In this study, we set up and analyze a cancer growth model that integrates a chemotherapy drug with the impact of vitamins in boosting and strengthening the immune system. The aim of this study is to determine the minimal amount of treatment required to eliminate cancer, which will help to reduce harm to patients. It is assumed that vitamins come from organic foods and beverages. The chemotherapy drug is added to delay and eliminate tumor cell growth and division. To that end, we suggest the tumor-immune model, composed of the interaction of tumor and immune cells, which is composed of two ordinary differential equations. The model’s fundamental mathematical properties, such as positivity, boundedness, and equilibrium existence, are exami

Background Numerous studies indicated that workers in the health sector suffer from work stress, hassles, and mental health problems associated with COVID-19, which negatively affect the completion of their job tasks. These studies pointed out the need to search for mechanisms that enable workers to cope with job stress effectively. Objectives This study investigated psychological flow, mental immunity, and job performance levels among the mental health workforce in Saudi Arabia. It also tried to reveal the psychological flow (PF) and mental immunity (MI) predictability of job performance (JP). Method A correlational survey design was employed. The study sample consisted of 120 mental health care practitioners (therapists, psychologists, co

Abstract Objective: To identify correlation of elevated LDH & CRP levels with the outcomes of COVID-19. Methodology: The cross-sectional retrospective study consisted of 200 COVID-19 patients who presented at a private clinical in Baghdad, Iraq. It was carried out from February 2021 to February 2022. Data included age, gender and clinical presentation. Blood samples were taken for high sensitivity CRP and LDH in the serum. Results: Out of 200 patients, 50 were critical and 150 severe according to clinical features. LDH and CRP showed a significant increase (p=0.000) in critical patients. This group involved admission to the respiratory intensive care unit requiring mechanical ventilation than in patients with severe COVID-19 (760.5±6.3 vs.

Background: Despite the importance of vaccines in preventing COVID-19, the willingness to receive COVID-19 vaccines is lower among RA patients than in the general population. Objective: To determine the extent of COVID-19 knowledge among RA patients and their attitudes and perceptions of COVID-19 vaccines. Methods: A qualitative study with a phenomenology approach was performed through face-to-face, individual-based, semi-structured interviews in the Baghdad Teaching Hospital, Baghdad, Iraq, rheumatology unit. A convenient sample of RA patients using disease-modifying anti-rheumatic drugs was included until the point of saturation. A thematic content analysis approach was used to analyze the obtained data. Results: Twenty-five RA pa

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

Breast cancer (BC) is first of the top 10 malignancies in Iraq. Dose‐volume histograms (DVHs) are most commonly used as a plan evaluation tool. This study aimed to assess DVH statistics using three‐dimensional conformal radiotherapies in BC in an adjuvant setting.

The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow