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Leptin influences estrogen metabolism and increases DNA adduct formation in breast cancer cells

Cancer Biology & Medicine 2016年第4期13卷 505-513页

作者： samia shouman Mohamed Wagih Marwa Kamel Department of Cancer Biology Unit of Pharmacology National Cancer Institute Cairo University Department of Pathology Faculty of Medicine Beni-Suef University

Objective: The elevated incidence of obesity has been paralleled with higher risks of breast cancer. High adiposity increases leptin secretion from adipose tissue, which in turn increases cancer cell proliferation. The interplay between leptin and estrogen is one of the mechanisms through which leptin influences breast carcinogenesis. An unbalanced estrogen metabolism increases the formations of catechol estrogen quinones, DNA adducts, and cancer mutations. This study aims to investigate the effect of leptin on some estrogen metabolic enzymes and DNA adduction in breast cancer cells.Methods: High performance liquid chromatography(HPLC) was performed to analyze the DNA adducts 4-OHE1[E2]-1-N3 adenine and 4-OHE1[E2]-1-N7 guanine. Reporter gene assay, real time reverse transcription polymerase chain reaction(real time RT-PCR), and Western blot were used to assess the expression of estrogen metabolizing genes and enzymes: Cytochrome P-4501B1(CYP1B1), Nicotinamide adenine dinucleotide phosphate-quinone oxidoreductase1(NQO1), and Catechol-O-methyl transferase(COMT).Results: Leptin significantly increased the DNA adducts 4-OHE1[E2]-1-N3 adenine and 4-OHE1[E2]-1-N7 guanine.Furthermore, leptin significantly upregulated CYP1B1 promoter activity and protein expression. The luciferase promoter activities of NQO1 and m RNA levels were significantly reduced. Moreover, leptin greatly reduced the reporter activities of the COMT-P1 and COMT-P2 promoters and diminished the protein expression of COMT.Conclusions: Leptin increases DNA adduct levels in breast cancer cells partly by affecting key genes and enzymes involved in estrogen metabolism. Thus, increased focus should be directed toward leptin and its effects on the estrogen metabolic pathway as an effective approach against breast cancer.

关键词： DNA加合物乳腺癌细胞激素代谢烟酰胺腺嘌呤二核苷酸瘦素逆转录聚合酶链反应高效液相色谱法基因启动子

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Self-supported copper-based gas diffusion electrodes improve the local CO_(2)concentration for efficient electrochemical CO_(2)reduction

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Frontiers of Chemical Science and Engineering 2024年第3期18卷 51-62页

作者： Azeem Mustafa Bachirou Guene Lougou Yong Shuai Zhijiang Wang Haseeb-ur-Rehman samia Razzaq Wei Wang Ruming Pan Jiupeng Zhao Key Laboratory of Aerospace Thermophysics of MIIT Harbin Institute of TechnologyHarbin150001China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin150001China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin150001China Mechanical Engineering Department University of Engineering and TechnologyTaxila47050Pakistan School of Aerospace Mechanical and Mechatronics EngineeringUniversity of SydneySydney2006Australia

Electrochemical CO_(2)reduction is a sustainable approach in green chemistry that enables the production of valuable chemicals and fuels while mitigating the environmental impact associated with CO_(2)emissions.Despite its several advantages,this technology suffers from an intrinsically low CO_(2)solubility in aqueous solutions,resulting in a lower local CO_(2)concentration near the electrode,which yields lower current densities and restricts product selectivity.Gas diffusion electrodes(GDEs),particularly those with tubular architectures,can solve these issues by increasing the local CO_(2)concentration and triple-phase interface,providing abundant electroactive sites to achieve superior reaction rates.In this study,robust and self-supported Cu flow-through gas diffusion electrodes(FTGDEs)were synthesized for efficient formate production via electrochemical CO_(2)reduction.They were further compared with traditional Cu electrodes,and it was found that higher local CO_(2)concentration due to improved mass transfer,the abundant surface area available for the generation of the triple-phase interface,and the porous structure of Cu FTGDEs enabled high formate Faradaic efficiency(76%)and current density(265 mA¸cm^(−2))at–0.9 V vs.reversible hydrogen electrode(RHE)in 0.5 mol·L^(−1)KHCO3.The combined phase inversion and calcination process of the Cu FTGDEs helped maintain a stable operation for several hours.The catalytic performance of the Cu FTGDEs was further investigated in a non-gas diffusion configuration to demonstrate the impact of local gas concentration on the activity and performance of electrochemical CO_(2)reduction.This study demonstrates the potential of flow-through gas-diffusion electrodes to enhance reaction kinetics for the highly efficient and selective reduction of CO_(2),offering promising applications in sustainable electrochemical processes.

