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Helicobacter pylori infection in adult obesity-related nephropathy patients under the partial differential network mathematical model-based artificial intelligence health data monitoring

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dc.contributor.author Xu, Pengjie
dc.contributor.author Chen, Bo
dc.contributor.author Takshe, Aseel A.
dc.contributor.author Omar, Khairi Mohamed
dc.date.accessioned 2021-06-17T07:02:25Z
dc.date.available 2021-06-17T07:02:25Z
dc.date.copyright 2021
dc.date.issued 2021-07
dc.identifier.citation Shen, H., Hu, Y., Liu, X., Jiang, Z., Ye, H., Takshe, A., & Al Dulaimi, S. H. K. (2021). Application of Machine Learning Risk Prediction Mathematical Model in the Diagnosis of Escherichia Coli Infection in Patients with Septic Shock by Cardiovascular Color Doppler Ultrasound. Results in Physics, 26, 104368. https://doi.org/10.1016/j.rinp.2021.104368 en_US
dc.identifier.issn 22113797
dc.identifier.uri https://doi.org/10.1016/j.rinp.2021.104371
dc.identifier.uri http://hdl.handle.net/20.500.12519/394
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Results in Physics (2021), available online at: https://doi.org/10.1016/j.rinp.2021.104371 en_US
dc.description.abstract This study aimed to explore the relationship between obesity-related glomerulopathy (ORG) and Helicobacter pylori (HP) infection in adults. Therefore, an artificial intelligence health data monitoring system was established based on the partial differential network mathematical model. The 13C breath test method was applied to detect HP infection in the research objects. A total of 130 patients were included in this study, and rolled into an experimental group (test results were negative) and a control group (test results were positive) according to the results of the 13C breath test. The results showed that there were 83 patients (63.85%) with HP-positive infection and 47 patients (36.15%) with HP-negative infection. The serum creatinine (SCr), 24-hour urine protein level, and the proportions of patients with damaged renal tubules, abnormal retinol-binding protein (RBP), and abnormal N-aceltyl-D-glucosaminidase (NAG) of the patients in the experimental group were markedly higher than those of the control group (P < 0.05); the proportions of patients with global sclerosis, segmental sclerosis, severe fibrosis, and moderate fibrosis in the experimental group were substantially higher than those of the control group (P < 0.05); and the proportions of patients with hypertriglyceridemia, hyperuricemia (HUA), and low high-density lipoprotein cholesterol (LDL-C) in the experimental group were greatly higher than the proportions of the control group (P < 0.05). In short, the partial differential network mathematical model-based artificial intelligence health data monitoring system under E-health can obtain the basic information of the patients in real time, and the HP-positive infection was highly expressed in the ORG patients. The renal function damage of patients with HP-positive infection in ORG was more serious than that of patients with HP-negative infection. Thus, HP infection might be related to the process of ORG. © 2021 The Author(s) en_US
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Xu, P., Department of Nephrology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang 315000, China; Chen, B., Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China; Takshe, A., Department of Environmental Health Sciences, Faculty of Communication, Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates; Omar, K.M., Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseries Results in Physics;Volume 26
dc.rights Creative Commons CC-BY-NC-ND License
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Electronic health en_US
dc.subject Helicobacter pylori infection en_US
dc.subject Intelligent health data monitoring en_US
dc.subject Obesity-related glomerulopathy en_US
dc.subject Partial differential network mathematical model en_US
dc.title Helicobacter pylori infection in adult obesity-related nephropathy patients under the partial differential network mathematical model-based artificial intelligence health data monitoring en_US
dc.type Article en_US
dc.rights.holder Copyright : © 2021 The Author(s)


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