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Web Based Survey of Adult Population of West and Central Saudi Arabia about Cause and Management of Keratoconus

Open Journal of Ophthalmology 2020年第4期10卷 341-350页

作者： Sultan Alzuhairy Raghad Hamza Moaz Alharbi Mohammed Alqadi Atheer Alharthi Khames Torki Alzahrani ali hassan alshehri Department of Ophthalmology Qassim University Buraidah Saudi Arabia Medical College Taif University Taif Saudi Arabia Faculty of Health Sciences King Abdul Aziz University Jeddah Saudi Arabia Medical College Najran University Najran Saudi Arabia Ministry of Health Jeddah Saudi Arabia

Background: Keratoconus (KC) causes visual disability in young adults. To improve its early detection and compliance, improving the awareness of patients and their relatives is crucial. We present the level of awareness about KC among the adult population of western Saudi Arabia and its determinants. Methods: This web-based survey was carried out from June to August 2020 in urban and semi-urban areas in western Saudi Arabia. Demographic and eye ailment profiles were collected. To generate responses on the presentation, causes, management and prevention of KC, a five-point Likert scale was used. The logit values were calculated and graded into good and poor awareness. Awareness was associated with other determinants. Results: We surveyed 420 Saudi adults. The median logit score of awareness about KC among participants was 2.86 (inter quartile range (IQR) 0.86;2.95). The median score of knowledge about the causes and presentation of KC was 2.86 (IQR 1.1;2.86) and for management, it was 0.00 (IQR 0.00;0.18). The level of awareness was good in 286 participants (68.1%) and poor in 134 (31.9%). Residents of major cities (P Conclusions: Awareness about the presentation, causes and management of KC was good in two-thirds of adults but with some variation. Strategies to involve the community and stress health education need to be urgently implemented. Campaigns should target the male and semi-urban population in western Saudi Arabia.

关键词： Keratoconus Public Saudi Arabia Awareness

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Structure-based discovery of *** phytochemicals as potential inhibitors against Monkeypox(mpox)viral protein

Journal of Biosafety and Biosecurity

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Journal of Biosafety and Biosecurity 2024年第3期6卷 157-169页

作者： Ranjan K.Mohapatra Ahmed Mahal Pranab K.Mohapatra Ashish K.Sarangi Snehasish Mishra Meshari A.Alsuwat Nada N.alshehri Sozan M.Abdelkhalig Mohammed Garout Mohammed Aljeldah Ahmad A.alshehri Ahmed Saif Mohammed Abdulrahman Alshahrani ali S.Alqahtani Yahya A.Almutawif Hamza M.A.Eid Faisal M Albaqami Mohnad Abdalla ali A.Rabaan Department of Chemistry Government College of EngineeringKeonjhar 758002OdishaIndia Department of Medical Biochemical Analysis College of Health TechnologyCihan University–ErbilErbilKurdistan RegionIraq Department of Chemistry CV Raman Global UniversityBhubaneswarOdishaIndia Department of Chemistry School of Applied SciencesCenturion University of Technology and ManagementBalangirOdishaIndia School of Biotechnology Campus-11KIIT Deemed-to-be-UniversityBhubaneswar 751024OdishaIndia Clinical Laboratory Sciences Department College of Applied Medical SciencesTaif UniversityAl-Taif 21974Saudi Arabia Internal Medicine Department King Khalid University Medical CityCollege of MedicineAbha 61481Saudi Arabia Department of Basic Medical Sciences College of MedicineAlMaarefa UniversityRiyadhSaudi Arabia Department of Community Medicine and Health Care for Pilgrims Faculty of MedicineUmm Al-Qura UniversityMakkah 21955Saudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesUniversity of Hafr Al BatinHafr Al Batin 39831Saudi Arabia Department of Clinical Laboratory Sciences Faculty of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Medical Laboratory Sciences Faculty of Applied Medical SciencesKing Khalid UniversityAbha 61481Saudi Arabia mDepartment of Medical Laboratories Technology College of Applied Medical SciencesTaibah UniversityMadinah 41411Saudi Arabia Biology Department College of ScienceIslamic University of MadinahMadinah 42351Saudi Arabia Pediatric Research Institute Children’s Hospital Affiliated to Shandong UniversityJinanShandong 250022China Molecular Diagnostic Laboratory Johns Hopkins Aramco HealthcareDhahran 31311Saudi Arabia College of Medicine Alfaisal UniversityRiyadh 11533Saudi Arabia Department of Public Health and Nutrition The University of HaripurHaripur 22610Pakistan

