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Urinary neutrophil gelatinase-associated lipocalin(NGAL)and serum cystatin C measurements for early diagnosis of acute kidney injury in children admitted to PICU

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World Journal of Pediatrics 2018年第2期14卷 134-142页

作者： Jameela Abdulaziz Kari Mohamed ahmed Shalaby Kholoud Sofyani Ahmad Saleh Sanad Albaraa Fuad Ossra Rayan Smeer Halabi Maha Hassan Aljuhani Wael Mohammad Toffaha Feras Aymen Moria Samar Sabry hanan abdelaziz ahmed Khalid Abdulaziz Alhasan Sara Sharief Osama Safdar Pediatric Nephrology Center of Excellence and Department of Pediatrics King Abdulaziz University80215Jeddah 21589Kingdom of Saudi Arabia Pediatric Intensive Care Unit Department of PediatricsKing Abdulaziz UniversityJeddahKingdom of Saudi Arabia Pediatric Department Cairo UniversityGizaEgypt Department of Pediatrics College of MedicineKing Khalid University HospitalKing Saud UniversityRiyadhKingdom of Saudi Arabia

Background Acute kidney injury(AKI)is common in critically ill children with significant mortality and *** creatinine is an insensitive and late biomarker compared to newly proposed AKI *** Prospective study in pediatric intensive care unit(PICU)over three months to compare between serum cystatin-C(s-Cys-C)and urinary neutrophil gelatinase-associated lipocalin(uNGAL)as AKI biomarkers at multiple time points with pediatric risk,injury,failure,loss,end-stage renal disease(pRIFLE)classification in diagnosing *** Forty children were *** these 40 children,22 developed AKI according to pRIFLE *** was no significant difference between AKI and non-AKI in age(P=0.29).Post cardiac surgery,renal insult was the main cause of AKI(27.3%).There was a twofold increased risk of incident AKI in those patients with high baseline uNGAL at PICU admission and almost a fourfold increased risk in patients with high baseline s-Cys-C at PICU *** levels were highly predictive of AKI during the follow-up period[area under the curve(AUC)=0.76,95%confidence interval(CI)0.61-0.92].The cutoff point with the highest correctly classified proportion was 223 ng/mL(≥12 centiles)which correctly predict 80.0%patients with AKI,with a corresponding sensitivity of 72.7%and a specificity of 89.9%.AUC for s-Cys-C was 0.86(95%CI 0.75-0.97),and the highest correctly classified proportion was 1009μg/L(≥13 centiles);75%of patients with AKI,with a corresponding sensitivity of 63.6%and a specificity of 88.9%.Conclusion uNGAL and s-Cys-C predicts AKI early in critically ill children.

关键词： Acute kidney injury Neutrophil gelatinase-associated lipocalin Serum cystatin C

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Current approaches in treatment of triple-negative breast cancer

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Cancer Biology & Medicine 2015年第2期12卷 106-116页

作者： hanan ahmed Wahba Hend ahmed El-Hadaad Clinical Oncology and Nuclear Medicine Mansoura University

Triple-negative breast cancer(TNBC) is diagnosed more frequently in younger and premenopausal women and is highly prevalent in African American women. TNBC is a term derived from tumors that are characterized by the absence of ER, Pg R, and HER2. So patients with TNBC do not benefit from hormonal or trastuzumab-based therapies. TNBCs are biologically aggressive, although some reports suggest that they respond to chemotherapy better than other types of breast cancer, prognosis remains poor. This is due to: shortened disease-free interval in the adjuvant and neoadjuvant setting and a more aggressive course in the metastatic setting.

关键词： Breast cancer triple-negative HER2

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A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

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Computers, Materials & Continua 2022年第12期73卷 5655-5669页

作者： Abeer Abdulaziz AlArfaj hanan ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Lie detection test is highly significant task due to its impact on criminology and *** lie detection test model using electroencephalogram(EEG)signals is studied in *** this paper we studied deep learning framework in lie detection test ***,we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole *** model describes a temporal feature map of the EEG signals measured during the lie detection test.A deep learning attention model(V-TAM)extracts the temporal map vector during the learning *** technique reduces computational time and lessens the overfitting in Deep Learning *** propose a Cascading attention model with a deep learning convolutional neural network(CNN).V-TAM model extracts local features and global features in separate paths spatial and ***,to enhance the EEG segmentation precision,a novel Visual-Temporal AttentionModel(V-TAM)is *** accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection *** compared our model with three recent published *** proposed model attained the highest performance of(98.5%)with(p<0.01).The visual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time *** investigationwere performed to prove the correctness and reliability of the proposed method through sizing of the input data,proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.

