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Infectious causes of fever of unknown origin in developing countries: An international ID-IRI study

Journal of Intensive Medicine 2024年第1期4卷 94-100页

作者： Hakan Erdem jaffar aal-tawfiq Maha Abid Wissal Ben Yahia George Akafity Manar Ezzelarab Ramadan Fatma Amer Amani El-Kholy Atousa Hakamifard Bilal Ahmad Rahimi Farouq Dayyab Hulya Caskurlu Reham Khedr Muhammad Tahir Lysien Zambrano Mumtaz Ali Khan Aun Raza Nagwa Mostafa El-Sayed Magdalena Baymakova Aysun Yalci Yasemin Cag Umran Elbahr Aamer Ikram Department of Infectious Diseases&Clinical Microbiology Gulhane School of MedicineTurkish Health Sciences UniversityAnkaraTurkey Specialty Internal Medicine and Quality and Patient Safety Department Johns Hopkins Aramco HealthcareDhahranSaudi Arabia Division of Infectious Diseases Indiana University School of MedicineIndianapolisINUSA Department of Infectious Diseases Farhat Hached University HospitalIbn El Jazzar Medical SchoolUniversity of SousseSousseTunisia Internal Medicine Department Farhat Hached University HospitalIbn El Jazzar Medical SchoolUniversity of SousseSousseTunisia Cape Coast Teaching Hospital Cape CoastGhana Medical Parasitology Faculty of MedicineSuez UniversitySuezEgypt Department of Medical Microbiology and Immunology Faculty of MedicineZagazig UniversityZagazigEgypt Department of Clinical Pathology Faculty of MedicineCairo UniversityGizaEgypt Department of Infectious Diseases School of MedicineIsfahan University of Medical SciencesIsfahanIran Department of Infectious Diseases Kandahar University Medical FacultyTeaching HospitalKandaharAfghanistan Department of Infectious Diseases Mohammed Bn Khalifa Bn Salman Alkhalifa Specialist Cardiac CenterAwaliBahrain Department of Infectious Diseases and Clinical Microbiology Faculty of MedicineIstanbul Medeniyet UniversityIstanbulTurkey Department of Pediatric Oncology National Cancer Institute–Cairo UniversityChildren Cancer Hospital EgyptCairoEgypt Department of Medicine Federal General HospitalIslamabadPakistan Institute for Research in Medical Sciences and Right to Health(ICIMEDES) Scientific Research Unit(UIC)Faculty of Medical Sciences(FCM)National Autonomous University of Honduras(UNAH)TegucigalpaHonduras National Institute of Health IslamabadPakistan Shaukat Khanum Memorial Cancer Hospital and Research Center LahorePakistan Medical Parasitology Department Research Institute of OphthalmologyGizaEgypt Department of Infectious Diseases Military Medical AcademySofiaBulgaria Department of Infecti

Background:Fever of unknown origin(FUO)in developing countries is an important dilemma and further research is needed to elucidate the infectious causes of ***:A multi-center study for infectious causes of FUO in lower middle-income countries(LMIC)and lowincome countries(LIC)was conducted between January 1,2018 and January 1,*** total,15 participating centers from seven different countries provided the data,which were collected through the Infectious DiseasesInternational Research Initiative *** adult patients with confirmed infection as the cause of FUO were included in the *** severity parameters were quick Sequential Organ Failure Assessment(qSOFA)≥2,intensive care unit(ICU)admission,vasopressor use,and invasive mechanical ventilation(IMV).Results:A total of 160 patients with infectious FUO were included in the ***,148(92.5%)patients had community-acquired infections and 12(7.5%)had hospital-acquired *** most common infectious syndromes were tuberculosis(TB)(n=27,16.9%),infective endocarditis(n=25,15.6%),malaria(n=21,13.1%),brucellosis(n=15,9.4%),and typhoid fever(n=9,5.6%).Plasmodium falciparum,Mycobacterium tuberculosis,Brucellae,Staphylococcus aureus,Salmonella typhi,and Rickettsiae were the leading infectious agents in this study.A total of 56(35.0%)cases had invasive procedures for *** mean qSOFA score was 0.76±0.94{median(interquartile range[IQR]):0(0–1)}.ICU admission(n=26,16.2%),vasopressor use(n=14,8.8%),and IMV(n=10,6.3%)were not ***,38(23.8%)patients had at least one of the severity *** mortality rate was 15(9.4%),and the mortality was attributable to the infection causing FUO in 12(7.5%)***:In LMIC and LIC,tuberculosis and cardiac infections were the most severe and the leading infections causing FUO.

关键词： Developing countries Fever of unknown origin Infection

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Decision Aid Model for Private-owned Electric Vehicles Participating in Frequency Regulation Ancillary Service Market

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 621-629页

作者： Liwei Wang Yingyun Sun Haotian Wang Pengfei Zhao Muhammad Safwan jaffar School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China

To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode(BM), unidirectional charging mode(UCM), and bidirectional charging/discharging mode(BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.

