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Prevalence of intestinal parasite infections among patients in local public hospitals of Hail,Northwestern Saudi Arabia

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Asian Pacific Journal of Tropical Medicine 2016年第1期9卷 41-45页

作者： omar hassen am.r Ibraheem M Ashankyty Najoua Al Sadok Haouas Department of Clinical Laboratory Science College of Applied Medical SciencesUniversity of Hail Departement de Biologie Clinique B Laboratoire de Parasitologie-Mycologie Medicate et Moleculaire(LR12ES08)Faculte de PharmacieUniversite de Monastir

Objective:To evaluate the prevalence of intestinal parasites among patients in Hail,Northwestern Saudi ***:Stool samples were collected from 130 patients(69females and 61 males) in Hail General *** sample was examined by direct wet mount microscopic examination using both normal saline and Lugol's iodine preparation and concentration techniques using salt and formol-ether *** stained smears were performed for intestinal coccidian using modified Ziehl-Neelsen ***:The overall prevalence of intestinal parasitic infection was 45.38%(59 cases).Forty-four(33.84%)were found to be infected with one or more intestinal protozoa.5(3.84%) were infected with helminthes and 10(7.69%) had mixed infection with both helminthes and *** most common intestinal helminth detected was Ancyloxtoma duvdenale(n=5.3.84%),followed by Ascaris *** *** Trichuris trichiura(n=2 for each species,1.5%).For intestinal protozoa,the coccidian Cryptosporidium parvum(n=25,19.23%) was the most common followed by Entamoeba histolytica/dispur(n=21,16.15%),Giurdia lamblia(n=15,11.54%),Entamoeba coli(n=5.3.85%) and Blastocysts hominis(n=3,2.30%).The prevalence of intestinal parasitic infections in females was significantly higher than in males(Prst study highlighting that intestinal parasites are still an important public health problem in Northwestern Saudi Arabia Therefore,health education would be the best way to prevent from intestinal parasite infections which are mainly food borne diseases.

关键词： Prevalence Intestinal parasites Hail KSA

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Nature-Inspired Level Set Segmentation Model for 3D-MrI Brain Tumor Detection

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Computers, Materials & Continua 2021年第7期68卷 961-981页

作者： Oday Ali hassen Sarmad omar Abter Ansam A.Abdulhussein Saad M.Darwish Yasmine M.Ibrahim Walaa Sheta Department of Computer Techniques Engineering Imam Al-Kadhum College(IKC)WasitIraq University of Information Technology and Communications Media Technology Engineering DepartmentIraq University of Information Technology and Communications Baghdad10081Iraq Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University63 Horreya AvenueEl–ShatbyAlexandria21526Egypt Informatics research Institute City of Scientific Research and Technology ApplicationsBorg EL Arab21934Egypt

Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer *** tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy *** prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex *** methods for detection and segmentation presently exist,but all lack high *** paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor ***,attention concentrated on the challenges related to level set *** study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set *** proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and *** suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast ***,P-ABCC is used to obtain tumor edges that are utilized as an initial MrI sequence *** level-set segmentation is then used to detect tumor regions from all volume slices with fewer *** suggest improved model efficiency compared to state-of-the-art methods for both datasets BrATS 2019 and BrATS *** BrATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%*** BrATS 2017,an increase in precision for WT was reac

关键词： 3D-MrI tumor diagnosis bio-inspired clustering ABC optimization multimodal detection

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