The liver is the primary organ for drug metabolism, elimination, Cyclophosphamid is the classical alkylating agent nitrogen mustard, its metabolism into two cytotoxic metabolites, and increase reactive oxygen species that is make liver toxicity. Safranal as the most abundant chemical in saffron essential oil, it have anti-oxidant, anti-inflammatory, antiapoptic and free radical scavenger activity. The aim of study is to assess the protective effects of safranal on the cyclophosphamide-induce liver toxicity in rat model. This occur by using five different groups of rats; control group, treatment group, cyclophosamide group (intraperitoneal i.p), cyclophosamide and (50mg and 100mg) oral safranal treatment groups. This study showed this pro

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

Background: One of the significant public health problems is the traumatic dental injury to the anterior teeth, it has a great impact on children’s daily. Physical and psychological disturbance, pain and other negative impacts, such as tendency to avoid laughing or smiling may be associated with traumatic dental injuries, that may affect the social relationships. To determine the occurrence of traumatic dental injuries in relation to quality of life, this study was established among children of primary schools. Material and Methods: A cross-sectional study was conducted among private (574) and governmental (1026) primary school children in Baghdad city. Dental trauma was assessed according to Ellis and Davey classification in1970

Polyethersulfone (PES) ultrafiltration membrane blending NaX zeolite crystals as a hydrophilic additive was examined for zinc (II) and lead ions Pb (II) removal from aqueous solutions. The effect of NaX zeolite content on the permeation flux and removal efficiency was studied. The results showed that adding zeolite to the polymer matrix enhanced the permeation flux. The permeation flux of all the zeolite/PES matrix membranes was higher than the pristine membrane. No significant improvement was observed in the removal of Zn (II) ions using all prepared membranes as the removal percentage did not raise above 29.2%. However, the removal percentage of Pb (II) ions was enhanced to 97% using a membrane containing 0.9%wt. zeolite. Also, it was

Background: Nanotechnology represents a new science that promises to provide a broad range of uses and improved technologies for biological and biomedical applications. One of the reasons behind the intense interest is that nanotechnology permits synthesis of materials that have structure is less than 100 nanometers. The present work revealed the effect of zinc oxide nanoparticles (ZnO NPs) on Streptococcus mutans of Human Saliva in comparison to de-ionized water. Materials and methods: Streptococcus mutans were isolated from saliva of forty eight volunteers of both sexes their age range between 18-22 years and then purified and diagnosed according to morphological characteristic and biochemical tests. Different concentrations of ZnO NPs w

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod