|
|
|
 |
Search published articles |
 |
|
Showing 4 results for Sars-Cov-2
J.s. Welsh, J.j. Bevelacqua, H. Mozdarani, S.a.r. Mortazavi, Ph.d., S.m.j. Mortazavi,
Volume 18, Issue 3 (7-2020)
Abstract
Astronauts are exposed to a wide variety of stressors ranging from radiation and microgravity to persistent fluids shifts, circadian shifts and the psychological stress of prolonged isolation and confinement. On deep space missions, beyond the range of the Earth's magnetosphere, ionizing radiation may increase oxidative stress and DNA damage, immune system dysregulation and alter the effectiveness of the cellular defense mechanisms. By reviewing the health problems reported by astronauts participated in previous space missions, it is evident that viral infections are not rare in space. Recent reports suggest that COVID-19 can last for a long time in communities. Although NASA implements countermeasures designed to limit crew illness during space missions such as a pre-flight quarantine, it is not clear whether an outbreak can be prevented. Currently, it is not likely that astronauts could get a viral infection, but the consequences of potential life-threatening viral diseases such as COVID-19 should be better characterized. In this paper we discuss why COVID-19 fatality in space might be significantly higher than on the Earth. The reasons for such an increased risk include 1) uselessness of social distancing due to microgravity 2) immune system dysregulation 3) possibly higher mutation rates of RNA viruses such as the novel coronavirus (SARS-CoV-2) 4) existence of strong selective pressure and 5) decreased maximum oxygen uptake. Given these considerations, the combined effects of microgravity, space radiation (and possibly other major space stressors) on the immune system of astronauts exposed to SARS-CoV-2 and possible interactions of the virus, space stressors and host should be carefully investigated.
Phd., H. Romdhane, H. Dziri, M. Ali Cherni, D. Ben-Sellem,
Volume 19, Issue 2 (4-2021)
Abstract
Background: A new coronavirus appeared in late December 2019 in Wuhan, China. He was named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus is responsible for Covid-19, the name given to the disease associated with it. It spreads worldwide, infecting more than a million people and killing more than 70 miles. The rapid and accurate diagnosis of suspected Covid-19 cases plays a crucial role in medical treatment and timely quarantine. Materials and Methods: In order to counter the Covid-19 pandemic, we have developed a method for the automatic detection of Covid-19, from 2D computed tomography (CT) chest images. It is a supervised software system based on the ANN (Artificial Neural Network) algorithm. Pulmonary CT images were collected from multiple international datasets, with a total of 395 images: 70% were used for training and 30% were used for testing. For each patient, the lungs were segmented using simple thresholding. Then, the segmented lungs were fed into a neural network to predict the probability of SARS-CoV-2 infectious. Results: The internal validation achieved a total accuracy of 97.5% with a specificity of 96.6 % and a 100 % sensitivity. Conclusion: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate Covid-19 diagnosis.
M.d., A. Mert, A. Kaptanoglu,
Volume 20, Issue 2 (4-2022)
Abstract
Background: Coronavirus disease 2019 (COVID-19) is a pandemic and increasing rapidly as a global health emergency which is caused by SARS-CoV-2 virus. The real-time reverse transcription polymerase chain reaction test (RT-PCR test) is considered as a gold standard method for diagnosing (COVID-19). However, the test has some limitations as it is not universally available, turnaround times can be lengthy, and reported sensitivities vary The aim of the study was to describe the imaging features of thorax computed tomography (CT) in diagnosing COVID-19. Materials and Methods: In this retrospective study, 36 patients were recruited from the emergency room and outpatient settings at a tertiary-care hospital, Istanbul, Turkey. Thorax CT was done and correlated with clinical findings to diagnose COVID-19. Results: A total of 36 of 41 patients were screened for thorax CT and were confirmed as COVID-19. There was no significant difference (p>0.05) between the age-distribution and gender for clinical and radiological findings. Conclusions: Thorax CT with clinical findings should be preferred as early diagnosis of COVID-19 and follow-up period.
Ph.d W. Li, Y. Zhou,
Volume 21, Issue 2 (4-2023)
Abstract
Background: This study aimed to review computed tomography (CT) findings in COVID-19 patients, and establish correlations between CT findings in patients with a short vs. long disease course, and in those with mild vs. severe disease. Materials and Methods: From February 2020 to March 2020, 58 patients with SARS-CoV-2 infections were retrospectively included. Clinical, laboratory, and CT findings were compared between patients with a short vs. long disease course, and in subgroups with mild vs. severe disease. Correlation analyses were performed to determine factors correlated to greater disease severity in patients with short/long disease courses, respectively. Results: Fifty-eight patients were included; 29 in the short disease course and 29 in the long disease course group. CT findings were similar between patients with a short and a long disease course (all, P > 0.05). Among the short disease course group, severe disease patients had significantly higher rates of right upper lobe involvement, 5 lobes affected, pericardial effusion, pleural involvement and bilateral pleural thickening, grid shadow, higher-density vascular shadows, crazy-paving appearance, lung consolidation, an air bronchogram sign, and fibrous foci than those with mild disease. Among the long disease course group, severe disease patients had significantly higher rates of right upper lobe and middle lobe involvement, 5 lobes affected, pleural effusion and thickening, grid shadow, higher-density vascular shadows, crazy-paving appearance, lung consolidation, an air bronchogram sign, and atelectasis. Conclusions: CT imaging findings may help to predict disease severity in COVID-19.
|
|
