|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Circulating micro RNA, mi R-122 and mi R-221 signature in Egyptian patients with chronic hepatitis C related hepatocellular carcinoma显示文摘AIM: To explore the potential usefulness of serum miR-122 and miR-221 as non-invasive diagnostic markers of hepatitis C virus(HCV)-related hepatocellular carcinoma(HCC).METHODS: This prospective study was conducted on 90 adult patients of both sex with HCV-related chronic liver disease and chronic hepatitis C related HCC. In addition to the 10 healthy control individuals, patients were stratified into; interferon-na?ve chronic hepatitis C(CH)(n = 30), post-hepatitis C compensated cirrhosis(LC)(n = 30) and treatment-naive HCC(n = 30). All patients and controls underwent full clinical assessment and laboratory investigations in addition to the evaluation of the level of serum miR NA expression by RT-PCR.RESULTS: There was a significant fold change in serum mi RNA expression in the different patient groups when compared to normal controls; mi R-122 showed significant fold increasing in both CH and HCC and significant fold decrease in LC. On the other hand, mi R-221 showed significant fold elevation in both CH and LC groups and significant fold decrease in HCC group(P = 0.01). Comparing fold changes in miR NAs in HCC group vs non HCC group(CH and Cirrhosis), there was non-significant fold elevation in miR-122(P = 0.21) and significant fold decreasing in miR-221 in HCC vs non-HCC(P = 0.03). ROC curve analysis for miR-221 yielded 87% sensitivity and 40% specificity for the differentiation of HCC patients from non-HCC at a cutoff 1.82. CONCLUSION: Serum miR-221 has a strong potential to serve as one of the novel non-invasive biomarkers of HCC. | Hassan El-Garem Ayman Ammer Hany Shehab Olfat Shaker Mohammed Anwer Wafaa El-Akel Heba Omar | 2014 | World Journal of Hepatology2014，6，11： | 18 |
| 2 | Verification of Lactobacillus brevis tolerance to simulated gastric juice and the potential effects of postbiotic gamma-aminobutyric acid in streptozotocin-induced diabetic mice显示文摘The therapeutic effect of gamma-aminobutyric acid(GABA)on diabetes was spread as one of the alarming epidemics worldwide.The study aims to investigate the function of Lactobacillus brevis KLDS_(1.0727) and KLDS_(1.0373) strains as glutamic acid decarboxylase 65(GAD65)carriers capable of generating GABA by comparing in vitro free and freeze-dried models and GABA intervention in vivo.PCR amplification of gad and in vitro i.e.,(growth rate,viability at different pH,bile tolerance,and survivability in simulated gastric juice)were performed.In vivo experiments were conducted in 7 groups of C57BL/6J mice.Each group was injected with streptozotocin(Cont_(STZ),INSSTZ,LAC1_(STZ),LAC_(1MFDSTZ),LAC_(2STZ),LAC_(2MFDSTZ))daily except for the control(Cont).One group was injected with insulin(INSSTZ).The body weight and hyperglycemia in the blood were assessed weekly,post-euthanasia blood plasma parameters,insulin,and histological examination were evaluated.Results indicated L.brevis strains demonstrated a great tolerance to bile and simulated gastric juice in vitro(P<0.05).Cont_(STZ) had the highest average glucose level(6.84±6.46)mmol/L while INS_(STZ) expressed dramatically decreed in glucose level and displayed a significant decline in the average of weekly blood glucose(−5.74±3.08)mmol/L.The lowest body weight(ContSTZ)was(19.30±0.25)g.Based on the blood plasma analysis,L.brevis strains improved good cholesterol properties,liver and kidney functions,where most of these parameters fall within the average the reference range and prevent the development of symptoms of type 1 diabetes in vivo.As recommended,L.brevis should be commonly distributed as a postbiotic GABA in pharmaceutical and nutritional applications. | Amro Abdelazez Heba Abdelmotaal Smith Etareri Evivie Maha Bikheet Rokayya Sami Hassan Mohamed Xiangchen Meng | 2022 | Food Science and Human Wellness2022，11，1： | 1 |
| 3 | Flavonoid constituents of Ephedra alata显示文摘 | Mahmoud A M N Hassan I E Heba H B | 1984 | Phytochemistry1984，23，12： | 1 |
| 4 | Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment显示文摘Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches. | Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，12： | 1 |
