检索结果-南通市图书馆

Green-synthetized selenium nanoparticles using berberine as a promising anticancer agent

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Integrative Medicine 2022年第1期20卷 65-72页

作者： Mohamed s.Othman sofian T.Obeidat amal H.Al-Bagawi Mohamed A.Fareid Alaa Fehaid Ahmed E.Abdel Moneim Basic sciences Department Deanship of Preparatory YearUniversity ofHa'ilHail 2240Saudi Arabia Faculty of Biotechnology October University for Modem Science and ArtsGiza 12451Egypt Chemistry Department Faculty of ScienceUniversity of Ha%Hail 2240Saudi Arabia Department of Botany and Microbiology Faculty of ScienceAl-Azhar UniversityCairo 35527Egypt Forensic Medicine and Toxicology Department Faculty of Veterinary MedicineMansoura UniversityDakahlia 35516Egypt Zoology and Entomology Department Faculty of ScienceHelwan UniversityCairo 11795Egypt

Objective: The chemo-preventative and therapeutic properties of selenium nanoparticles(se NPs) have been documented over recent decades and suggest the potential uses of se NPs in medicine. Biogenic se NPs have higher biocompatibility and stability than chemically synthesized nanoparticles, which enhances their medical applications, especially in the field of cancer therapy. This study evaluated the potential of green-synthetized se NPs by using berberine(Ber) as an antitumor agent and elucidated the mechanism by which these molecules combat Ehrlich solid tumors(EsTs).Methods: se NPs containing Ber(se NPs-Ber) were synthesized using Ber and Na2 se O3 and characterized with Fourier transform infrared spectroscopy. sixty male swiss albino mice were then acclimatized for one week, injected with Ehrlich ascites tumor cells, and divided into four groups: EsT, EsT + cisplatin(5 mg/kg), EsT + Ber(20 mg/kg), and EsT + se NPs-Ber(0.5 mg/kg). At the end of a 16-day observation period, 12 mice from each group were euthanized to analyze differences in the body weight, tumor size,gene expression, and oxidative stress markers in the four groups. Three mice from each group were kept alive to compare the survival ***: Treatment with se NPs-Ber significantly improved the survival rate and decreased the body weight and tumor size, compared to the EsT group. se NPs-Ber reduced oxidative stress in tumor tissue,as indicated by a decrease in the lipid peroxidation and nitric oxide levels and an increase in the glutathione levels. Moreover, se NPs-Ber activated an apoptotic cascade in the tumor cells by downregulating the B-cell lymphoma 2(Bcl-2) expression rate and upregulating the Bcl-2-associated X protein and caspase-3 expression rates. se NPs-Ber also considerably improved the histopathological alterations in the developed tumor tissue, compared to the EsT ***: Our study provides a new insight into the potential role of green-synthesized se NPs by using Ber as

关键词： selenium nanoparticles Berberine Ehrlich solid tumor Oxidative stress Apoptosis

A digital assistant for shading paper sketches

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Visual Computing for Industry,Biomedicine,and Art 2020年第1期3卷 152-167页

作者： amal Dev Parakkat Hari Hara Gowtham sarang Joshi Ramanathan Muthuganapathy Indian Institute of Technology Madras Chennai 600036India Indian Institute of Technology Guwahati Assam 781039India

We present a mixed reality-based assistive system for shading paper *** a paper sketch made by an artist,our interface helps inexperienced users to shade it ***,using a simple Delaunaytriangulation based inflation algorithm,an approximate depth map is *** system then highlights areas(to assist shading)based on a rendering of the 2.5-dimensional inflated model of the input *** the help of a mixed reality system,we project the highlighted areas back to aid *** hints given by the system are used for shading and are smudged appropriately to apply an artistic shading to the *** user is given flexibility at various levels to simulate conditions such as height and light *** show that the proposed system aids novice users in creating sketches with impressive shading.

关键词： shading Paper sketch Mixed reality Iso-contours Delaunay triangulation Digital art

Robust Image Watermarking Using LWT and stochastic Gradient Firefly Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第4期75卷 393-407页

作者： sachin sharma Meena Malik Chander Prabha amal Al-Rasheed Mona Alduailij sultan Almakdi Department of CsE Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522302India Deparmtent of CsE Chandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India College of Computer and Information sciences Princess Nourah Bint Abdulrahman University84428Riyadh11671Saudi Arabia Department of Computer science College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the ***, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak signalto Noise Ratio (PsNR), and high structural similarity Matrix (ssIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PsNR and ssIM are obtainedusing the stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PsNR and ssIM as quality metrics.

关键词： Image watermarking lifting wavelet transform discrete wavelet transform(DWT) firefly technique invariant moments

Circulating tumor and cancer stem cells in hepatitis C virusassociated liver disease

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Gastroenterology 2014年第48期20卷 18240-18248页

作者： Abeer A Bahnassy Abdel-Rahman N Zekri Ahmed El-Bastawisy amal Fawzy Marwa shetta Nehal Hussein Dalia Omran Abdallah A s Ahmed samir s El-Labbody Molecular Pathology Unit Pathology Department National Cancer Institute Cairo University Cairo 11796 Egypt Virology and Immunology Unit Cancer Biology Department National Cancer Institute Cairo University Cairo 11796 Egypt Medical Oncology Department National Cancer Institute Cairo University Cairo 11796 Egypt Clinical Pathology Department National Cancer Institute Cairo University Cairo 11796 Egypt Clinical Pathology Kasr Al-Aini Cairo university Cairo 11782 Egypt Tropical Medicine Department Faculty of Medicine Cairo University Cairo 11796 Egypt Microbiology Department El-Azhar University Cairo 11964 Egypt

AIM: To assess the role of circulating tumor cells (CTCs) and cancer stem cells (CsCs) in hepatitis C virus (HCV)-associated liver disease.

