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

Liquid – liquid interface reaction is the method for
preparation nanoparticles (NP'S) which depend on the super
saturation of ions that provide by using the system that consist from
toluene and water, the first one is above the second to obtain
nanoparticles (NP's) CdS at the interface separated between these
two immiscible liquid. The structure properties were characterized by
XRD-diffraction and transmission electron microscopy.
The crystalline size estimate from X-ray diffraction pattern
using Scherer equation to be about 7nm,and by TEM analysis give us
that ananosize is about 5 nm which give a strong comparable with
Bohr radius. Photoluminescence analysis give two emission peak,
the first one around

Oxidative stress markers are of important diagnostic parameters for many disorders including cholelithiasis. This present study has aimed to assess the state of oxidative stress in symptomatic radiographically confirmed (Cholelithiasis) patients by measuring two parameters used as oxidative stress parameters which are serum myeloperoxidase (MPO) and superoxide dismutase (SOD). This study was carried out on 100 patient diagnosed as (Cholelithiasis) patients with 30 age and sex matched healthy controls by measuring serum (MPO) and (SOD) by ELIZA technique .Results showed significantly decrease in antioxidant enzyme(SOD) and increase in serum level of (MPO) comparing with controls.

Keywords: Cholelithiasis , Oxidative stress

   A new nano-sized NiMo/TiO₂-γ-Al₂O₃ was prepared as a Hydrodesulphurization catalyst for Iraqi gas oil with sulfur content of 8980 ppm, supplied from Al-Dura Refinery. Sol-gel method was used to prepare TiO₂- γ-Al₂O₃ nano catalyst support with 64% TiO_2, 32% Al₂O₃, Ni-Mo/TiO-γ-Al₂O₃ catalyst was prepared under vacuum impregnation conditions to loading metals with percentage 3.8 wt.% and 14 wt.% for nickel and molybdenum respectively while the percentage for alumina, and titanium became 21.7, and 58.61 respectively. The synthesized TiO₂- γ-Al₂O₃ nanocomposites and Ni-Mo /TiO₂

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

 Glay pots experiments were carried out in the botanical garden of Biology Department/  College of Education for Pure Science Ibn AL-Haitham / Baghdad University for the growing season 2014-2015 , to evaluate the effect of foliar spraying of hydrogen peroxide ( H2O2) and glutamic acid and their interaction on some growth parameters and yield components of black cumin plant . The experiment included the following factors :- 1- Four concentrations of hydrogen peroxide (0 , 5 , 10 , 20 ) mM.L-1 . 2- Three concentrations of glutamic acid ( 0 , 50 , 100 ) mg.L-1 .      The experiment was designed according to completely randomized design (CRD) with three replications ,        Results revealed that

It is becoming a public health issue to predict which expectant women will develop gestational diabetes mellitus (GDM). The goal of this case control research is to investigate the role of maternal oxidative stress levels in the first, second, and third trimesters, as well as other factors, in the development of gestational diabetes mellitus (GDM). Methods Between October and December 2021, 142 women participated in this research. The 101 GDM patients were split into three groups based on their gestation (T1, T2, and T3), and 41 healthy pregnant women were chosen as the comparison group. TAS and TOS levels of oxidative stress and XO were calculated using a Spectrophotometer for colorimetric techniques; fasting and random sugar levels, as

The present work is devoted to investigate the performance of a homemade Y-shape catalytic microreactor for degradation of dibenzothiophene (DBT), as a model of sulphur compounds including in gas oil, utilizing solar incident energy. The microchannel was coated with TiO2 nanoparticles which were used as a photocatalyst. Performance of the microreactor was investigated using different conditions (e.g., DBT concentration, LHSV, operating temperature, and (H₂O₂/DBT) ratio). Our experiments show that, in the absence of UV light, no reaction takes place. The results revealed that outlet concentration of DBT decreases as the mean residence time in the microreactor increases. Also, it was noted that operating temperature s

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a