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Effect of Diabetes Self-Management Education on Glycaemic Control in Sudanese Adults with Type 2 Diabetes

Journal of Biosciences and Medicines 2024年第3期12卷 316-327页

作者： Sahar Moawia Balla Elnour Tayseer Abdelmotalib ahmed Taha Haiam Abdalla Wadatalla Ziryab Zainelabdin mohamed Elmahdi Marwah Isam Abdulmajeed Mohammedahmed Rowa Abdelmonem Sidig Hamadto Nahla Yousif Osman Mohammed Saeed Omnia Mubarak Saad Abdallah Sulafa Abdelbagi Mustafa ahmed hanady abdelhameed ahmed mohamed Sarah Khalil Fathi Khalil Family Medicine Health Gates Primary Care Riyadh SAU Family Medicine Dhaman Health Assurance Hospitals Company Kuwait KWT Family Medicine First Health Cluster Riyadh SAU Emergency Department Sheikh Khalifa Medical City Abudahbi ARE Family Medicine University of Medical Sciences and Technology Khartoum SDN Family Medicine Sudan Medical Specialization Board-Sudan Khartoum SDN Family Medicine Ultra Care Home Care Company Riyadh SAU Medicine Department Health Gates Primary Care Riyadh SAU Family Medicine Mouwasat Hospital Madinah SAU

Research Background: The high prevalence of diabetes in Sudan, estimated at 16%, highlights the importance of effective health education in diabetes management. Diabetes self-management education has been identified as a crucial tool in enhancing the knowledge, attitudes, and abilities necessary for self-management among individuals with diabetes. Aim: To assess the impact of diabetes self-management education on medication adherence and glycemic control in Sudanese adults with type 2 diabetes before and 3 months after the DSME intervention. Method: The study was conducted in Sudan between September 2022 and March 2023, it was an interventional, one-group, pre- and post-test study that aimed to assess the impact of diabetes self-management education (DSME) on medication adherence and diabetes control in Sudanese adults with type 2 diabetes. The research was conducted in primary health care centers in six cities in Sudan and involved 244 participants. The data entry and statistical analysis were conducted using the Statistical Package for Social Sciences version 27.0. A paired t test was used for analysis. Results: The study included 244 participants, 67% of whom were males. The age mean ± SD was 48.6 ± 9.3 years, and 85.3% of participants were married. Age at onset of diabetes mean ± SD was 40.60 ± 7.81 years;44.6% had diabetes for less than 5 years;and 84.1% had a positive family history of diabetes mellitus. The levels of poor, low, and partial adherence to medication decreased by 8.2%, 4%, and 20.6%, respectively, after the intervention. The levels of good and high medication regime adherence increased by 13% and 19.8%, respectively;BMI decreased by 1.1 ± 0.73 kg/m2 (p = 0.005). The fasting blood sugar decreased by 69 ± 32.9 mg/dl (p = 0.049), and the glycated hemoglobin decreased by 1.21 ± 0.28% (p = 0.001). Conclusions: The findings of this study reinforce the importance of patient education in improving glycemic control and enhancing self-management

关键词： Sudan Adherence Intervention Education Self-Management Diabetes

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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Computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga ahmed mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams ahmed M.Elshewey El-Sayed M.El-kenawy abdelhameed Ibrahim Abdelaziz A.Abdelhamid mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Incidence of Oxa23 and Oxa51 Genes Associated with Bacterial Isolated from Patients with Urosepsis: Single Centre Prespective

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American Journal of Molecular Biology 2022年第3期12卷 85-96页

作者： Salma Omer Ibrahim Elimam M. A. Mohammed Sami Mahjoub Taha Sanaa Mohammed Yousif Hajir Omer Omer Omer Mirghani Seif-Elnasr Seitelbanat Yassin Yousif abdelhameed Mohammed Omer Abu Elhasan mohamed Taj-Eldin Adam Dawoud Abakar Elhadi Abdalla ahmed Salaheldein G. Elzaki Department of Medical Microbiology Faculty of Medical Lab Sciences University of Gezira Wad Medani Sudan Department of Urology Faculty of Medicine University of Gezira Wad Medani Sudan National Blood Bank Ministry of Health Khartoum Sudan University Hospital Galway Galway Ireland Department of Urology Gezira Hospital for Renal Disease and Surgery Wad Medani Sudan Department of Pharmaceutics Faculty of Pharmacy University of Gezira Wad Medani Sudan Department of Clinical Chemistry National Cancer Institute University of Gezira Wad Medani Sudan Department of Medical Microbiology Wad Medani College for Medical Science and Technology Wad Medani Sudan Department of Medical Parasitology Faculty of Medical Laboratory Sciences University of Gezira Wad Medani Sudan Department of Epidemiology Tropical Medicine Research Institute Khartoum Sudan

Background: Urosepsis is one of the most common infections that require empirical broad spectrum antibiotics immediately after diagnosis. This has led to development of bacterial resistance by acquiring the capability to destroy the β-lactam ring. Methodology: This is a cross-sectional hospital-based study. The study was conducted from 2019 to 2020 at Gezira Hospital for Renal diseases and surgery (GHRDS). A hundred patients were diagnosed clinically with urosepsis and the isolated organisms were Escherichia coli, Staphylococcus aureus, Proteus mirabilis, Klebsiella pneumonia and Pseudomonas aeruginosa. The susceptibility test was conducted by Kirby Bauer disc diffusion technique according to clinical laboratory standard institute (CLSI) guidelines. Seventy eight samples of bacterial genomic DNA were confirmed by 16srRNA and multiplex PCR, were performed for genotypic blaOXA-51 and blaOXA-23 gene characterization of isolated bacteria. Then gel electrophoresis was used to identify the presence or absence of (blaOXA-51 and blaOXA-23) genes. Results: 88.5% (69/78) in 16srRNA detected. Using multiplex PCR, the frequencies of blaOXA-51 and blaOXA-23 genes were 13% and 10.1%, respectively. The percentages of isolates which yielded both blaOXA-51 and blaOXA-23 among P. aeruginosa was 25% (1/4), among K. pneumonia was 17% (1/6), and among E. coli was 8% (3/37). Only blaOXA-51 was detected in P. mirabilis 10% (1/10) and only blaOXA-23 was detected in S. aureus 5% (1/18). Conclusion: In this study, the presence of blaOXA-51 and blaOXA-23 genes was increased in the isolated bacteria.

关键词： Urosepsis Carbapenem-Resistant Enterobacteriaceae (CRE) blaOXA-51 and blaOXA-23

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