Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CM) are a promising in vitro tool for drug development and disease modeling, but their immature electrophysiology limits their diagnostic utility. Tissue engineering approaches involving aligned and 3D culture enhance hiPSC-CM maturation but are insufficient to induce electrophysiological maturation. We hypothesized that recapitulating postnatal switching of the heart's primary adenosine triphosphate source from glycolysis to fatty acid oxidation could enhance maturation of hiPSC-CM. We combined hiPSC-CM with microfabrication to create 3D cardiac microphysiological systems (MPS) that enhanced immediate microtissue alignment and tissue specific extracellular matrix production. Using Robust Experimental design, we identified a maturation media that allowed the cardiac MPS to correctly assess false positive and negative drug response. Finally, we employed mathematical modeling and gene expression data to explain the observed changes in electrophysiology and pharmacology of MPS exposed to maturation media. In contrast, the same media had no effects on 2D hiPSC-CM monolayers. These results suggest that systematic combination of biophysical stimuli and metabolic cues can enhance the electrophysiological maturation of hiPSCderived cardiomyocytes. Results and Discussion Robust Design Experiments Indicate Optimal Carbon Sourcing for Mature Beating PhysiologyWe have developed a microfabricated cardiac MPS, employing hiPSC-CM, for drug testing (Mathur et al. 2015). In the present study, we formed cardiac MPS that that mimicked the mass composition of the human heart by combining 80% hiPSC-CM and 20% hiPSC-SC (Supplemental Methods, Fig. S1-2). We employed Robust Experimental Design to screen for the effects of glucose, oleic acid, palmitic acid, and albumin (bovine serum albumin, BSA) levels on hiPSC-CM maturity ( Table 1). MPS were incubated with different fatty-acid media for ten days, at which time their beating physiology and calcium flux were assessed. Optimal media would reduce automaticity (e.g. reduce spontaneous beating rate), while also reducing the interval between peak contraction and peak relaxation (a surrogate for APD) in field-paced tissues (Fig. 1A-C), while maintaining a high level of beating prevalence during pacing (defined as the percent of the tissue with substantial contractile motion; Fig. 1D). In general, beating prevalence was consistent with calcium flux amplitude (Fig. 1D,F), and beating interval correlated with rate-corrected Full-Width Half Maximum calcium flux time, FWHMc; Fig. 1B,E).
Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (± 10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with re-The first and last author contributed equally to this work. Asterisk indicates corresponding author.
Background Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. Objective This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. Methods Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. Results In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were −1.0 (95% CI −12.3 to 10.2) and −0.9 (95% CI −6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. Conclusions The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.
BackgroundCough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalized treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in COVID-19 and other lower respiratory tract infections.MethodsWe conducted a single-center, exploratory, observational cohort study on automated cough detection in patients hospitalized for COVID-19- (n=32) and non-COVID-19 pneumonia (n=14) between April and November 2020 at the Cantonal Hospital St.Gallen, Switzerland. Cough detection was achieved using smartphone-based audio recordings coupled to an ensemble of convolutional neural networks. Cough levels were correlated to established markers of inflammation and oxygenation.Measurements and main resultsCough frequency was highest upon hospital admission and declined steadily with recovery. There was a characteristic pattern of daily cough fluctuations, with little activity during the night and two coughing peaks during the day. Hourly cough counts were strongly correlated with clinical markers of disease activity and laboratory markers of inflammation, suggesting cough as a surrogate-of-disease in acute respiratory tract infections. No apparent differences in cough evolution were observed between COVID-19- and non-COVID-19 pneumonia.ConclusionsAutomated, quantitative, smartphone-based detection of cough is feasible in hospitalized patients and correlates with disease activity in lower respiratory tract infections. Our approach allows for near real-time telemonitoring of individuals in aerosol isolation. Larger trials are warranted to decipher the use of cough as a digital biomarker for prognosis and tailored treatment in lower respiratory tract infections.
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