| 5 | Flavonoid constitutents of Ephedra alata显示文摘 | MAHMOUD A M N HASSAN I E HEBA H B | 1984 | Phytochemistry1984，23，： | 1 |
| 6 | Pan-plastome approach empowers the assessment of genetic variation in cultivated Capsicum species显示文摘Pepper species(Capsicum spp.)are widely used as food,spice,decoration,and medicine.Despite the recent old-world culinary impact,more than 50 commercially recognized pod types have been recorded worldwide from three taxonomic complexes(A,B,and P).The current study aimed to apply a pan-plastome approach to resolve the plastomic boundaries among those complexes and identify effective loci for the taxonomical resolution and molecular identification of the studied species/varieties.High-resolution pan-plastomes of five species and two varieties were assembled and compared from 321 accessions.Phyloplastomic and network analyses clarified the taxonomic position of the studied species/varieties and revealed a pronounced number of accessions to be the rare and endemic species,C.galapagoense,that were mistakenly labeled as C.annuum var.glabriusculum among others.Similarly,some NCBIdeposited plastomes were clustered differently from their labels.The rpl23-trnI intergenic spacer contained a 44 bp tandem repeat that,in addition to other InDels,was capable of discriminating the investigated Capsicum species/varieties.The rps16-trnQ/rbcL-accD/ycf3-trnS gene set was determined to be sufficiently polymorphic to retrieve the complete phyloplastomic signal among the studied Capsicum spp.The pan-plastome approach was shown to be useful in resolving the taxonomical complexes,settling the incomplete lineage sorting conflict and developing a molecular marker set for Capsicum spp.identification. | Mahmoud Magdy Lijun Ou Huiyang Yu Rong Chen Yuhong Zhou Heba Hassan Bihong Feng Nathan Taitano Esther van der Knaap Xuexiao Zou Feng Li Bo Ouyang | 2019 | Horticulture Research2019，6，1： | 1 |
| 7 | The flavonoids of phragrnites australis flowers显示文摘 | MAHMOUD A M N HASSAN I E S HEBA H B | 1980 | Phytochemistry1980，19，8： | 1 |
| 8 | Role of endoscopic ultrasound and cyst fluid tumor markers in diagnosis of pancreatic cystic lesions显示文摘BACKGROUND Pancreatic cystic lesions(PCLs) are common in clinical practice. The accurate classification and diagnosis of these lesions are crucial to avoid unnecessary treatment of benign lesions and missed opportunities for early treatment of potentially malignant lesions.AIM To evaluate the role of cyst fluid analysis of different tumor markers such as cancer antigens [e.g., cancer antigen(CA)19-9, CA72-4], carcinoembryonic antigen(CEA), serine protease inhibitor Kazal-type 1(SPINK1), interleukin 1 beta(IL1-β), vascular endothelial growth factor A(VEGF-A), and prostaglandin E2(PGE2)], amylase, and mucin stain in diagnosing pancreatic cysts and differentiating malignant from benign lesions.METHODS This study included 76 patients diagnosed with PCLs using different imaging modalities. All patients underwent endoscopic ultrasound(EUS) and EUS-fine needle aspiration(EUS-FNA) for characterization and sampling of different PCLs.RESULTS The mean age of studied patients was 47.4 ± 11.4 years, with a slight female predominance(59.2%). Mucin stain showed high statistical significance in predicting malignancy with a sensitivity of 87.1% and specificity of 95.56%. It also showed a positive predictive value and negative predictive value of 93.1% and 91.49%, respectively(P < 0.001). We found that positive mucin stain, cyst fluid glucose, SPINK1, amylase, and CEA levels had high statistical significance(P < 0.0001). In contrast, IL-1β, CA 72-4, VEGF-A, VEGFR2, and PGE2 did not show any statistical significance. Univariate regression analysis for prediction of malignancy in PCLs showed a statistically significant positive correlation with mural nodules, lymph nodes, cyst diameter, mucin stain, and cyst fluid CEA. Meanwhile, logistic multivariable regression analysis proved that mural nodules, mucin stain, and SPINK1 were independent predictors of malignancy in cystic pancreatic lesions.CONCLUSION EUS examination of cyst morphology with cytopathological analysis and cyst fluid analysis could improve the differentiation between malignant and benign pancreatic cysts. Also, CEA, glucose, and SPINK1 could be used as promising markers to predict malignant pancreatic cysts. | Hussein Hassan Okasha Abeer Abdellatef Shaimaa Elkholy Mohamad-Sherif Mogawer Ayman Yosry Magdy Elserafy Eman Medhat Hanaa Khalaf Magdy Fouad Tamer Elbaz Ahmed Ramadan Mervat E Behiry Kerolis Y William Ghada Habib Mona Kaddah Haitham Abdel-Hamid Amr Abou-Elmagd Ahmed Galal Wael A Abbas Ahmed Youssef Altonbary Mahmoud El-Ansary Aml E Abdou Hani Haggag Tarek Ali Abdellah Mohamed A Elfeki Heba Ahmed Faheem Hani M Khattab Mervat El-Ansary Safia Beshir Mohamed El-Nady | 2022 | World Journal of Gastrointestinal Endoscopy2022，14，6： | 1 |
