Background: Acute radiodermatitis is a common side effect during and after radiotherapy course in breast cancer patients treated by radiotherapy. This study assess the frequency of acute radiodermatitis and record the predictive factors for acute radiodermatitis. Patients and Methods: A descriptive case series study conducted at Baghdad, Iraq from August 2020 to September 2021. 70 female scheduled for radiotherapy sessions enrolled in this study. sociodemographic data were recorded and Skin examination before radiotherapy and weekly till the end of the radiotherapy sessions was done to report the frequency, risk factors, clinical picture and grades of acute radiodermatitis based on The National Cancer Institute’s Common Terminology Crite

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Today, the prediction system and survival rate became an important request. A previous paper constructed a scoring system to predict breast cancer mortality at 5 to 10 years by using age, personal history of breast cancer, grade, TNM stage and multicentricity as prognostic factors in Spain population. This paper highlights the improvement of survival prediction by using fuzzy logic, through upgrading the scoring system to make it more accurate and efficient in cases of unknown factors, age groups, and in the way of how to calculate the final score. By using Matlab as a simulator, the result shows a wide variation in the possibility of values for calculating the risk percentage instead of only 16. Additionally, the accuracy will be calculate

Introduction and Aim: Pseudomonas aeruginosa is a nosocomial infection with an ability to develop high levels of antibiotic resistance. The efflux pump system is one of the mechanisms that is linked to multidrug resistance in P. aeruginosa. In this study, we employed siRNA loaded on gold nanoparticles against the MexA efflux pump gene to decrease the MexA gene expression in P. aeruginosa and estimated antibiotic resistance after gene silencing.   Materials and Methods: This study examined four strains of P. aeruginosa isolated from patients in various hospitals in Baghdad. Bacteria isolated were identified by biochemical tests and Vitek compact 2 system.  Single-stranded siRNA (33bp) designed in this study was loaded onto gold

There are many animal models for polycystic ovary (PCO); using exogenous testosterone enanthate is one of the methods of induction of these models. However, induction of insulin resistance should also be studied in the modeling technics. Therefore, the present study aims to investigate the expression of insulin receptor substrate (Irs)-2 mRNA in the liver tissue of rat PCO model. Nineteen Wistar rats were divided into three groups; (1) PCO modeling group (N =7) received daily 1.0 mg/100g testosterone enanthate solved in olive oil along with free access dextrose water 5%, (2) vehicle group (N =6), which handled like the PCO group, but did not receive testosterone enanthate, (3) control group (N =6) with standard care. Al