A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection.

Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in "irregularly irregular" heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.

SARS-CoV-2 spike protein-induced cell fusion activates the cGAS-STING pathway and the interferon response.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the unprecedented coronavirus disease 2019 (COVID-19) pandemic. Critical cases of COVID-19 are characterized by the production of excessive amounts of cytokines and extensive lung damage, which is partially caused by the fusion of SARS-CoV-2-infected pneumocytes. Here, we found that cell fusion caused by the SARS-CoV-2 spike (S) protein induced a type I interferon (IFN) response. This function of the S protein required its cleavage by proteases at the S1/S2 and the S2' sites. We further showed that cell fusion damaged nuclei and resulted in the formation of micronuclei that were sensed by the cytosolic DNA sensor cGAS and led to the activation of its downstream effector STING. Phosphorylation of the transcriptional regulator IRF3 and the expression of IFNB, which encodes a type I IFN, were abrogated in cGAS-deficient fused cells. Moreover, infection with VSV-SARS-CoV-2 also induced cell fusion, DNA damage, and cGAS-STING-dependent expression of IFNB. Together, these results uncover a pathway underlying the IFN response to SARS-CoV-2 infection. Our data suggest a mechanism by which fused pneumocytes in the lungs of patients with COVID-19 may enhance the production of IFNs and other cytokines, thus exacerbating disease severity.