| 9 | Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data显示文摘Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male users.Efforts had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for Arabic.Most of the research works are done mainly for English and least amount of effort for non-English language.The study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for Twitter.Therefore,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)model.The presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter data.To attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word embedding.Next,the MSDAE model is exploited for the identification of gender into two classes namely male and female.In the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our work.The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics.The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches. | Badriyya B.Al-onazi Mohamed K.Nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 10 | Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning显示文摘Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures. | Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 11 | Reliable Analytic Strategy to Correlate the Morphological and Cytological Parameters on Lupinus termis L, against Fusarium oxysporum Infection显示文摘 | Ramadan Abd Elghany Mohamed Heba Hassan Elsalahy Osama Abdel-Hafeez Al-Bedak Hoda Abd-EI-Fatah Mostafa Ahmed Nemmat Abd Elgawad Hussein | 2015 | Journal of Agricultural Science and Technology(A)2015，5，7： | 0 |
| 12 | Oscillation Criteria of Second Order Half Linear Delay Dynamic Equations on Time Scales显示文摘In this paper,we establish some new oscillation criteria for a non autonomous second order delay dynamic equation(r(t)g(x~Δ(t)))~Δ+ p(t)f(x(τ(t)))=0,on a time scale T.Oscillation behavior of this equation is not studied before.Our results not only apply on differential equations when T=R,difference equations when T=N but can be applied on different types of time scales such as when T=q^N for q>1 and also improve most previous results.Finally,we give some examples to illustrate our main results. | H.A. AGWO A.M.M. KHODIER HEBA A. HASSAN | 2017 | Acta Mathematicae Applicatae Sinica2017，33，1： | 0 |
| 13 | Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction显示文摘Smart Grid(SG)technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures,control systems,and communication technologies.In SGs,user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption.Since the entire procedure is valued on the basis of time,it is essential to perform adaptive estimation of the SG’s stability.Recent advancements inMachine Learning(ML)andDeep Learning(DL)models enable the designing of effective stability prediction models in SGs.In this background,the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction(WWOODL-SGSP)model.The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner.To attain this,the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level.Then,WWO algorithm is applied to choose an optimal subset of features from the pre-processed data.Next,Deep Belief Network(DBN)model is followed to predict the stability level of SGs.Finally,Slime Mold Algorithm(SMA)is exploited to fine tune the hyperparameters involved in DBN model.In order to validate the enhanced performance of the proposedWWOODL-SGSP model,a wide range of experimental analyses was performed.The simulation results confirmthe enhanced predictive results of WWOODL-SGSP model over other recent approaches. | Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohammad Alamgeer Mohamed K.Nour Anas Abdelrahman Abdelwahed Motwakel | 2022 | Computers, Materials & Continua2022，，12： | 0 |
| 14 | Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks显示文摘Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches. | Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员