Association of Comorbidities with Coronavirus Disease 2019: A Review

Role of Genetic Variants and Gene Expression in the Susceptibility and Severity of COVID-19

Since its first report in December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly emerged as a pandemic affecting nearly all countries worldwide. As the COVID-19 pandemic progresses, the need to identify genetic risk factors for susceptibility to this serious illness has emerged. Host genetic factors, along with other risk factors may help determine susceptibility to respiratory tract infections. It is hypothesized that the ACE2 gene, encoding angiotensin-converting enzyme 2 (ACE2), is a genetic risk factor for SARS-CoV-2 infection and is required by the virus to enter cells. Together with ACE2, transmembrane protease serine 2 (TMPRSS2) and dipeptidyl peptidase-4 (DPP4) also play an important role in disease severity. Evaluating the role of genetic variants in determining the direction of respiratory infections will help identify potential drug target candidates for further study in COVID-19 patients. We have summarized the latest reports demonstrating that ACE2 variants, their expression, and epigenetic factors may influence an individual's susceptibility to SARS-CoV-2 infection and disease outcome.

Review on COVID-19 Etiopathogenesis, Clinical Presentation and Treatment Available with Emphasis on ACE2

In December 2019, Wuhan city in the Hubei province of China reported for the first time a cluster of patients infected with a novel coronavirus, since then there has been an outburst of this disease across the globe affecting millions of human inhabitants. Severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2), is a member of beta coronavirus family which upon exposure caused a highly infectious disease called novel coronavirus disease-2019 (COVID-19). COVID-19, a probably bat originated disease was declared by World Health Organization (WHO) as a global pandemic in March 2020. Since then, despite rigorous global containment and quarantine efforts, the disease has affected nearly 56,261,952 laboratory confirmed human population and caused deaths of over 1,349,506 lives worldwide. Virus passes in majority through respiratory droplets and then enters lung epithelial cells by binding to angiotensin converting enzyme 2 (ACE2) receptor and there it undergoes replication and targeting host cells causing severe pathogenesis. Majority of human population exposed to SARS-CoV-2 having fully functional immune system undergo asymptomatic infection while 5-10% are symptomatic and only 1-2% are critically affected and requires ventilation support. Older people or people with co-morbidities are severely affected by COVID-19. These categories of patients also display cytokine storm due to dysfunctional immune response which brutally destroys the affected organs and may lead to death in some. Real time PCR is still considered as standard method of diagnosis along with other serology, radiological and biochemical investigations. Till date, no specific validated medication is available for the treatment of COVID-19 patients. Thus, this review provides detailed knowledge about the different landscapes of disease incidence, etiopathogenesis, involvement of various organs, diagnostic criteria's and treatment guidelines followed for management of COVID-19 infection since its inception. In conclusion, extensive research to recognize novel pathways and their cross talk to combat this virus in precarious settings is our future positive hope.

Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches

To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets.