Effect of Alcoholic Extract from Three Plants that is Rich of Flavonoids on two Virulence Factors in E. coli

In this study, the flavonoid content was estimated by HPLC for Vitis vinifera, zingiber officinale and date palm phoenix dactylifera after alcoholic extracts (Ethanolic extracts) and exposure of Escherichia coli that were isolated in 30 Covid-19 patients. From the medical isolation unit in Salah al-Din Hospital, Tikrit city, Republic of Iraq, bacteria were exposed to plant extracts containing flavonoids at different concentrations and the minimum inhibitory concentrations (MIC) were estimated. Inhibition zones were observed at all concentrations, and then the polymerase chain reaction (PCR) was performed. On two of the most important genes that encode two of the virulence factors in E.coli bacteria, the fimA gene and the PgaD gene, and this procedure showed clear effects on the level of the mentioned genes through PCR technology, which opens the door to the possibility of wider utilization and the development of effective compounds And its exploitation from various plants with a medical reputation known historically "in light of the health difficulties we are going through due to a pandemic Corona virus. © 2022, HIV Nursing. All rights reserved.

THE CHALLENGES OF SUSTAINABLE DEVELOPMENT IN NIGERIA AMIDST THE GLOBAL PANDEMIC (COVID-19)

Background: Sustainable Development is that which meets the needs of the present without undermining the ability of future generations to fulfill their own needs. Unfortunately, the 17 Sustainable Development Goals (SDGs) targets set by the United Nations to be achieved by 2030 have been affected to a large extent by the current COVID-19 Pandemic. The Coronavirus COVID-19 pandemic has so far infected more than 270 million people and killed over 5.3 million. Aside from the direct impact on people's lives and health, the Pandemic's most considerable impact has been by far on Education, the Economy, and Social infrastructures, especially in developing countries. Objective: The work reviews of literature and SDG Reports on the impact of the Pandemics on the Goals relating to the Economy (SDG 1, 2, and 8), Education (SDG 4), and health (SDG 3) in the Nigerian context, using some of the critical indicators of these Goals. Methods: We explore data from the SDG interactive platform to compare the trend and changes in these indicators before and after the Pandemic's start to determine the pandemics' effect on these Goals. In addition, we discussed the global efforts toward vaccine access to bring an end to the current Pandemic. Conclusions: The review suggested that the Pandemic has significantly impacted all indicators targeted in this work. It also suggests some possible solutions, using exemplary sustainable developments that still impact ordinary citizens' economies and livelihoods. Further recommends deploying online teaching-learning methods and other stakeholders' investments in the health and education sectors. © The Author(s), 2022.

Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images

The outbreak of COVID-19 has cost the world a lot of lives and causes the shutdown of businesses which get most of the countries gone into economic recession. Despite the fact that some of the vaccines of the pandemic are now available, immediately after the first wave of the COVID-19 pandemic, the second wave of the pandemic has now started and causes a lot of lives and grounds a lot of businesses that have resumed. Therefore, in order to contain its further spread among humans, testing and screening of a large number of suspected COVID-19 cases for appropriate quarantine and treatment measures are of high priority to all governments around the world. However, most of the countries are facing inadequate and standard laboratories for testing a large number of suspected COVID-19 cases in their countries despite the fact that the virus is now endemic like other communicable diseases. Therefore, alternatives in non-medical diagnosis of COVID-19 techniques using artificial intelligence which include deep learning, data mining, machine learning, expert system, software agent, and other techniques are urgently needed in the cause of the diagnosis, containing and combatting the further spread of the pandemic. In this study, deep learning algorithms were used to develop models for predicting COVID-19 using chest x-ray images, and models were able to extract COVID-19 imagery features and provide clinical diagnosis ahead of the pathogenic test with a view to saving time, thereby complementing COVID-19 testing laboratories. ResNet50-based model was found to have the highest accuracy, sensitivity, and AUC score of 99%, 89%, and 96%, respectively. In contrast, EfficientNet B4-based model was found to have the highest specificity of 89%. Therefore, ResNet50-based model which has the highest sensitivity of 89% can be used for diagnosis of COVID-19 infection as well as an adjuvant tool in radiology department in hospitals. © 2022, Springer Nature Switzerland AG.