Electrocardiographic (ECG) signals that monitor heart activity can help identifying disease-related anomalies. Reliable automatic anomaly detection has been shown to be useful in supporting physicians in reading ECG signals. Decision support systems may be useful in such cases but their reliability can be guaranteed. Autoencoders (AEs) have been extensively used to analyse signals in many fields. Convolutional Autoencoders (CAE) are a particular class of AE showing optimal performances in detecting signal anomalies. Thus, CAEs can be used to support and automatise the task of anomaly detection. We design and use a CAE-based system to detect anomalies in ECG signals to support cardiologists in identifying anomalies related to possible diseases. Our tool outperforms other state-of-the-art ECG anomaly detection approaches tested on a real dataset. In the task of anomaly detection, our CAE obtains a ROC AUC of 97.82% with a simulated test set and a ROC AUC of 99.75% using on a real test set. The tool and the source code are available at https://github.com/UgoLomoio/EG_DSS_CAE.

The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on Spike protein structure on the subvariants of XBB with particular attention to the velocity of variant diffusion and virus activity w.r.t. its diffusion. We examine the structural and functional distinctions of the variants in 3 different conformations: (i) Spike glycoprotein in complex with ACE2 (1-up state), (ii) Spike glycoprotein (closed-1 state) and (iii) S protein (open-1 state). We also estimate the transmissibility of the affinity binding between Spike proteins and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in controlling the spread of these variants. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly.