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

Background: Polymethylmethacrylate (PMMA) is the most ‎commonly used m‏aterial in denture construction. This material is ‎far from ideal in fulfilling the‎ mechanical requirements, like low impact and transverse strength and poor thermal conductivity are present in this material. The purpose of this study was to study the effect of addition a composite which include 1%wt silanized silicone dioxide nano fillers (SiO2) and 1wt% oxygen plasma treated polypropylene fiber (PP) on some properties of heat cured acrylic resin denture base material (PMMA). Materials and methods: One hund‏red (100) prepared specimens were divided into five groups according to the tests, each group consisted of 20 specimens and t

Microbial antibiotics resistance is considered a serious health issue in the Middle East and developing countries. In this study, the Fe₂O₃ nanoparticles was prepared chemically, and the particles size and shape were analyzed by using Scan electron microscope (SEM) and X-Ray diffraction (XRD). Different concentration of Fe₂O₃ nanoparticles were used and examined on E.coli and S. aureus. Using liquid dilution and in vitro cytotoxicity assay by microplate toxicity test (MTT). The microbial cell metabolic activity was measured on gram-negative, gram-positive bacteria and fungi after treating with different concentrations of Fe₂O₃ nanoparticl

Background: Beta thalassemia major (β-TM) is an inheritable condition with many complications, especially in children. The blood-borne viral infection was proposed as a risk factor due to the recurrent blood transfusion regimen (hemotherapy) as human parvovirus B19 (B19V). Objective: This study investigated the B19V seroprevalence, DNA presence, B19V viral load, and B19V genotypes in β-TM patients and blood donors. Methods: This is a cross-sectional study incorporating 180 subjects, segregated into three distinct groups each of 60 patients, namely control, β-TM, and β-TM infected with Hepatitis C Virus (HCV).  For the B19V prevalence in the studied group, the ELISA technique and real-time PCR were used. The genotyping was follo

The preparation and characterization of innovative nanocomposites based on zinc oxide nanorods (ZNR) encapsulated by graphene (Gr) nanosheets and decorated with silver (Ag), and cupper (Cu) nanoparticles (NP) were studied. The prepared nanocomposites (ZNR@Gr/Cu-Ag) were examined by different techniques including Field Emission Scanning Electron Microscope (FESEM), Transmission electron microscopy (TEM), Atomic force microscopy (AFM), UV-Vis spectrophotometer and fluorescence spectroscopy. The results showed that the ZNR has been good cover by five layers of graphene and decorated with Ag and Cu NPs with particles size of about 10-15 nm. The ZNR@Gr/Cu-Ag nanocomposites exhibit high absorption behavior in ultraviolet (UV) region of sp

This researchpaper includes the incorporation of Alliin at various energy levels and angles 

With Metformin using Gaussian 09 and Gaussian view 06. Two computers were used in this work. Samples were generated to draw, integrate, simulate and measure the value of the potential energy surface by means of which the lowest energy value was (-1227.408au). The best correlation compound was achieved between Alliin and Metformin through the low energy values where the best place for metformin to b