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exendin-4 inhibits the survival and invasiveness of two colorectal cancer cell lines via suppressing GS3Kβ/β-catenin/NF-κB axis through activating SIRT1

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BIOCeLL 2021年第5期45卷 1337-1353页

作者： ATTALLA F.eL-KOTT AYMAN e.eL-KeNAWY eMAN R.eLBeALY ALI S.ALSHeHRI HeBA S.KHALIFA MASHAeL MOHAMMeD BIN-MeFeRIJ ehab e.massoud AMIRA M.ALRAMLAWY Biology Department College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Zoology Department College of ScienceDamanhour UniversityDamanhour22511Egypt Pathology Department College of MedicineTaif UniversityTaif11099Saudi Arabia Biology Department College of Science for GirlsKing Khalid UniversityAbha61413Saudi Arabia Biology Department College of SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Biology Department Faculty of Science and ArtsDahran AljnoubKing Khalid UniversityAbha61413Saudi Arabia Research Center for Advanced Materials Science King Khalid UniversityAbha61413Saudi Arabia Agriculture Research Centre SoilWater and Environment Research InstituteGiza12619Egypt Mansoura Research Centre for Cord Stem Cell(MARC-CSC) Stem Cells BankChildren’s HospitalMansoura UniversityMansoura35516Egypt

This study examined if the anti-tumorigenesis effect of exendin-4 in HT29 and HCT116 colorectal cancer(CRC)involves modulation of SIRT1 and Akt/GSR3K/β-catenin/NF-κB ***29 and HCT116 cells were treated either with increasing levels of exendin-4(0.0-200μM)or with exendin-4(at its IC50)in the presence or absence of eX-527(10μM/a selective SIRT1 inhibitor)or exendin-4(9-39)amide(e(9-39)A)(1μM/an exendin-4 antagonist).In a dose-dependent manner,exendin-4 inhibited cell survival,but enhanced levels of lactate dehydrogenase(LDH)and single-stranded DNA(ssDNA)in both HT29 and *** both cell lines and at it has an IC50(45μM for HT29 and 35μM for HCT1165),exendin-4 also significantly reduced cell survival,migration,and invasion of both cell types,with no effect on the expression GLP-1 receptors(GLPRs)nor of the activity of *** these doses,exendin-4 also increased the expression of SIRT1 but reduced the acetylation of NF-κB and the expression of Bax and cleaved caspase-3 and in both cell ***,protein levels of p-GS3Kβ(Ser9),total and acetylatedβ-catenin,and Anix2 were significantly decreased,but levels of p-GS3Kβ(Ser9)and p-β-catenin(Ser33/37/Thr41)were significantly increased in both HT29 and HCT116-exendin-4 treated *** the effects exerted by exendin-4 were completely prevented by ex527 or e(9-39)*** conclusion,exendin-4 suppresses the tumorigenesis of HT29 and HCT116 CRC cell activation of GS3Kβ-induced inhibition ofβ-catenin and NF-κβin a SIRT1-dependent mechanism.

关键词： Colorectal cancer exendin-4 SIRT1 GS3Kβ β-catenin NF-κB

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid eljaaly Maha Farouk SSabir ehab Bahaudien Ashary S.M.Abo-Dahab e.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia electrical and Computer engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based efficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAe)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Hybrid Single Image Super-Resolution Algorithm for Medical Images

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Computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid el-Shafai ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of electronics and electrical Communications engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia electrical engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia electrical engineering Department Aswan UniversityAswan81542Egypt electronics and Micro-electronics Laboratory(e.μ.e.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of engineering&Technology(AIeT) AlexandriaEgypt electronics and Communications engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to ***,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)***,it is important to maintain the shape and size of the medical images while enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce *** proposed framework comprises five GAN models:enhanced SR Generative Adversarial Networks(eSRGAN),enhanced deep SR GAN(eDSRGAN),Sub-Pixel-GAN,SRGAN,and efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×***,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared error(MSe),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage *** obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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Computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt electrical and Computer engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan el-Sayed M.el-kenawy Ali e.Takieldeen Tarek M.Hassan ehab A.Hegazy elsayed Abdel Fattah eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Nanofluid Flows Within Porous enclosures Using Non-Linear Boussinesq Approximation

