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.authorXu, Pengjie
dc.contributor.authorChen, Bo
dc.contributor.authorTakshe, Aseel A.
dc.contributor.authorOmar, Khairi Mohamed
dc.date.accessioned2021-06-17T07:02:25Z
dc.date.available2021-06-17T07:02:25Z
dc.date.copyright2021
dc.date.issued2021-07
dc.descriptionThis article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Results in Physics (2021), accessible online through this link https://doi.org/10.1016/j.rinp.2021.104371en_US
dc.description.abstractThis 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.identifier.citationXu, P., Chen, B., Takshe, A., & Omar, K. M. (2021). Helicobacter pylori infection in adult obesity-related nephropathy patients under the partial differential network mathematical model-based artificial intelligence health data monitoring. Results in Physics, 26, 104371. https://doi.org/10.1016/j.rinp.2021.104371en_US
dc.identifier.issn22113797
dc.identifier.urihttps://doi.org/10.1016/j.rinp.2021.104371
dc.identifier.urihttp://hdl.handle.net/20.500.12519/394
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relationAuthors 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.ispartofseriesResults in Physics;Volume 26
dc.rightsCreative Commons CC-BY-NC-ND License
dc.rights.holderCopyright : © 2021 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectronic healthen_US
dc.subjectHelicobacter pylori infectionen_US
dc.subjectIntelligent health data monitoringen_US
dc.subjectObesity-related glomerulopathyen_US
dc.subjectPartial differential network mathematical modelen_US
dc.titleHelicobacter pylori infection in adult obesity-related nephropathy patients under the partial differential network mathematical model-based artificial intelligence health data monitoringen_US
dc.typeArticleen_US

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