ABSTRACT
This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system. © 2022 IEEE.
ABSTRACT
By calculating the centrality measures of the nodes of the SARS-CoV-2 protein interactome network, we have identified the viral proteins of potential greatest interest for further experimental investigation to understand the mechanisms by which SARS-CoV-2 attacks cells and to identify possible therapeutic targets. The proteins identified in this study including NSP13, NSP7, ORF3a, ORF8a, and ORF8b, were found to be involved in crucial processes of the viral life cycle, and some of them are currently suspected to be antiviral targets. These results thus demonstrate the importance - and the predictive power- of the in silico analysis of the viral interactome to guide and support experimental investigation, which could otherwise be too complex and time-consuming to carry out in clinical and experimental research, given the size and interaction density of the viral protein network and the current still partial knowledge of this new virus. © 2021 IEEE.
ABSTRACT
OBJECTIVE: In late December 2019 in Wuhan (China), Health Commission reported a cluster of pneumonia cases of unknown etiology, subsequently isolated and named Severe Acute Respiratory Syndrome (SARS) Coronavirus 2 (CoV-2). In this review, the main transmission routes and causes of mortality associated with COVID-19 were investigated. MATERIAL AND METHODS: A review was carried out to recognize relevant research available until 10 April 2020. RESULTS: The main transmission routes of COVID-19 have been the following: animal to human and human-to-human pathways, namely: respiratory transmission; oro-fecal transmission; air, surface-human transmission. Transmission from asymptomatic persons, healthcare transmission, and interfamily transmission have been well documented. CONCLUSIONS: SARS-CoV-2 possesses powerful pathogenicity and transmissibility. It is presumed to spread primarily via respiratory droplets and close contact. The most probable transmission pathway is definitely the inter-human one. Asymptomatic patients seem to play a crucial role in spreading the infection. Because of COVID-19 infection pandemic potential, careful surveillance is essential to monitor its future host adaptation, viral evolution, infectivity, transmissibility, and pathogenicity in order to gain an effective vaccine and flock immunity and reduce mortality as soon and as much as it is possible.