关键词： CO_(2)electroreduction flow-through delivery hollow fiber structure local concentration formate

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

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Computers, Materials & Continua 2024年第6期79卷 4845-4861页

作者： samia Allaoua Chelloug Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

关键词： Intrusion detection multi classification deep learning stacking NSL-KDD

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Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

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Computers, Materials & Continua 2024年第1期78卷 873-897页

作者： Shereen K.Refaay samia A.Ali Moumen T.El-Melegy Louai A.Maghrabi Hamdy H.El-Sayed Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Engineering Assiut UniversityAssiut711745Egypt College of Engineering University of Business and TechnologyJeddah23411Saudi Arabia

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

关键词： Wireless sensor networks Rao algorithms optimization LEACH PEAGSIS

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Impact of the COVID-19 Pandemic on Cancer Patients: Experience of the medical oncology department of CHU HASSAN II Fez.

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Journal of Cancer Therapy 2024年第3期15卷 83-91页

作者： Khadija Hinaje Oumaima Talbi Lamiae Amaadour Karima Oualla Zineb Benbrahim samia Arifi Nawfel Mellas Department of Medical Oncology Hassan II University Hospital University Sidi Mohamed Ben Abdellah Fez Morocco

Introduction: In December 2019, COVID 19 spread worldwide, but did not officially start in MOROCCO until March 02, 2020. Since then, this pandemic has significantly impacted the health status of patients in general and cancer patients in particular. The main objective of our study is to evaluate the prognosis of patients treated for cancer and infected with COVID-19. Material and method: A descriptive study with prospective collection was carried out at the medical oncology department of CHU Hassan II in FEZ over a period of two years, from March 2020 to March 2022. Data was carried out on the software SPSS. Results: One hundred cancer patients tested positive for COVID-19 infection and were collected within our department. The average age was 56 years [22-91]. The sex ratio was 1.2. Patients with breast cancer were the most affected by this infection (34%). The clinical symptomatology was dominated by the respiratory syndrome (45%).The diagnosis was made through thoracic CT scan in 62% of cases. 76% of patients were in a metastatic stage. 96% of patients were undergoing oncological treatment. For symptomatique patients, the standard treatment approach involved using antibiotics in 76% of cases. Evolution was marked by recovery in 79% of patients, with a death rate of 12% in this cancer patient population. Conclusion: COVID-19 infection is particularly severe in cancer patients. Mortality among these patients remains high and is associated with overall patient characteristics. However, anticancer treatments have not shown deleterious effects on the course of COVID-19.

关键词： COVID-19 Cancer. SARS-CoV-2

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Fasting and Cancer: Practice and Patients’ Quality of Life during Ramadan

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Journal of Cancer Therapy 2024年第3期15卷 49-56页

作者： Oumaima Talbi Zineb Benbrahim Khadija Hinaje Imad Chakri Kaoutar Maadin Lamiae Amaadour Karima Oualla samia Arifi Nawfel Mellas Department of Medical Oncology Faculty of Medicine Pharmacy and Dental Medicine of Fez University Sidi Mohamed BenAbdellah Fez Morocco Epidemiology Laboratory Clinical Research and Community Health Faculty of Medicine Pharmacy and Dental Medicine of Fez University Sidi Mohamed BenAbdellah Fez Morocco

