The dynamic nature of coronavirus-19 (Covid-19) has caused a wreaked havoc globally, with millions of confirmed cases and deaths. Therefore, it is important to understand the psychological impact of the Covid-19 on the patients. In the present study, we examine whether intolerance of uncertainty was related to the severity of symptoms and whether this relationship is mediated by perception of illness and covid-19 fear. The study sample comprised of 98 Covid-19 patients (Mean = 35.17 SD = 12.89). Mediation analysis was conducted using the PROCESS macro for SPSS. Results of mediation analysis showed that the direct effect of intolerance of uncertainty on symptom severity was insignificant. However, the indirect effect via illness perception was significant, reflecting full mediation. The findings add knowledge to our understanding of the psychological consequences of Covid-19. The present study has implications for mental health services for patients with Covid-19, which will play a vital role in recovery from the illness.

Aim: The purpose of our study was to assess the presentation of COVID-19 disease in terms of clinical and radiological features in our population. Methods: 64 RT-PCR documented COVID-19 patients were included in the study. Clinical, biochemical, and radiological data were collected and analyzed retrospectively from last week of March to 30 th April 2020. Results: Out of the 64 patients, 38 (59.4%) were males, 44 (68.7%) had a history of contact with COVID-19 positive patient. 26.6%patients were in the age group of 21–30 years. 53.1% patients were asymptomatic while as cough and fever were the most common symptoms in 21.8 and 20.3% patients, respectively. Anosmia was present in four patients. Hypertension and hypothyroidism were the most common comorbid illnesses among the study population in 9.4% patients each. Lymphopenia was present in 38% of patients CRP was increased in 83% patients, LDH in 90.2%, and ferritin in 51.5% of patients. 17 (26.6%) patients had bilateral disease in CT. RUL was the most common lobe involved in 18 (28.1%) patients. GGO and consolidation were seen in 22 (34.45) and 13 (20.3%) patients, respectively. Vessel enlargement was observed in 11 (17.2%) patients. All five lobes were involved in 9 (14.1%) patients. Five patients developed severe disease with respiratory comprise; two of them eventually died. Conclusion: The clinical and radiological characteristics of COVID-19 patients vary among different populations. Although there are no radiological features which seems to be characteristic of COVID-19, but CT helps in evaluation of the patients as many asymptomatic ones have some radiological findings suggestive of viral pneumonia.

Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.