Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.
Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreVegetation monitoring is considered an important application in remote sensing task due to variation of vegetation types and their distribution. The vegetation concentration around the Earth is increase in 5% in 2000 according to NASA monitoring. This increase is due to the Indian vegetable programs. In this research, the vegetation monitoring in Baghdad city was done using Normalized Difference Vegetation Index (NDVI) for temporal Landsat satellite images (Landsat 5 TM& Landsat 8 OIL). These images had been used and utilize in different times during the period from 2000, 2010, 2015 & 2017. The outcomes of the study demonstrate that a change in the vegetation Cover (VC) in Baghdad city. (NDVI) generally shows a
... Show MoreBackground: Complete seal of the root canal system following its chemo-mechanical debridement plays a pivotal role for achieving successful endodontic treatment. This can be established by reducing the gaps between the core filling material and root canal wall. Aim: To assess and compare the dislocation resistance of root canals obturated with GuttaFusion® and TotalFill BC sealer versus single cone obturation technique and TotalFill BC sealer after instrumentation of the canals with WaveOne, ProTaper Next and ProTaper Universal system. Material and Method: Sixty extracted human permanent mandibular premolars were conducted in the current study. The teeth were decorated and left the root with 15mm length; the roots were divided randomly i
... Show MoreThe Vulnerable Indian Roofed Turtle Pangshura tecta (Gray, 1831) (Testudines: Geoemydidae) occurs in the Sub-Himalayan lowlands of India, Nepal, Bangladesh, and Pakistan. Little is known about its natural history, no studies have been conducted revealing its natural predators. In this study, a group of Large-billed Crow Corvus macrorhynchos Wagler, 1827 (Passeriformes: Corvidae) was observed hunting and predating on an Indian Roofed Turtle carcass in the bank of river Kuakhai, Bhubaneswar, India. The first record of this predation behaviour is reported and substantiated by photographic evidence.
Objective: To determine the ability of uVDBP to discern SRNS from steroid-sensitive nephrotic syndrome (SSNS) in Iraqi children. Materials and Methods: This cross-sectional study enrolled children with SRNS (n=31) and SSNS (n=32) from the pediatric nephrology clinic of Babylon Hospital for Maternity and Pediatrics over three months. Patients' characteristics in terms of demographics, clinical data, and urinary investigations were collected. Quantitative analysis of uVDBP levels was undertaken via a commercially available ELISA kit. Results: The median uVDBP values were significantly higher (p-value<0.001) in the SRNS group (median=10.26, IQR=5.91 μg/mL) than in the SSNS group (median=0.953, IQR=4.12 μg/mL). A negative correlati
... Show MoreAryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a potent ligand for AhR and a known carcinogen. While AhR activation by TCDD leads to significant immunosuppression, how this translates into carcinogenic signal is unclear. Recently, we demonstrated that activation of AhR by TCDD in naïve C57BL6 mice leads to massive induction of myeloid derived-suppressor cells (MDSCs). In the current study, we investigated the role of the gut microbiota in TCDD-mediated MDSC induction. TCDD caused significant alterations in the gut microbiome, such as increases in Prevotella and Lactobacillus, while decreasing Sutterella and Bacteroides. Fecal transplants from TCDD-treated
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