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Computers, Materials & Continua 2021年第3期66卷 3195-3213页

作者： Sameh e.Ahmed Dalal Alrowaili ehab Mahmoud Mohamed Abdelraheem M.Aly Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Mathematics South Valley UniversityQena83523Egypt Mathematics Department College of ScienceJouf UniversitySakakaSaudi Arabia electrical engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia electrical engineering Department Aswan UniversityAswan81542Egypt

In this paper,the Galerkin finite element method(FeM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and *** enclosure has an inclination angle and its side-walls have varying sinusoidal temperature *** working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as *** porous medium is modeled using the Brinkman Forchheimer extended Darcy *** obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0≤ζ≤1,the phase deviation 00≤Φ≤1800,the inclination angle 00≤γ≤900,the nanoparticles volume fraction 0%≤φ≤4%,the amplitude ratio 0≤a≤1 and the Rayleigh number 104≤Ra≤*** results revealed that the average Nusselt number is enhanced by 0.73%,26.46%and 35.42%at Ra=104,105 and 106,respectively,when the non-linearBoussinesq parameter is varied from 0 to *** addition,rate of heat transfer in the case of a non-uniformly heating is higher than that of a uniformly ***-linear Boussinesq parameter rises the flow speed and heat transfer in an *** deviation makes clear changes on the isotherms and heat transfer rate on the right wall of an *** inclination angle varies the flow speed and it has a slight effect on heat transfer in an enclosure.

关键词： CBS scheme non-linear Boussinesq approximation nonuniformly heating non-darcy flow nanofluid

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Adsorptive removal of iron and manganese ions from aqueous solutions with microporous chitosan/polyethylene glycol blend membrane

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Journal of environmental Sciences 2012年第8期24卷 1425-1432页

作者： Neama A.Reiad Omar e.Abdel Salam ehab F.Abadir Farid A.Harraz Sanitary&environmental Department Housing&Building National Research Center87 el Tahrir st.DokkiEgypt Department of Chemical engineering Faculty of EngineeringCairo UniversityGizaEgypt Advanced Materials Technology Department Central Metallurgical R&D InstituteHelwanEgypt

Microporous chitosan （CS） membranes were directly prepared by extraction of poly（ethylene glycol）（PeG） from CS/PeG blend membrane and were examined for iron and manganese ions removal from aqueous solutions. The different variables affecting the adsorption capacity of the membranes such as contact time, pH of the sorption medium, and initial metal ion concentration in the feed solution were investigated on a batch adsorption basis. The affinity of CS/PeG blend membrane to adsorb Fe（II） ions is higher than that of Mn（II） ions, with adsorption equilibrium achieved after 60 min for Fe（II） and Mn（II） ions. By increasing CS]PeG ratio in the blend membrane the adsorption capacity of metal ions increased. Among all parameters, pH has the most significant effect on the adsorption capacity, particularly in the range of 2.9-5.9. The increase in CS/PeG ratio was found to enhance the adsorption capacity of the membranes. The effects of initial concentration of metal ions on the extent of metal ions removal were investigated in detail. The experimental data were better fitted to Freundlich equation than Langmuir. In addition, it was found that the iron and manganese ions adsorbed on the membranes can be effectively desorbed in 0.1 mol/L HCl solution （up to 98% desorption efficiency） and the blend membranes can be reused almost without loss of the adsorption capacity for iron and manganese ions.

