Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

Worldwide, hundreds of millions of people have been infected with COVID-19 since December 2019; however, about 20% or less developed severe symptoms. The main aim of the current study was to  assess  the  relationship  between  the  severity of Covid-19 and different clinical and laboratory parameters. A total number of 466 Arabs have willingly joined this prospective cohort. Out of the total number, 297 subjects (63.7%) had negative COVID-19 tests, and thus, they were recruited as controls, while 169 subjects (36.3%) who tested positive for COVID-19 were enrolled as cases. Out of the total number of COVID-19 patients, 127 (75.15%) presented with mild symptoms, and 42 (24.85%) had severe symptoms. The age range for the partic

We examine 10 hypothetical patients suffering from some of the symptoms of COVID 19 (modified) using topological concepts on topological spaces created from equality and similarity interactions and our information system. This is determined by the degree of accuracy obtained by weighing the value of the lower and upper figures. In practice, this approach has become clearer.

Background: Age progression is regarded as a critical risk factor in morbidity and mortality because of a weakened immune system. Although various studies have dealt with electrolyte imbalance in COVID-19 patients, the outcomes of these studies were partially understood. Objective: The current study aims to determine some biochemical parameters in old Iraqi COVID-19 patients and highlight the outcomes according to the aging role in the development of COVID-19 by suggesting new mechanisms. Materials and methods: forty COVID-19 patients were enrolled in the current study and divided into two groups: Gm includes (20) men, and Gf includes  (20) women. The parameters (Na+, K+, Cl-, LDH, and Hb ) were determined in sera of patients and c

Thrombosis is a common clinical feature associated with morbidity and mortality in coronavirus disease-2019 (COVID-19) patients. Cytokine storm in COVID-19 increases patients' systemic inflammation, which can cause multiple health consequences. In this work, we aimed to indicate the effect of Pfizer-BioNTech vaccination on the modulation of monocyte chemoattractant protein-3 (MCP-3), matrix metalloproteinase 1 (MMP-1), and tumor necrosis factor-alpha (TNF-α) levels, and other systemic inflammatory biomarkers that associates with COVID-19 severity in patients who suffers from thrombosis consequences. For this purpose, ninety people were collected from Ibn Al-Nafees Hospital and divided into three groups each of which contained 30 people, 15

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121