关键词： Lie detection EEG image deep learning machine learning

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An Intelligent Tree Extractive Text Summarization Deep Learning

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Computers, Materials & Continua 2022年第11期73卷 4231-4244页

In recent research,deep learning algorithms have presented effective representation learning models for natural *** deep learningbased models create better data representation than classical *** are capable of automated extraction of distributed representation of *** this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses *** proposed model employs a tree structured mechanism to generate the phrase and text *** proposed architecture mimics the tree configuration of the text-texts and provide better feature *** also incorporates an attention mechanism that offers an additional information source to conduct better summary *** novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary *** model classification is divided into multiple features recognition such as information entropy,significance,redundancy and *** model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)*** experimental results prove that our proposed model has better summarization precision *** models by a considerable margin.

关键词： Neural network architecture text structure abstractive summarization

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A Dual Attention Encoder-Decoder Text Summarization Model

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Computers, Materials & Continua 2023年第2期74卷 3697-3710页

作者： Nada Ali Hakami hanan ahmed Hosni Mahmoud Jazan University Computer Science DepartmentCollege of Computer Science and Information TechnologyJazanSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

A worthy text summarization should represent the fundamental content of the *** studies on computerized text summarization tried to present solutions to this challenging *** models are employed extensively in text summarization *** attention techniques are utilized to acquire the context data in the decoding ***,without real and efficient feature extraction,the produced summary may diverge from the core *** this article,we present an encoder-decoder attention system employing dual attention *** the dual attention mechanism,the attention algorithm gathers main data from the encoder *** the dual attentionmodel,the system can capture and producemore rational main *** merging of the two attention phases produces precise and rational text *** enhanced attention mechanism gives high score to text repetition to increase phrase *** also captures the relationship between phrases and the title giving them higher *** assessed our proposed model with or without significance optimization using ablation *** model with significance optimization achieved the highest performance of 96.7%precision and the least CPU time among other models in both training and sentence extraction.

关键词： Text summarization attention model phrase significance

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Epigenetic regulation of insulin-like growth factor axis in hepatocellular carcinoma

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World Journal of Gastroenterology 2016年第9期22卷 2668-2677页

作者： Hend Mohamed El Tayebi ahmed Ihab abdelaziz The Molecular Pathology Research Group Department of Pharmacology and Toxicology Faculty of Pharmacy and Biotechnology German University in Cairo

The insulin-like growth factor(IGF) signaling path-way is an important pathway in the process of hepa-tocarcinogenesis,and the IGF network is clearly dysregulated in many cancers and developmental *** hepatocellular carcinoma(HCC),only a minority of patients are eligible for curative treatments,such as tumor resection or liver ***,there is a high recurrence of HCC after surgical tumor *** research efforts have focused on targeting IGF axis members in an attempt to find therapeutic options for many health *** this review,we shed lights on the regulation of members of the IGF axis,mainly by micro RNAs in *** RNAs in HCC attempt to halt the aberrant expression of the IGF network,and a single micro RNA can have multiple downstream targets in one or more signaling *** micro RNAs is a relatively new approach for identifying an efficient radical cure for HCC.

关键词： Hepatocellular carcinoma Insulin-like growth factors Insulin-like growth factor receptors Epigenetics microRNAs

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Deep Learning Prediction Model for Heart Disease for Elderly Patients

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Intelligent Automation & Soft Computing 2023年第2期35卷 2527-2540页

作者： Abeer Abdulaziz AlArfaj hanan ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The detection of heart disease is a problematic task in medical *** diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical *** massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things ***,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart *** proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning *** mod-el performance is veriﬁed with a full and minimum set of *** features enhance the processing time of the classiﬁcation process while the accuracy is *** performance of classiﬁers with less features has been analyzed with experimental *** proposed system is built on the Internet of Things Platform for medical data for the classiﬁcation process which aids medical profes-sionals to detect heart diseases through cloud *** results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble *** analysis of the proposed diagnostic system can determine the heart disease risks efﬁ*** results demonstrate thatﬂexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.