Outbreaks of Monkeypox(mpox)in over 100 non-endemic countries in 2022 represented a serious global health *** a neglected disease,mpox has become a global public health issue.A42R profilinlike protein from mpox(PDB ID:4QWO)represents a potential new lead for drug development and may interact with various synthetic and natural *** this report,the interaction of A42R profilinlike protein with six phytochemicals found in the medicinal plant Ficus religiosa(abundant in India)was *** on the predicted and compared protein–ligand binding energies,biological properties,IC_(50) values and toxicity,two compounds,kaempferol(C-1)and piperine(C-4),were *** characteristics and quantitative structure–activity relationship(QSAR)of these two compounds were determined,and molecular dynamics(MD)simulations were *** silico examination of the kaempferol(C-1)and piperine(C-4)interactions with A42R profilin-like protein gave best-pose ligandbinding energies of–6.98 and–5.57 kcal/mol,*** predicted IC_(50) of C-1 was 7.63 lM and 82 lM for *** data indicated that kaempferol and piperine are non-mutagenic,and the QSAR data revealed that piperlongumine(5.92)and piperine(5.25)had higher log P values than the other compounds *** simulations of A42R profilin-like protein in complex with C-1 and C-4 were performed to examine the stability of the ligand–protein ***/C and C-4 showed the highest affinity and activities,they may be suitable lead candidates for developing mpox therapeutic *** study should facilitate discovering and synthesizing innovative therapeutics to address other infectious diseases.

关键词： Monkeypox Mpox Phytochemical Molecular docking MD simulation ADMET QSAR

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Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

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Computers, Materials & Continua 2022年第8期72卷 2411-2427页

作者： Mubashir ali Anees Baqir Hafiz Husnain Raza Sherazi Asad Hussain Asma hassan alshehri Muhammad ali Imran Department of Management Information and Production EngineeringUniversity of BergamoBergamo24129Italy Department of Environmental Sciences Informatics and StatisticsCa’Foscari University of VeniceVenice30123Italy School of Computing and Engineering University of West LondonLondonW55RFUK Department of Engineering and Applied Sciences University of BergamoBergamo24129Italy Durma College of Science and Humanities Shaqra UniversityShaqra11961Saudi Arabia School of Engineering University of GlasgowGlasgowG128QQUK

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades,social media platforms(such as Facebook,Twitter,and Instagram)have consumed a large proportion of time in our daily *** tend to stay alive on their social media with recent updates,as it has become the primary source of interactionwithin social *** social media platforms offer several remarkable features but are simultaneously prone to various critical *** studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression,anxiety,suicide commitment,and mental disorder,particularly in the young adults who have excessively spent time on socialmedia which necessitates a thorough psychological analysis of all these *** study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status *** this paper,we start with depression detection in the first instance and then expand on analyzing six other psychotic issues(e.g.,depression,anxiety,psychopathic deviate,hypochondria,unrealistic,and hypomania)commonly found in adults due to extreme use of social media *** classify the psychotic issues with the user’s mental state,we have employed different Machine Learning(ML)classifiers i.e.,Random Forest(RF),Support Vector Machine(SVM),Naïve Bayes(NB),and K-Nearest Neighbor(KNN).The used ML models are trained and tested by using different combinations of features selection *** observe themost suitable classifiers for psychotic issue classification,a cost-benefit function(sometimes termed as‘Suitability’)has been used which combines the accuracy of the model with its execution *** experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.

关键词： Psychotic behaviors mental health socialmedia machine learning

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The Impact of Blood Transfusion on the Efficiency of Stem Cell Transplants

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Case Reports in Clinical Medicine 2024年第8期13卷 328-338页

作者： Amer Saud AL-Humaidan Saleh Saud Almutairi Mohammed Hussain Khubrani Raffah Mahdi Bajudah Waleed Mohammad Alzabidi Mohammed Hafiz Almasabi Bader Mohammed Saleh Alhomaid Waad ali alshehri Wejdan Saleh Alghamdi Raghad Abdullah Alwthinani Department of Pathology and Laboratory King Abdulaziz Medical City Ministry of National Guard Health Affairs Riyadh Saudi Arabia

