The recent developments of m-health technologies particularly in the developing world are increasing sharply due to the importance and accelerated adoption of these technologies in the developing countries. However, there are few if any studies on the effectiveness of mobile health in post conflict regions especially in the Middle East region. In this paper we describe the design, implementation and clinical outcomes of a feasibility study on mobile diabetes management in Basra, Southern Iraq as an exemplar for the effectiveness of mobile health technologies for improved healthcare delivery in similar post conflict regions. The key clinical outcome of this study indicated the lowering of HbA1C levels in the mobile health group indicating the potential of deploying such technologies in these regions where health resources are limited and challenging.

Coronavirus (COVID-19) pandemic detection considers a critical and challenging task for the doctors. The coronavirus disease spread so rapidly between people and infected roughly fourteen million people worldwide. For this reason, it is very much necessary to detect infected people with coronavirus and take the action to prevent of spread this virus. In this study, the COVID-19 classification methodology is adopted to detect the infected patient with coronavirus using CT images. The deep learning is applied to recognize the affected CT images of COVID-19 from others by employing the deep feature. This methodology can be beneficial for the medical practitioner to diagnosis the infected patient with coronavirus. The result is based on new data collections named BasrahDataset that included different CT scan video for Iraqi patients. The system gives promised results with 99% F1-score for detecting COVID-19.

A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.

Background: Diabetes and hypertension have been identified as risk factors for HCV complications in previous studies. This has sparked the interest in the field of prevention by identifying at-risk individuals and increasing investments for screening among pharmacists. The aim of this study was to screen for risk factors, including age, gender, BMI, hypertension, diabetes, and obesity, in Egyptian patients with HCV. Methods: A prospective cross-sectional study was carried out from September 2018 to February 2019, with a total of 1,959 medical records collected. By comparing the patients’ characteristics, variables related to metabolic risk, and body composition measurements, regression models have been established to determine any confounding factors. Results: The prevalence of HCV antibody was 41.0% in men and 59.0% in women. Among the variables included in the regression analysis, age, BMI, and uncontrolled hypertension were found to have statistically significant associations with diabetes in HCV positive cases (p < 0.001). HCV patients ? 40 years old with high BMI were found to have significant associations with both, diabetes and hypertension (p < 0.001). Hypertensive HCV patients were found to have significant associations with gender, age ? 40, and DM (p < 0.001). Conclusion: HCV infection and metabolic disorders have a closed cycle relationship. Reducing the complications of DM has a promising prospective of limiting the complications of HCV.

Background: Percutaneous nephrolithotomy (PCNL) is the first choice for treatment of large renal stone >2 cm. The prone position is the classical position preferred by most surgeons. Aiming to improve patient anesthesia and surgery-related inconveniences of the prone position, Valdivia et al ., 1987, described the performance of PCNL with the patient in the supine position. Hence, we aimed to study the safety and efficacy of flank-free modified supine position in PCNL compared to the standard prone position. Patients and Methods: This is a prospective randomized study for 60 patients with large renal stones planned for PCNL operation during the period from November 2017 to May 2019. Patients were divided into two groups (30 patients each group): Group A – patients underwent PCNL in the prone position and Group B – patients underwent PCNL in the modified flank supine position. Patients’ demographics, stone size, Hounsfield unit with intraoperative details as fluoroscopy time, operative time, and complications were recorded. Postoperatively, need for or not to blood transfusions, hospital stay, stone-free status, and postoperative complications were assessed. Results: There was no statistically significant difference between the prone and supine positions regarding stone size (4 cm vs. 4.5 cm, P = 0.16), Hounsfield unit (940 HU vs. 955 HU, P = 0.78), body mass index (31.2 kg/m 2 vs. 32.5 kg/m 2 , P = 0.49), fluoroscopy time (6.9 min vs. 7.3 min, P = 0.5), operative time (89.5 min vs. 90.4 min, P = 0.9), residual stones (10% vs. 20%, P = 0.8), and hospital stay (45.6 h vs. 48.6 h, P = 0.5). Fever occurred in 3.3% of cases in each group and urine leakage observed in one patient with prone position. No blood transfusion was needed in both the groups. Conclusions: PCNL in the modified supine position proved to be a safe and effective choice compared to the prone position for adult patients with large renal calculi.