Nowadays nanoparticles have widespread application in various industriesbecause of their special and unique features, there are many studies in sideeffects of nanomaterial. This study done by 40 white female mice withevery other day intraperitoneally injection of low and high doses of both ofZnO kg of body weight) and FeOnanoparticles (5 and 40 mg/kg). After a 15 days period, the mice weresacrificed and blood samples were collected for hormone analysis, andtissue samples for morphometric studies.Statistical Analysis shows significant differences in LH, Estrogen,Progesterone hormone levels between groups, while there are insignificantdifferences in Follicle stimulating hormone (FSH) level between thegroups compared with its level in

Some types of the fungus Aspergillus were isolated from some hospitals in the city of Baghdad (Imam Ali Hospital and Sadr General Hospital). The samples were taken by Transport media at a rate of three replicates of each place isolated from samples from different places within the hospital (waste, baths, the sick beds, corridors and room floors) for the purpose of isolating and diagnosing the fungus on the Czapeck Dox Agar media. It was noticed that the spread rate of fungus Aspergillus was 70% compared to other species that have emerged during the isolation process of the Sabouraud's Dextrose Agar media. The species A.niger (56.25%) was considered the most common type of fungus visible during the isolation process of the Imam Ali Hospit

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

The study is concern on determine the type of Candida spp.in leukemia patients that were infected with oral candidiasis as a result to their immune suppression (weekend immune system) due to their submission to radiation and chemotherapy treatment. The result showed that the most common isolates were C. guillermondii 19 which represent 31.66% of cases, then followed by C. itermedia 11 which represent 18.3%, while the less common isolates were for C. zeylamodes 3 which represent 5%.