Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance.
(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.
Human respiratory syncytial virus (hRSV) is one of the main etiological agents of diseases of the lower respiratory tract, and is often responsible for the hospitalization of children and the elderly. To date, treatments are only palliative and there is no vaccine available. The airways of patients infected with hRSV exhibit intense neutrophil infiltration, which is responsible for the release of neutrophil extracellular traps (NETs). These are extracellular structures consisting of DNA associated with intracellular proteins, and are efficient in capturing and eliminating various microorganisms, including some viruses. hRSV induces the release of NETs into the lung tissue of infected individuals; however, the pathophysiological consequences of this event have not been elucidated. The objective of this study was to utilize in vitro and in silico assays to investigate the impact of NETs on hRSV infection. NETs, generated by neutrophils stimulated with phorbol myristate acetate (PMA), displayed long fragments of DNA and an electrophoretic profile suggestive of the presence of proteins that are classically associated with these structures (elastase, cathepsin G, myeloperoxidase, and histones). The presence of NETs (>2 μg/ml) in HEp-2 cell culture medium resulted in cellular cytotoxicity of less than 50%. Pre-incubation (1 h) of viral particles (multiplicity of infection (MOI) values of 0.1, 0.5, and 1.0) with NETs (2-32 μg/ml) resulted in cellular protection from virus-induced death of HEp-2 cells. Concurrently, there was a reduction in the formation of syncytia, which is related to decreased viral spread in infected tissue. Results from western blotting and molecular docking, suggest interactions between F protein of the hRSV viral envelope and BPI (bactericidal permeability-increasing protein), a microbicidal member of NETs. Interactions occurred at sites important for the neutralization and coordination of the hRSV infection/replication process. Our results showed that the presence of NETs decreases hRSV-induced cellular damage, possibly by directly affecting viral particle capture and/or interfering with the fusion activity of the F protein. These findings broaden the understanding of the role of NETs during hRSV infection.
We show that the oxidation-triggered destabilization of boronic acid-installed polycarbonate nanoparticles depends both on H2O2 content, as well as on oxidized polymer concentration, which should fall below the critical micelle value.
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