关键词： Cancer stem cells Circulating tumor cells Hepatitis C virus genotype-4 Hepatocellular carcinoma

Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 4123-4138页

作者： Areej A.Malibari Marwa Obayya Mohamed K.Nour amal s.Mehanna Manar Ahmed Hamza Abu sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Industrial and systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease *** cancer is the most widely affected cancer worldwide,with an increased death rate *** to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast *** detection of breast cancer will reduce the death rate *** early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive *** paper proposed an efficient and optimized deep learning-based feature selection approach with this *** model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm *** learning is used in the extraction of features *** ext,a convolution neural network,is used to extract the *** two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant *** optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant *** feature extraction and classification process used two datasets,breast,and *** to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAs dataset.

关键词： Breast cancer detection computer-aided diagnosis(CAD) deep learning CNN entropy butterfly optimization

Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： saud s.Alotaibi amal Al-Rasheed sami Althahabi Manar Ahmed Hamza Abdullah Mohamed Abu sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information system College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,symbiotic Organism search(sOs)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

High Thermoelectric Figure of Merit of Ag8sns6 Component Prepared by Electrodeposition Technique

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics Letters 2015年第12期32卷 126-130页

作者： TAHER Ghrib amal Lafy Al-Otaibi MUNIRAH Abdullah Almessiere IBTIssEM Ben Assaker RADHOUANE Chtourou Laboratory of Physical Alloys Science Faculty of Dammam University of Dammam Saudi Arabia Laboratoire Photovoltaique Centre de Recherches et des Technologies de l'Energie Technopole bozj cedria Bp 95 hammam lif 2050 Tunisie

A new thermoelectric material Ag8sns6, with ultra-low thermal conductivity in thin film shape, is prepared on indium tin oxide coated g/ass （ITO） substrates using a chemical process via the electrodeposition technique. The structural, thermal and electrical properties are studied and presented in detail, which demonstrate that the material is of semiconductor type, orthorhombic structure, with a band gap in the order of 1.56eV and a free carrier concentration of 1.46 × 10^17 cm-3. The thermal conductivity, thermal diffusivity, thermal conduction mode, seebeck coefficient and electrical conductivity are determined using the photo-thermal deflection technique combined with the Boltzmann transport theory and Cahill＇s model, showing that the Agssns6 material has a low thermal conductivity of 3.8 Wm - 1K- 1, high electrical conductivity of 2.4 × 10^5 sm- 1, seebeck coefficient of -180μVK-1 and a power factor of 6.9mWK-2m-1, implying that it is more efficient than those obtained in recently experimental investigations for thermoelectric devices.

关键词： Figure High Thermoelectric Figure of Merit of Ag8sns6 Component Prepared by Electrodeposition Technique Ag sns

MIoT Based skin Cancer Detection Using Bregman Recurrent Deep Learning

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 6253-6267页

作者： Nithya Rekha sivakumar sara Abdelwahab Ghorashi Faten Khalid Karim Eatedal Alabdulkreem amal Al-Rasheed Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and *** the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance *** recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin ***,accurate cancer detection was not performed with minimum time *** order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFs-CPRRDLC)technique is introduced for detecting cancer at an earlier *** MBDFs-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and *** patient information is composed of *** patient information was stored in mobile clouds server for performing predictive *** collected data are sent to the recurrent deep learning *** the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature scaling technique to find the significant features for disease identification resulting in decreases time *** by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training *** process is repeatedly performed until the error gets *** this way,disease classification is accurately performed with higher *** evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient *** observed result confirms that the pr

关键词： MIoT skin cancer detection recurrent deep learning classification multidimensional bregman divergencive scaling cophenetic correlative piecewise regression

Highlights in pathogenesis of vitiligo

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Clinical Cases 2015年第3期3卷 221-230页

作者： Ghada F Mohammed amal Hussein Gomaa Mohammed saleh Al-Dhubaibi Department of Dermatology and Venereology Faculty of Medicine Suez Canal University Department of Dermatology Faculty of Medicine Qassim University

Vitiligo is a common pigmentary disorder. Many studies across decades and all over the world have attempted to illustrate the pathogenesis behind it; however, the pathogenesis of vitiligo remains elusive. This review article, we present the findings behind the most and updated theories behind this psychologically debilitating and disfiguring disease. The discussion begun with the role of genetic predisposition followed by neural theory first proposed in the 1950 s. Wehighlight the autoimmune hypothesis, followed by the reactive oxygen species model, zinc-α2-glycoprotein deficiency hypothesis, viral theory, intrinsic theory and biochemical, molecular and cellular alterations accounting for loss of functioning melanocytes in vitiligo. Many theories were elaborated to clarify vitiligo pathogenesis. It is a multifactorial disease involving the interplay of several factors. Future research is needed to clarify the interaction of these factors for better understanding of vitiligo pathogenesis and subsequent successful treatment.

关键词： Etiopathogenesis Pigmentary disorder Non-segmental vitiligo segmental vitiligo Vitiligo