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.

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

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

New hydrazide compounds (A2) and (A9) were prepared from their corresponding esters (A1, A8) .These esters were also prepared from their precursors 5-ethoxy carbonyl-(4methoxyphenyl)-6-methyl-1,2,3,4-tetrahydropyrimidine-2-thione (A1) via multicomponent reaction type and from hippuric acid respectively. The hydrazide compounds were then allowed to react with some aldehydes forming the corresponding hydrazones (A3-7) and (A1014). The synthesized compounds were characterized by IR, 1H-NMR, 13C-NMR, and Mass spectroscopies and well discussed. .

Abstract

The multiple linear regression model of the important regression models used in the analysis for different fields of science Such as business, economics, medicine and social sciences high in data has undesirable effects on analysis results . The multicollinearity is a major problem in multiple linear regression. In its simplest state, it leads to the departure of the model parameter that is capable of its scientific properties, Also there is an important problem in regression analysis is the presence of high leverage points in the data have undesirable effects on the results of the analysis , In this research , we present some of

Smoking-related diseases can be attributed to the inhalation of many different toxins, including heavy metals, which have a host of harmful health effects. The primary objective of this study was to determine whether local and imported cigarette brands used in Iraq , have they elevated levels of metals or not .Three metals Lead (Pb) , Cadmium (Cd) and Chromium(Cr) were determined in tobacco of seventeen brands of imported cigarettes commonly available in Iraq and three Iraqi domestic cigarettes , which were randomly taken from retail market in Baghdad by using flame atomic absorption spectrometry. The produced data of imported and local cigarette brands were discussed and compared together and with studies from elsewhere .The result

An experiment was conducted in pots with 4 Kg soil per pot in the green house of Biology department, College of Education (Ibn Al-Haithum), university of Baghdad, the growing season of (2008-2009), to study the effects of four levels of urea (0, 0.1, 0.2, 0.4) g/pot, that equivalent to (0, 100, 200 and 400) Kg/ha. Also three levels of superphosphate fertilizer (0.2, 0.1, and 0) g/pot, that equivalent to (200, 100, and 0) Kg/ha were added to the local yields components. The result showed that the high level of both fertilizer (400 Kg urea/ha and 200 Kg superphosphate/ha) caused a significant increase in number of pods/plant, dry weight of pods, the weight of 100 seeds and the weight of all seeds/plant, with a significant decrease in the c