Denture cleansing is an essential step that can stop cross‑contamination and adds to the health of the patient, denture durability, and the general quality of life. A disinfection technique must be practical and devoid of damaging effects on the material's properties used to construct the denture base. The main aim of this study is to evaluate the effect of three concentrations of electrolyzed water denture cleanser on heat cure acrylic and polyamide after immersion in electrolyzed water. The evaluation is based on their efficacy on surface hardness, wettability, and color stability compared with one submerged in distilled water as a control group. The method consists of eighty samples of heat-cured acrylic and polyamide material.

Background: Asymmetry assessment is an important component of orthodontic diagnosis and treatment planning. Several studies attempted to find the relationship between craniometric asymmetry and skeletal jaws relationship and many authors found some extent of asymmetry in individuals with normal jaws relationship. The use of Computed tomography (CT) allows for the assessment of asymmetry on a dimensionally accurate volumetric image, aim of the study is to determine if there are differences in craniometric asymmetry between patient with skeletal class I and patients with skeletal class II relationship using Helical CT scan. Materials and Methods: Ninety individuals with clinically symmetrical faces were imaged with Helical CT scan, and aging

Bacteriophage of E. Coli interspecies from sewage samples were isolated , the phage particles were isolated from two different sewage samples . The first sample was collected from sewage sample of Baghdad university and the second sample was isolated from domestic sewage sample , first sample showed phages specialized for three E. Coli interspecies bacteria (first plate ) and two E. Coli interspecies bacteria (second plate ) , meanwhile second sample showed phage specialized for two E. Coli. interspeciesThe study of appearance of E coli phages from first sample showed three types of E. coli phages with different size of inhibition zone ( 1 , 0.7,0.5 )Cm respectively ( first plate ) , meanwhile E. Coli interspecies bacteria showed phages

Biosorption of lead, chromium, and cadmium ions from aqueous solution by dead anaerobic biomass (DAB) was studied in single, binary, and ternary systems with initial concentration of 50 mg/l. The metal-DAB affinity was the same for all systems. The main biosorption mechanisms were complexation and physical adsorption of metallic cations onto natural active functional groups on the cell wall matrix of the DAB. It was found that biosorption of the metallic cations onto DAB cell wall component was a surface process. The main functional groups involved in the metallic cation biosorption were apparently carboxyl, amino, hydroxyle, sulfhydryl, and sulfonate. These groups were part of the DAB cell wall structural polymers. Hydroxyle groups (–O

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de