Background: While blood product transfusion is essential for managing hematologic deficits in Allogenic Hematopoietic stem cell transplant (AHSCT) recipients, it has risks including infectious disease transmission, alloimmunization, and transfusion reactions. These risks have sparked an ongoing debate regarding the overall impact of transfusions on patient outcomes. Thus, this study aimed to evaluate the impact of Red Blood Cells (RBCs) and/or platelet transfusion on the infection incidence and overall survival in AHSCT patients. Methods: We performed a retrospective analysis of clinical and laboratory data of sixty adult patients with primary malignant hematological disorder who had undergone AHSCT. Participants’ data were categorized into two groups;Group 1 (low transfusion group) consisted of patients receiving 10 units. Quantitative data were expressed as mean ± SD. The t-test of significance and Chi-square (χ2) test were used, with p ≤ 0.05 considered significant. Result: A total of 60 patients’ data was included. In Group 1, out of 30 patients, 13 (43.33%) developed infections. In contrast, Group 2 had 21 (70%) out of 30 patients develop infections. Group 1 had a higher survival rate (57.8%) than Group 2 (transfusion > 10 units) (46.2%) with a chi-square value = 23.56, and p-value Conclusion: The volume of blood product transfusions has a considerable impact on patient outcomes, particularly infection and survival rates. Additional long-term prospective studies and larger randomized controlled trials are needed to strengthen the evidence for determining transfusion protocols for these patients.

关键词： Hematologic Neoplasms Hematopoietic Stem Cell Transplantation Blood Transfusion Survival Rate

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Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

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Computer Systems Science & Engineering 2023年第2期44卷 1679-1689页

作者： Abdullah alshehri Nayeem Khan ali Alowayr Mohammed Yahya Alghamdi Information Technology Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Computer Science Department Faculty of Science and Arts at BuljurashiAl Baha UniversityAl Baha65799Saudi Arabia

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior *** framework models the user behavior as sequences of events representing the user activities at such a *** represented sequences are thenﬁtted into a recurrent neural network model to extract features that draw distinctive behavior for individual ***,the model can recognize frequencies of regular behavior to proﬁle the user manner in the *** subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular *** importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the *** detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including ***,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reﬂect a normal network workﬂ*** contrast,the irregular patterns can trigger an alert for a potential *** framework has been fully described where the evaluation metrics have also been *** experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM *** paper has been concluded with pro-viding the potential directions for future improvements.

关键词： Cybersecurity deep learning machine learning user behavior analytics

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A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

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Computers, Materials & Continua 2022年第10期73卷 641-655页

作者： Abdullah A.Asiri Amna Iqbal Javed Ferzund Tariq ali Muhammad Aamir Khalaf A.Alshamrani hassan A.Alshamrani Fawaz F.Alqahtani Muhammad Irfan ali H.D.alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan College of Engineering Najran UniversityNajran61441Saudi Arabia

Abnormal growth of brain tissues is the real cause of brain *** for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a *** manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level *** address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of *** this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN *** handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant *** model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain *** the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing *** modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.

关键词： Brain tumor magnetic resonance images convolutional neural network classification

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Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

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Computers, Materials & Continua 2022年第12期73卷 5735-5753页

作者： Abdullah A.Asiri Muhammad Aamir Ahmad Shaf Tariq ali Muhammad Zeeshan Muhammad Irfan Khalaf A.Alshamrani hassan A.Alshamrani Fawaz F.Alqahtani ali H.D.alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan 66000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperforms the best results associated with the existing methods.

关键词： CNN brain tumor block-wise structure VGG19 VGG16

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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

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Computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan hassan A.Alshamrani Fawaz F.Alqahtani Osama M.alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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Mordell Elliptic Curve Based Design of Nonlinear Component of Block Cipher

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Computers, Materials & Continua 2022年第11期73卷 2913-2930页

作者： Hafeez ur Rehman Tariq Shah Mohammad Mazyad Hazzazi ali alshehri Bassfar Zaid Department of Mathematics Quaid-i-Azam UniversityIslamabadPakistan Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Science University of TabukTabuk71491Saudi Arabia Department of Information Technology University of TabukTabuk71491Saudi Arabia

Elliptic curves(ECs)are deemed one of the most solid structures against modern computational attacks because of their small key size and high *** many well-known cryptosystems,the substitution box(Sbox)is used as the only nonlinear portion of a security ***,it has been shown that using dynamic S-boxes rather than static S-boxes increases the security of a *** conferred study also extends the practical application of ECs in designing the nonlinear components of block ciphers in symmetric key *** this study,instead of the Mordell elliptic curve(MEC)over the prime field,the Galois field has been engaged in constructing the S-boxes,the main nonlinear component of the block ***,the proposed scheme uses the coordinates of MEC and the operation of the Galois field to generate a higher number of S-boxes with optimal nonlinearity,which increases the security of *** proposed S-boxes resilience against prominent algebraic and statistical attacks is evaluated to determine its potential to induce confusion and produce acceptable results compared to other ***,the majority logic criteria(MLC)are used to assess the new S-boxes usage in the image encryption application,and the outcomes indicate that they have significant cryptographic strength.

关键词： Galois field Mordell elliptic curve nonlinearity substitution box

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq ali Muhammad Aamir ali Usman Muhammad Irfan hassan A.Alshamrani Khlood M.Mehdar Osama M.alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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