, Chen, Shiyu, Xiao, Si-Yu, Sun, Muyan, Li, Xiaona, and Zhu, Fang

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundMost of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. ObjectiveThis study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. MethodsA total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. ResultsWe present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. ConclusionsTo our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

Published
2020
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3. Soil metabolomics: Deciphering underground metabolic webs in terrestrial ecosystems.

Author

Song, Yang, Yao, Shi, Li, Xiaona, Wang, Tao, Jiang, Xin, Bolan, Nanthi, Warren, Charles, Northen, Trent, and Chang, Scott

Subjects
Carbon cycling, Dissolved organic matter, Extraction method, Metabolomes, Rhizosphere ecology, Soil microbiome
Abstract

Soil metabolomics is an emerging approach for profiling diverse small molecule metabolites, i.e., metabolomes, in the soil. Soil metabolites, including fatty acids, amino acids, lipids, organic acids, sugars, and volatile organic compounds, often contain essential nutrients such as nitrogen, phosphorus, and sulfur and are directly linked to soil biogeochemical cycles driven by soil microorganisms. This paper presents an overview of methods for analyzing soil metabolites and the state-of-the-art of soil metabolomics in relation to soil nutrient cycling. We describe important applications of metabolomics in studying soil carbon cycling and sequestration, and the response of soil organic pools to changing environmental conditions. This includes using metabolomics to provide new insights into the close relationships between soil microbiome and metabolome, as well as responses of soil metabolome to plant and environmental stresses such as soil contamination. We also highlight the advantage of using soil metabolomics to study the biogeochemical cycles of elements and suggest that future research needs to better understand factors driving soil function and health.

Published
2024

15. Microbiome-derived bile acids contribute to elevated antigenic response and bone erosion in rheumatoid arthritis

Author

Su, Xiuli, Li, Xiaona, Bian, Yanqin, Ren, Qing, Li, Leiguang, Wu, Xiaohao, Luan, Hemi, He, Bing, He, Xiaojuan, Feng, Hui, Cheng, Xingye, Kim, Pan-Jun, Tang, Leihan, Lu, Aiping, Xiao, Lianbo, Tian, Liang, Yang, Zhu, and Cai, Zongwei

Subjects
Physics - Biological Physics, Quantitative Biology - Quantitative Methods
Abstract

Rheumatoid arthritis (RA) is a chronic, disabling and incurable autoimmune disease. It has been widely recognized that gut microbial dysbiosis is an important contributor to the pathogenesis of RA, although distinct alterations in microbiota have been associated with this disease. Yet, the metabolites that mediate the impacts of the gut microbiome on RA are less well understood. Here, with microbial profiling and non-targeted metabolomics, we revealed profound yet diverse perturbation of the gut microbiome and metabolome in RA patients in a discovery set. In the Bacteroides-dominated RA patients, differentiation of gut microbiome resulted in distinct bile acid profiles compared to healthy subjects. Predominated Bacteroides species expressing BSH and 7a-HSDH increased, leading to elevated secondary bile acid production in this subgroup of RA patients. Reduced serum fibroblast growth factor-19 and dysregulated bile acids were evidence of impaired farnesoid X receptor-mediated signaling in the patients. This gut microbiota-bile acid axis was correlated to ACPA. The patients from the validation sets demonstrated that ACPA-positive patients have more abundant bacteria expressing BSH and 7a-HSDH but less Clostridium scindens expressing 7a-dehydroxylation enzymes, together with dysregulated microbial bile acid metabolism and more severe bone erosion than ACPA-negative ones. Mediation analyses revealed putative causal relationships between the gut microbiome, bile acids, and ACPA-positive RA, supporting a potential causal effect of Bacteroides species in increasing levels of ACPA and bone erosion mediated via disturbing bile acid metabolism. These results provide insights into the role of gut dysbiosis in RA in a manifestation-specific manner, as well as the functions of bile acids in this gut-joint axis, which may be a potential intervention target for precisely controlling RA conditions., Comment: 38 pages, 6 figures

Published
2023

19. A Dynamic Tuning Method of Wireless Power Transfer System Based on Phase-Controlled Inductor Circuit

Author

He, Shan, Li, Xiaona, Peng, Yuhao, Zhang, Weige, Zeng, Guohong, Du, Xiaogong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yang, Jianwei, editor, Liu, Zhigang, editor, Diao, Lijun, editor, and An, Min, editor

Published
2024
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