Background: The altered status of some essential trace elements observed in diabetes could have deleterious influences on the health of the diabetics. Objectives: To estimate and study the potential role of serum Selenium in type 1, type 2 diabetics and healthy subjects; and its relation with lipid profile and glycemic index. Methods: A case control designed study was carried out at the National Diabetes Center (NDC) / Al-Mustansiria University; on a total of 94 participants formed of 32 type 1 diabetics, 32 type 2 diabetics and 30 healthy control participants. Data collected about age, sex and BMI; also, blood samples examined for FPG, HbA1C, serum total cholesterol, HDL cholesterol, non-HDL cholesterol, serum triglyceride and sera were

Aim: To learn more about Oral Lichen Planus Iraqi patients, including their background information, symptoms, and prognosis. Materials and Methods: From the Oral and Maxillofacial Pathology Department, College of Dentistry, Baghdad University, we retrospectively reviewed the medical records of 68 patients with a histologically confirmed clinical diagnosis of oral lichen planus and subsequently contacted the patients by phone to evaluate their prognosis. Results: Females were more likely than males to experience severe pain; the reticular form of Oral Lichen Planus was the most prevalent at 38.2%, but the erosive type was more prevalent among females. Only 53 of 68 patients responded to phone calls. More than 37% of those respondents reporte

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

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class