关键词： chitosan blend membrane iron and manganese ions adsorption

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GReeN SYNTHeSIS OF ANTIBACTeRIAL CHITOSAN FILMS LOADeD WITH SILVeR NANOPARTICLeS

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Chinese Journal of Polymer Science 2013年第7期31卷 984-993页

作者： Neama A. Reiad Omar e. Abdel Salam ehab F. Abadir Farid A. Harraz Sanitary & environmental engineering Department Housing & Building National Research Center Department of Chemical engineering Faculty of EngineeringCairo University Advanced Materials Technology Department Central Metallurgical R & D Institute

An eco-friendly chemical reduction method was successfully used for the preparation of chitosan （CTS） composite films loaded with silver nanoparticles （AgNPs） by self assembly method using poly（ethylene glycol） as both reducing and stabilizing agent. UV-Vis spectra of the prepared chitosan loaded silver nanoparticles （CTSLAg） films reveal that full reduction of silver ions to silver nanoparticles takes place at 90 ℃. The effect of reaction conditions on the silver nanoparticles formation was investigated using UV-Vis spectrophotometer. The morphology of the films was tested by scanning electron microscopy （SeM）. The DSC curves showed that the CTSLAg film had a favorable compatibility and heat stability. AgNPs were confirmed by XRD and UV-Vis spectroscopy. The TeM findings revealed that the silver nanoparticles synthesized were spherical in shape with uniform dispersal, and by increasing CTS：PeG ratio larger silver nanoparticles could be obtained. The results of antibacterial study reveal that the prepared nanoeomposite films exhibited potential inhibition.

关键词： Chitosan Silver nanoparticles Antibacterial activity.

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Millimeter-Wave Concurrent Beamforming:A Multi-Player Multi-Armed Bandit Approach

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Computers, Materials & Continua 2020年第12期65卷 1987-2007页

作者： ehab Mahmoud Mohamed Sherief Hashima Kohei Hatano Hani Kasban Mohamed Rihan electrical engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia electrical engineering Department Faculty of EngineeringAswan UniversityAswan81542Egypt Computational Learning Theory Team RIKEN-Advanced Intelligent ProjectFukuoka819-0395Japan engineering Department Nuclear Research CenterEgyptian Atomic Energy AuthorityCairo13759Egypt Faculty of Arts and Science Kyushu UniversityFukuok819-0395Japan electronics and electrical Communication engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to fully cover a target environment with gigabits per second(Gbps)***,inter-beam interference prevents maximizing the sum rates of the established concurrent *** this paper,a reinforcement learning(RL)approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent ***,the problem is formulated as a multiplayer multiarmed bandit(MAB),where mmWave APs act as the players aiming to maximize their achievable rewards,i.e.,data rates,and the arms to play are the available beam *** this setup,a selfish concurrent multiplayer MAB strategy is *** different MAB algorithms,namely,ϵ-greedy,upper confidence bound(UCB),Thompson sampling(TS),and exponential weight algorithm for exploration and exploitation(eXP3)are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous *** a few rounds of interactions,mmWave APs learn how to select concurrent beams that enhance the overall system *** proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.

关键词： Millimeter wave(mmWave) concurrent transmissions reinforcement learning multiarmed bandit(MAB)

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Simulating Annealing PLL for Autonomous Microgrid Systems

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Smart Grid and Renewable energy 2019年第5期10卷 141-154页

作者： ehab H. e. Bayoumi electrical and electronics engineering Department University of Swaziland Kwaulsini Swaziland

The Phase Locked Loop controller parameters are the key-point that affects the dynamic performance of the autonomous microgrid. They have to be optimally tuned to guarantee enhanced overall system stability. In this paper, two-microgrid plant with their associate PWM inverter connected to the ac main grid and the load is used as an example to demonstrate the capabilities of the proposed system. The Phase Locked Loop controller is designed and tuned using the Simulating Annealing algorithm. This algorithm is used to select the Phase Locked Loop PI controller gains with optimal percentage overshoot, rise time and settling time. The controller is tested during the transition between grid-connected and autonomous operation and in reverse order. The controller is compared with Ziegler and Nichols P and PI controllers. It shows the effectiveness and the extraordinary control response of the proposed control technique with respect to percentage overshoot, rise time and settling time control parameters compared to the conventional one.

关键词： Simulating Annealing PI Controllers Microgrid Operations

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