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

Date
2021-07
Authors
Xu, Pengjie
Chen, Bo
Takshe, Aseel A.
Omar, Khairi Mohamed
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
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)
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
Keywords
Electronic health, Helicobacter pylori infection, Intelligent health data monitoring, Obesity-related glomerulopathy, Partial differential network mathematical model
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