Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed

Relationship between platelet count and platelet width distribution and serum uric acid 1 concentrations in patients with untreated essential hypertension

Hematological parameters have emerged as independent determinants of high serum concentrations of uric-acid and predictive-factors in the evaluation of the total cardiovascular-risk in patients with essential-hypertensive. Here we have investigated the possible relationships between hematological-factors and serum uric-acid levels in hypertensive-patients recruited as part of Mashhad-Stroke and Heart-Atherosclerotic-Disorders cohort study. Two-thousand three-hundred and thirty four hypertensive individuals were recruited from this cohort and these were divided into two groups; those with either high or low serum uric acid concentrations. Demographic, biochemical and hematological characteristics of population were evaluated in all the subjects. Logistic-regression-analysis was performed to determine the association of hematological-parameters with hypertension. Of the 2334 hypertensive-subjects, 290 cases had low uric-acid, and 2044 had high serum uric acid concentrations. Compared with the low uric acid group, the patients with high serum uric acid, had higher values for several hematological parameters, whilst platelet counts (PLT) were lower. Multiple linear regression analysis showed that PLT and serum hs-CRP were correlated with serum uric acid level. Stepwise multiple logistic regression model confirmed that PDW and gender were independent determinant of a high serum uric acid. PDW and PLT appear to be independently associated with serum uric acid level in patients with hypertension

Exploring the perception of aid organizations’ staff about factors affecting management of mass casualty traffic incidents in Iran: a grounded theory study

Background: Traffic incidents are of main health issues all around the world and cause countless deaths, heavy casualties, and considerable tangible and intangible damage. In this regard, mass casualty traffic incidents are worthy of special attention as, in addition to all losses and damage, they create challenges in the way of providing health services to the victims. Aim: The present study is an attempt to explore the challenges and facilitators in management of mass casualty traffic incidents in Iran. Methods: This qualitative grounded theory study was carried out with participation of 14 purposively selected experienced managers, paramedics and staff of aid organizations in different provinces of Iran in 2016. Semi- structured interviews were conducted in order to develop the theory. The transcribed interviews were analyzed through open, axial and selective coding. Results: Despite the recent and relatively good improvements in facilities and management procedure of mass casualty traffic incidents in Iran, several problems such as lack of coordination, lack of centralized and integrated command system, large number of organizations participating in operations, duplicate attempts and parallel operations carried out by different organizations, intervention of lay people, and cultural factors halt provision of effective health services to the victims. Conclusion: It is necessary to improve the theoretical and practical knowledge of the relief personnel and paramedics, provide public with education about first aid and improve driving culture, prohibit laypeople from intervening in aid operations, and increase quality and quantity of aid facilities