In the current review, an updated list of dark beetle species (Coleoptera, Tenebrionidae) recorded in Iraq was given. The current paper is based on previous studies in the literature and contains all dark beetles referred to in Iraq, except for the species within the Pimelinae subfamily.
The investigation of this review showed the presence of 89 species belonging to 34 genera within five subfamilies. This work included mentioning the basionyms and synonyms for genera and species with their global distribution, as well as, correcting the scientific names that were mentioned in the previous checklists.

Introduction: Syphilis is a sexually transmitted disease, that may be transferred from mothers to infants during pregnancy if it is left untreated. Method: This study was conducted among 65 women who suffered from recurrent abortions in Iraq. Syphilis screening recombinant (IgM + IgG) level by ELISA, RADIM (Italy) and rapid plasma reagin (RPR) (positive and negative results) tests were used to analyse the data. Results: A non-significant association was observed with age (p=0.989), and the number of healthy births (p=0.643). Non-significant differences were observed in comparisons between smoker and non-smoker percentages in the study group. The rapid test for syphilis confirmation was applied using Rapid Plasma Reagin (RPR) tests.

The construction sector is considered an important and influential pivot in the national economy of any country.  Nations are working to develop this sector, receiving modern and developed techniques. So, this sector can be a carrier or a receiver of modern technologies. The cost of technology transfer between the international companies that sponsor this sector is a matter of great importance, especially since different factors affect the need for this advanced technology. The cost of technology transfer in construction is related to multiple factors presented by Knowledge, equipment, plant, hardware and software. The lack of distinguishing and evaluating the direct and indirect costs in the construction sector during

Acontaminated ophthalmic solutions represent a potential cause of avoidable ocular infection. This study aimed to determine the magnitude and pattern of microbial contamination of eye drops in out patient at the department of ophthalmology, at Baghdad national hospital, Iraq. Fifty four vials from the out patient clinic were obtained for microbial examination after an average use of 2 weeks. The dropper tip and the residual eye drop were examined for contamination. The specimens were cultured, the number of colonies counted, the organisms identified. Eight (15%)  out of 54 analyzed vials were contaminated , most bacteria identi

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