Y Adnan, H Atiyah, IH Neamah…, International Development Planning Review, 2024

To study the qualitative changes in testis tissue after carbon tetrachloride (CCl4) administration and to determine whether citric acid (CA) has a protective effect against testis damage induced by CCl4. This study compared two types of CA by measuring the histoarchitecture of the testis and serum levels of progesterone, estrogen and testosterone on mice. One of the most produced organic acid is citric acid. In this study, CA produced by microbial fermentation using Aspergillus Niger 5mg/kg and derived from citrus limon 400mg/kg (lemon). Mice were treated with daily intraperitoneal (i.p.) injection for seven successive days after randomly separated into six groups: (1) control, (2) CCl4 (0.02%), (3) limon citric acid (400 mg/kg), (4) CCl4 (

Polish Academy of Sciences

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