Machine Learning Models Towards Prediction of COVID and Non-COVID 19 Patients in the Hospital's Intensive Care Units (ICU)

The global proliferation of COVID-19 prompted research towards the virus's detection and eventual eradication. One important area of research is the use of machine learning (ML) to realize and battle COVID-19. The goal of this study is to use machine learning to monitor COVID and non-COVID-19 patients and decide whether or not to transfer them to the intensive care unit (ICU). The precise disease diagnosis was essential due to the lack of oxygen supplementation in the majority of hospitals around the world. It will improve the effectiveness of the ICU facilities and lessen the load on the medical personnel and the ICU facilities by accurately forecasting how patients will be treated. If stable patients are recognized among all patients, home treatment could be established for stable patients. In this research, three machine learning algorithms were chosen as the method used, which are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Extra Tree Classifier. These algorithms were chosen for their simplicity and robustness and based on the conducted literature review. A dataset containing 100 ICU and 131 stable patients of Covid and non-Covid samples from 24th Moscow City State Hospital was used. By using SMOTE technique with 10-fold cross-validation and feature selection on the dataset, KNN achieved an accuracy of 94.65%, SVM with an accuracy of 94.65%, and an accuracy of 96.18% for the Extra Tree Classifier. The outcomes of this research on the selected dataset prove how accurate these algorithms were able to predict the classes © 2022, Mathematical Modelling of Engineering Problems.All Rights Reserved.

Prevalence and epidemiological trends in mortality due to COVID-19 in Saudi Arabia.

OBJECTIVES: This article describes the prevalence and epidemiological trends of COVID-19 mortality in the largest registry in the Kingdom of Saudi Arabia (KSA). STUDY DESIGN: A prospective epidemiological cohort study using data from all healthcare facilities in KSA collected between March 23, 2020, and April 30, 2022. Data on the number of daily deaths directly related to COVID-19 were gathered, analyzed, and reported. METHOD: Data analysis was carried out using national and regional crude case fatality rate and death per 100,000 population. Descriptive statistics using numbers and proportions were used to describe age, gender, nationality, and comorbidities. The mortality trend was plotted and compared with international figures. In addition, the most common comorbidities associated with mortality and the proportion of patients who received COVID-19 vaccine were reported. RESULTS: The total reported number of deaths between March 23, 2020, and April 30, 2022, was 9085. Crude case fatality rate was 1.21%, and death per 100,000 population was 25.38, which compared favorably to figures reported by several developed countries. The highest percentages of deaths were among individuals aged between 60 and 69 years, males (71%), and individuals with diabetes (60%). Only 2.8% of mortalities occur in patients who received COVID-19 vaccine. Diabetes, hypertension, and heart failure had the highest attributable risk of mortality among patients who died due to COVID-19. CONCLUSION: Case fatality rate and death per 100,000 population in KSA are among the lowest in the world due to multiple factors. Several comorbidities have been identified, namely, diabetes, hypertension, obesity, and cardiac arrhythmias.

Assessment of renal function among covid-19 patients admitted to the intensive care unit

Severe acute respiratory viral infections are frequently associated with multiple organ failure, including acute kidney damage. The present study aimed to investigate the associated influence of COVID-19 on renal function in patients admitted to the intensive care unit in Asir region, Saudi Arabia. Thirty patients infected with COVID-19 who were referred to the intensive care unit during November and October 2020 at Asir central hospital, Asir region, Saudi Arabia were recruited. The age of patients ranged between 30 and 90 years old. Renal function tests exhibited dramatic changes in the renal biomarkers in patients with COVID-19. Blood urea levels in COVID-19 patients were significantly higher than in the control group. In addition, significantly lower albumin levels with abnormally decreased total protein levels were found in COVID-19 patients. Among the different electrolytes analyzed, a significantly lower calcium level was observed in COVID-19 patients' groups than in the controls. Renal function tests for COVID-19-infected ICU patients revealed significant changes, indicating the major impact of COVID-19 on kidney function. Monitoring renal function tests may assist in the early prognosis of COVID-19 patients. It is, therefore, crucial to increase the understanding of renal function tests in COVID-19 patients who were admitted to the hospital before their condition deteriorated. © 2021 Pakistan Journal of Pharmaceutical Sciences. All rights reserved.

Development of a Colorimetric Tool for SARS-CoV-2 and Other Respiratory Viruses Detection Using Sialic Acid Fabricated Gold Nanoparticles

Sialic acid that presents on the surface of lung epithelial cells is considered as one of the main binding targets for many respiratory viruses, including influenza and the current coronavirus (SARS-CoV-2) through the viral surface protein hemagglutinin. Gold nanoparticles (Au NPs) are extensively used in the diagnostic field owing to a phenomenon known as 'surface plasmonic resonance' in which the scattered light is absorbed by these NPs and can be detected via UV-Vis spectrophotometry. Consequently, sialic acid conjugated Au NPs (SA-Au NPs) were utilized for their plasmonic effect against SARS-CoV-2, influenza B virus, and Middle-East respiratory syndrome-related coronavirus (MERS) in patients' swab samples. The SA-Au NPs system was prepared by a one-pot synthesis method, through which the NPs solution color changed from pale yellow to dark red wine color, indicting its successful preparation. In addition, the SA-Au NPs had an average particle size of 30 +/- 1 nm, negative zeta potential (-30 +/- 0.3 mV), and a UV absorbance of 525 nm. These NPs have proven their ability to change the color of the NPs solutions and patients' swabs that contain SARS-CoV-2, influenza B, and MERS viruses, suggesting a rapid and straightforward detection tool that would reduce the spread of these viral infections and accelerate the therapeutic intervention.