Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data

Myristoylated rhinovirus VP4 protein activates TLR2-dependent pro-inflammatory gene expression.

Asthma exacerbations are often caused by rhinovirus (RV). We and others have shown that Toll-like receptor 2 (TLR2), a membrane surface receptor that recognizes bacterial lipopeptides and lipoteichoic acid, is required and sufficient for RV-induced proinflammatory responses in vitro and in vivo. We hypothesized that viral protein-4 (VP4), an internal capsid protein that is myristoylated upon viral replication and externalized upon viral binding, is a ligand for TLR2. Recombinant VP4 and myristoylated VP4 (MyrVP4) were purified by Ni-affinity chromatography. MyrVP4 was also purified from RV-A1B-infected HeLa cells by urea solubilization and anti-VP4 affinity chromatography. Finally, synthetic MyrVP4 was produced by chemical peptide synthesis. MyrVP4-TLR2 interactions were assessed by confocal fluorescence microscopy, fluorescence resonance energy transfer (FRET), and monitoring VP4-induced cytokine mRNA expression in the presence of anti-TLR2 and anti-VP4. MyrVP4 and TLR2 colocalized in TLR2-expressing HEK-293 cells, mouse bone marrow-derived macrophages, human bronchoalveolar macrophages, and human airway epithelial cells. Colocalization was absent in TLR2-null HEK-293 cells and blocked by anti-TLR2 and anti-VP4. Cy3-labeled MyrVP4 and Cy5-labeled anti-TLR2 showed an average fractional FRET efficiency of 0.24 ± 0.05, and Cy5-labeled anti-TLR2 increased and unlabeled MyrVP4 decreased FRET efficiency. MyrVP4-induced chemokine mRNA expression was higher than that elicited by VP4 alone and was attenuated by anti-TLR2 and anti-VP4. Cytokine expression was similarly increased by MyrVP4 purified from RV-infected HeLa cells and synthetic MyrVP4. We conclude that, during RV infection, MyrVP4 and TLR2 interact to generate a proinflammatory response.http://deepblue.lib.umich.edu/bitstream/2027.42/175727/2/ajplung.00365.2018.pdfPublished versio