关键词： Heart disease internet of things deep learning

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Automated Patient Discomfort Detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 2559-2577页

作者： Imran ahmed Iqbal Khan Misbah Ahmad Awais Adnan hanan Aljuaid Center of Excellence in Information Technology Institute of Management SciencesPeshawarPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)RiyadhSaudi Arabia

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been *** IoTbased sensors are used in the healthcare sector and offer quality and safe care to *** work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively *** do this,the overhead view patients’data set has been *** testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based *** model uses confidence maps and detects 18 different key points at various locations of the body of the *** association rules and part affinity fields,the detected key points are later converted into six main body ***,the distance of subsequent key points is measured using coordinates ***,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal *** accuracy of the proposed system is assessed on various test *** experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

关键词： Artificial intelligence patient monitoring discomfort detection deep learning

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Catalytic Effect of Transition Metal Complexes of Triaminoguanidine on the Thermolysis of Energetic NC/DEGDN Composite

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火炸药学报 2024年第3期47卷 209-219,I0003页

作者： Mohammed Dourari ahmed Fouzi Tarchoun Djalal Trache Amir abdelaziz Roufaida Tiliouine Tessnim Barkat Weiqiang Pang Energetic Materials Laboratory(EMLab) Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria Xi′an Modern Chemistry Research Institute Xi′an 710065China

The transition metal complexes of triaminoguanidine(TAG-M,where M=Cobalt(Co)or Iron(Fe))have been *** catalytic effect of these complexes on the thermolysis of energetic composite based on nitrocellulose and diethylene glycol dinitrate,has been *** characterization of the resulting energetic composites was carried out using scanning electron microscopy(SEM),X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),and differential scanning calorimetry(DSC).Isoconversional kinetic analysis was performed to determine the Arrhenius parameters associated with the thermolysis of the elaborated energetic *** is found that TAG-M complexes have strong catalytic effect on the thermo-kinetic decomposition of NC/DEGDN by decreasing the apparent activation energy and significantly increased the total heat *** models that govern the decomposition processes are also studied,and it is revealed that different reaction processes are accomplished by introduction metal complexes of ***,this study serves as a valuable reference for future research focused on the investigation of catalytic combustion features of solid propellants.

关键词： triaminoguanidine transition metal complexes nitrocellulose diethylene glycol dinitrate catalytic effect

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Chemical design and characterization of cellulosic derivatives containing high-nitrogen functional groups:Towards the next generation of energetic biopolymers

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Defence Technology（防务技术） 2022年第4期18卷 537-546页

作者： ahmed Fouzi Tarchoun Djalal Trache Thomas M.Klapotke Amir abdelaziz Mehdi Derradji Slimane Bekhouche Energetic Materials Laboratory Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria Energetic Propulsion Laboratory Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria Department of Chemistry Ludwig Maximilian University Butenandtstrasse 5-13(D)D-81377MunichGermany Process Engineering Laboratory Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria

In this research,a promising class of insensitive and high-energy dense biopolymers,which contain nitrogen-rich 1H-tetrazol-1-yl acetate and nitrate ester functional groups,was successfully synthesized through tetrazole derivatization and nitration of cellulose and its micro-sized derivative(TNCN and TCMCN).Their molecular structures,physicochemical properties,thermal behaviors,mechanical sensitivities and detonation performances were studied and compared to those of the corresponding nitrocellulose and nitrated micro-sized cellulose(NCN and CMCN).The developed energetic TNCN and TCMCN exhibited insensitive character with excellent features such as density of 1.710 g/cm3and 1.726 g/cm3,nitrogen content of 20.95%and 22.59%,and detonation velocity of 7552 m/s and 7786 m/s,respectively,and thereby demonstrate their potential applications as new generation of energetic biopolymers to substitute the common ***,thermal results showed that the designed nitrated and chemical modified cellulosic biopolymers displayed good thermal stability with multistep decomposition *** results enrich future prospects for the design of promising insensitive and high-energy dense cellulose-rich materials and commence a new chapter in this field.

关键词： Cellulose Surface functionalization Tetrazole Nitrate ester Nitrogen-rich polymer Energetic properties

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