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Competing Interests: There are no conflicts of interest for all authors.

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.

Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan-Tompkins (AMPT), which is a simplified version of the well-established Pan-Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan-Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5-20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.

The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations ( P value = 1 × 10 -4 ) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens. IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy., (Copyright © 2019 Ma et al.)

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.

Early-life wheezing-associated infections with rhinovirus (RV) have been associated with asthma development in children. We have shown that RV infection of 6-day-old mice induces mucous metaplasia and airways hyperresponsiveness, which is dependent on IL-13, IL-25, and type 2 innate lymphoid cells (ILC2s). Infection of immature mice fails to induce lung IFN-γ expression, in contrast to mature 8-week-old mice with a robust IFN-γ response, consistent with the notion that deficient IFN-γ production in immature mice permits RV-induced type 2 immune responses. We therefore examined the effects of intranasal IFN-γ administration on RV-induced ILC2 expansion and IL-13 expression in 6-day-old BALB/c and IL-13 reporter mice. Airway responses were assessed by histology, immunofluorescence microscopy, quantitative polymerase chain reaction, ELISA, and flow cytometry. Lung ILC2s were also treated with IFN-γ ex vivo. We found that, compared with untreated RV-infected immature mice, IFN-γ treatment attenuated RV-induced IL-13 and Muc5ac mRNA expression and mucous metaplasia. IFN-γ also reduced ILC2 expansion and the percentage of IL-13-secreting ILC2s. IFN-γ had no effect on the mRNA or protein expression of IL-25, IL-33, or thymic stromal lymphoprotein. Finally, IFN-γ treatment of sorted ILC2s reduced IL-5, IL-13, IL-17RB, ST2, and GATA-3 mRNA expression. We conclude that, in immature mice, IFN-γ inhibits ILC2 expansion and IL-13 expression in vivo and ex vivo, thereby attenuating RV-induced mucous metaplasia. These findings demonstrate the antagonistic function of IFN-γ on ILC2 expansion and gene expression, the absence of which may contribute to the development of an asthma-like phenotype after early-life RV infection.