关键词： Electric vehicle(EV) frequency regulation decision aid model(DAM) utility maximization battery wear cost

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Weber Law Based Approach for Multi-Class Image Forgery Detection

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Computers, Materials & Continua 2024年第1期78卷 145-166页

作者： Arslan Akram Javed Rashid Arfan jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar Department of Computer Science Superior UniversityLahore54000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science Bahria UniversityLahore CampusLahore54600Pakistan

Today’s forensic science introduces a new research area for digital image analysis formultimedia ***,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered *** forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing ***,an image can contain one or more types of *** study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial ***,images are divided into equal blocks to get scale-invariant *** law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing *** proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and ***(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and *** average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

关键词： Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Beyond bacteria:Role of non-bacterial gut microbiota species in inflammatory bowel disease and colorectal cancer progression

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World Journal of Gastroenterology 2024年第36期30卷 4078-4082页

作者： Hania Haque Syeda Warisha Zehra Mohammad Shahzaib Saif Abbas Nazish jaffar of Medicine Jinnah Sindh Medical UniversityKarachi 75510SindhPakistan Department of Pathology Jinnah Sindh Medical UniversityKarachi 75510SindhPakistan

This letter emphasizes the need to expand discussions on gut microbiome’s role in inflammatory bowel disease(IBD)and colorectal cancer(CRC)by including the often-overlooked non-bacterial components of the human gut *** highlights how viral,fungal and archaeal inhabitants of the gut respond towards gut dysbiosis and contribute to disease *** such as bacteriophages target certain bacterial species and modulate the immune *** viruses found associated include Epstein-Barr virus,human papillomavirus,John Cunningham virus,cytomegalovirus,and human herpes simplex virus type *** such as Candida albicans and Malassezia contribute by forming tissue-invasive filaments and producing inflammatory cytokines,***,mainly methanogens are also found altering the microbial fermentation *** correspondence,thus underscores the significance of considering the pathological and physiological mechanisms of the entire spectrum of the gut microbiota to develop effective therapeutic interventions for both IBD and CRC.

关键词： Gut microbiota Colorectal cancer Inflammatory bowel disease Dysbiosis Bacteriophages Methanogens Fungi

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Middle East respiratory syndrome coronavirus： current situation and travel-associated concerns

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Frontiers of Medicine 2016年第2期10卷 111-119页

作者： jaffar A. Al-tawfiq Ali S. Omrani Ziad A. Memish johns Hopkins Aramco Healthcare Dhahran 31311 Kingdom of Saudi Arabia Indiana University School of Medicine Indianapolis IN 46202-3082 USA Department of Medicine Section of Infectious Diseases King Faisal Specialist Hospital and Research Centre Riyadh 11211 Kingdom of Saudi Arabia Ministry of Health Riyadh 11514 Kingdom of Saudi Arabia College of Medicine Alfaisal University Riyadh 11533 Kingdom of Saudi Arabia

The emergence of Middle East respiratory syndrome coronavirus （MERS-CoV） in 2012 brought back memories of the occurrence of severe acute respiratory syndrome coronavirus （SARS-CoV） in 2002. More than 1500 MERS-CoV cases were recorded in 42 months with a case fatality rate （CFR） of 40%. Meanwhile, 8000 cases of SARS-CoV were confirmed in six months with a CFR of 10%. The clinical presentation of MERS-CoV ranges from mild and non-specific presentation to progressive and severe pneumonia. No predictive signs or symptoms exist to differentiate MERS-CoV from community-acquired pneumonia in hospitalized patients. An apparent heterogeneity was observed in transmission. Most MERS-CoV cases were secondary to large outbreaks in healthcare settings. These cases were secondary to community-acquired cases, which may also cause family outbreaks. Travel-associated MERS infection remains low. However, the virus exhibited a clear tendency to cause large outbreaks outside the Arabian Peninsula as exemplified by the outbreak in the Republic of Korea. In this review, we summarize the current knowledge about MERS-CoV and highlight travel-related issues.

关键词： coronavirus MERS Middle East respiratory syndrome

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Middle East respiratory syndrome coronavirus in pediatrics: a report of seven cases from Saudi Arabia

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Frontiers of Medicine 2019年第1期13卷 126-130页

作者： Sarah H. Alfaraj jaffar A. Al-tawfiq Talal A. Altuwaijri Ziad A. Memish University of British Columbia VancouverV6T 1Z4Canada Corona Center Infectious Diseases DivisionDepartment of PediatricPrince Mohamed Bin Abdulaziz HospitalMinistry of HealthRiyadh 11676Saudi Arabia Indiana University School of Medicine IndianapolisIN 46202USA Johns Hopkins Aramco Healthcare Dhahran 31311Saudi Arabia Department of Surgery King Saud UniversityRiyadh 11692Saudi Arabia College of Medicine Alfaisal UniversityRiyadh 11533Saudi Arabia Infectious Diseases Division Department of MedicinePrince Mohamed Bin Abdulaziz HospitalMinistry of HealthRiyadh 11676Saudi Arabia Department of Global Health Rollins School of Public HealthEmoiy UniversityAtlantaGA 30322USA

Infection with Middle East respiratory syndrome coronavirus (MERS-CoV) emerged in 2012 as an important respiratory disease with high fatality rates of 40%—60%. Despite the increased number of cases over subsequent years, the number of pediatric cases remained low. A review of studies conducted from June 2012 to April 19, 2016 reported 31 pediatric MERS-CoV cases. In this paper, we present the clinical and laboratory features of seven patients with pediatric MERS. Five patients had no underlying medical illnesses, and three patients were asymptomatic. Of the seven cases, four (57%) patients sought medical advice within 1-7 days from the onset of symptoms. The three other patients (43%) were asymptomatic and were in contact with patients with confirmed diagnosis of MERS-CoV. The most common presenting symptoms were fever (57%), cough (14%), shortness of breath (14%), vomiting (28%), and diarrhea (28%). Two (28.6%) patients had platelet counts of < 150 ×10^9/L, and one patient had an underlying end-stage renal disease. The remaining patients presented with normal blood count, liver function, and urea and creatinine levels. The documented MERS-CoV Ct values were 32 38 for four of the seven cases. Two patients (28.6%) had abnormal chest radiographic findings of bilateral infiltration. One patient (14.3%) required ventilator support, and two patients (28.6%) required oxygen supplementation. All the seven patients were discharged without complications.

关键词： Middle East respiratory syndrome coronavirus MERS-CoV pregnancy pediatrics

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