With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

Parasitic diseases can affect infection with COVID-19 obviously, as protective agents, or by reducing severity of this viral infection. This current review mentions the common symptoms between human parasites and symptoms of COVID-19, and explains the mechanism actions of parasites, which may prevent or reduce severity of this viral infection. Pre-existing parasitic infections provide prohibition against pathogenicity of COVID-19, by altering the balance of gut microbiota that can vary the immune response to this virus infection.  

ABSTRACT

Background: Al-Najaf province , Iraq , has experienced an outbreak of coronavirus disease 2019 (Covid-19). Epidemiological and clinical characteristics of (Covid-19) infection have been reported but a detailed clinical course and risk factors for mortality including medical comorbidities and severity of illness at time of presentation , have not been well described.

Methods: From February 24 to April 7, 2020, a case series study done on 123 PCR-confirmed cases of (Covid-19) admitted to Al-Hakeem Hospital And Quarantine Center (AHQC), in Al-Najaf Province, Iraq. Demographics, clinical and laboratory data  gathered from a local database at (AHQC). SPSS(statist

Background: The COVID-19 pandemic has had effects beyond the respiratory system, impacting health and quality of life. Stress-related to the pandemic has led to temporary menstrual pattern changes in around one-third of women. These changes, likely driven by stress and anxiety, can result in problematic heavy bleeding, causing anemia and negatively affecting women's well-being. This also places a substantial socioeconomic burden on individuals, families, healthcare, and society.

Objectives: This study examined the impact of COVID-19 infection on the hormone levels (estradiol, prolactin, follicle-stimulating hormone, and luteinizing hormone) and heavy menstrual bleeding in Iraqi premenopausal women

Covid-19 is a respiratory disease similar to pneumonia that results from an infection with SARS-CoV-2, a recently identified virus that became a global pandemic in 2020. The severe cases of the disease show a cytokine storm, which is excessive, uncontrolled production of pro inflammatory cytokines. MicroRNA-155 is an epigenetic microRNA that has the ability to control pro-inflammatory responses in many diseases. We aim to determine the relationship between microRNA-155 expression and some cytokines (interleukin-6, interleukin-8, and interleukin-1β) in severe covid-19 cases. A case-control study of 235 samples was collected from 120 patients with severe covid-19 and 115 of mild c

This study aimed to evaluate the preparedness and adherence of community pharmacists to the International Pharmaceutical Federation (FIP) Health Advisory COVID-19 guidelines for pharmacists (July 2020) during COVID-19 pandemic. This was a cross-sectional study based on electronic survey using google form, which was distributed from November 19, 2020 to January 1, 2021 using social media platforms. The survey measured 21 pharmacy preventive measures (PM). A multivariate regression analysis was used to identify factors influencing pharmacy implementing of PM. Hand disinfection after serving patients represented the main adopted measure (89.3%). Surprisingly, only 35.4% of participants implemented the proper ways of hand disinfection during fa

     Widespread COVID-19 infections have sparked global attempts to contain the virus and eradicate it. Most researchers utilize machine learning (ML) algorithms to predict this virus. However, researchers face challenges, such as selecting the appropriate parameters and the best algorithm to achieve an accurate prediction. Therefore, an expert data scientist is needed. To overcome the need for data scientists and because some researchers have limited professionalism in data analysis, this study concerns developing a COVID-19 detection system using automated ML (AutoML) tools to detect infected patients. A blood test dataset that has 111 variables and 5644 cases was used. The model is built with three experiments using Python's Auto-

Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.

BACKGROUND: SARS-CoV-2 (COVID-19) is considered a highly infectious and life threatening disease. OBJECTIVE: The present paper aims to evaluate various aspects of preventive measures and clinical management of the scheduled visits for orthodontic patients to the dental clinics during the outbreak of COVID-19, and to assess how orthodontists dealt with this challenge. METHODS: Orthodontists in private and public clinics were invited to fill a questionnaire that addressed infection control protocols and concerns about clinical management of patients in the clinics during the pandemic. Frequncies and percentages of the responses were obtained and compared using Chi-square tests. RESULTS: About 77% of those working in private clinics, a