Introduction: Ramadan is the ninth lunar month of the Muslim calendar, and fasting during Ramadan is one of the five pillars of Islam. Muslim cancer patients and health professionals are faced with a dilemma between the necessity of fasting and its possible adverse effects during Ramadan on the health status of cancer patients. We believe that addressing the issue of fasting in cancer patients is of global interest. Objectives: Prevalence of fasting in cancer patients during Ramadan;Predictive factors of Ramadan fasting in cancer patients;The effect of fasting during Ramadan on the quality of life of cancer patients. Materials and Methods: A structured questionnaire was used to interview adult cancer patients who received chemotherapy IV—or oral treatment at the medical oncology department at the Hassan II University Hospital in Fez during the month of Ramadan 1443 Hijri (April to May 2022). The quality of cancer patients’ lives during their fasting practice in Ramadan was evaluated via the QLQ-C30 questionnaires. Results: 48% of patients did not fast during Ramadan, 22% and 30% were partial and complete fasters respectively. Patients, with OMS 0 to 1, who receive outpatient treatment, non-metastatic patients and patients on oral treatment were more frequent in the completely fasted group. All these factors showed statistically significant values (p < 0.05). 44% of patients sought advice from the treating oncologist if they could fast, while only 15% of patients during Ramadan asked an imam if they could be excused from fasting. A comparison of the QLQ-C30 data showed a significant improvement in emotional functioning with fasting, however, patients had clinically more but not significantly fatigue after fasting. Conclusion: Female gender, good WHO, non-metastatic disease, and oral or outpatient treatment were more likely to be fasting. This study argues that fasting did not influence the quality of life, and appears to significantly improve emotional functioning in cancer patients during Ramadan.

关键词： Ramadan Fasting Quality of Life Practice

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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

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Computers, Materials & Continua 2022年第1期70卷 1683-1697页

作者： Mohamed Esmail Karar Marwa Ahmed shouman Claire Chalopin Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Computer Engineering and Networks College of Computing and Information TechnologyShaqra UniversityShaqra11961Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Innovation Center for Computer Assisted Surgery(ICCAS) Universit鋞LeipzigLeipzig04103Germany

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide daily.AlthoughChest X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently.In this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary classifier.Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested images.Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe.This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients.The results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent studies.Consequently,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

关键词： COVID-19 medical imaging machine learning applications adversarial neural networks ultrasound

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烧结过程温度场模拟及各参数影响分析

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钢铁研究学报 2024年第2期36卷 177-187页

作者：杨双平郭世杰池延斌刘守满孙海兴张甜甜西安建筑科技大学冶金工程学院陕西西安710055

以某钢厂150 m^(2)烧结机为研究对象,利用CFD软件FLUENT为计算平台,将现场实际参数作为信息输入,模拟计算了该企业烧结过程,通过对比风箱烟气温度实测值与模拟值,分析误差及其来源,验证了烧结模拟的正确性。采用单因素分析法探究了点火... 详细信息

以某钢厂150 m^(2)烧结机为研究对象,利用CFD软件FLUENT为计算平台,将现场实际参数作为信息输入,模拟计算了该企业烧结过程,通过对比风箱烟气温度实测值与模拟值,分析误差及其来源,验证了烧结模拟的正确性。采用单因素分析法探究了点火温度、配碳量及孔隙率对料层温度分布的影响规律。结果表明:点火温度只能影响料层上部极值温度,对烧结时间以及成矿质量影响不大;配碳量从4.2%增加至4.6%,料层底部极值温度升高且烧结时间缩短,有利于提高烧结矿质量以及成品率;孔隙率从0.48增大至0.52,气-固相间的热交换反应加剧,料层自蓄热作用加强,垂直烧结速度加快,烧结效率提高。

关键词：烧结过程 FLUENT 模拟计算料层温度场

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