Iridoid glycosides are a group of naturally occurring chemical compounds. They are a large family of compounds biosynthesized by plants, they often have pharmacological effects. The aim of this study is to isolate and identified iridoid glycoside in a newly studied, cultivated in Iraq named Gardenis jasminoides. The medicinal importance of iridoid glycoside, on one hand and absence of phytochemical investigation on leaves of Gardenia on the other hand, acquired this study its importance. Many compounds were isolated from leaves plant part one of these compounds was identified by different chemical analysis like: melting point (MP), thin layer chromatography (TLC), Fourier transforms infrared spectra (FTIR) and high performance l

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.

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,

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 digital dermatoglyphics were studied in 120 females derived from northern region of Iraq (60 Arabs and 60 Kurds). Two kinds of analyses were perfomed : Quantitative and Qualtative. The unilateral and bilateral analyses for dermal ridge counts in each digital and the overall did not reveal any significant difference when t-test was used. A high correlation coefficients were revealed in this study between homologous and adjacent digits, moreover, significant differences were revealed between Arabian and Kurdish samples in both analyses when Fisher Z transform test was used, but the significant differences in the bilateral analysis exceed the ones in the unilateral. This indicates the importance of the former analysis in detecting the vari

This research paper is about thevariationin the degree of Continentality climate of the
Iraq during (40) years for a number of climate station. Using Poresof formula, it is found out
that the climate of Iraq ranges between extreme Continentality and very extreme
Continentality, and that the Continentality degree is characterized with extreme frequency
from one year to another. In certain years, the degree of climate Continentality decreases
while in other years it rises in such a way that there is no similarity in the Continental degree
from one year to another for the same station.
As for the general trend of the degree of Continentality, the last years had noticed
special variations, which are divided in to thre