The assessment of the mechanical properties of the respiratory system is typically done by oscillating flow into the lungs via the trachea, measuring the resulting pressure generated at the trachea, and relating the two signals to each other in terms of some suitable mathematical model. If the perturbing flow signal is broadband and not too large in amplitude, linear behavior is usually assumed and the input impedance calculated. Alternatively, some researchers have used flow signals that are narrow band but large in amplitude, and invoked nonlinear lumped-parameter models to account for the relationship between flow and pressure. There has been little attempt, however, to deal with respiratory data that are both broadband and reflective of system nonlinearities. In the present study, we collected such data from mice. To interpret these data, we first developed a time-domain approximation to a widely used model of respiratory input impedance. We then extended this model to include nonlinear resistive and elastic terms. We found that the nonlinear elastic term fit the data better than the linear model or the nonlinear resistance model when amplitudes were large. This model may be useful for detecting overinflation of the lung during mechanical ventilation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1114/1.1553453 | DOI Listing |
Chem Res Toxicol
January 2025
Department of Chemical, Environmental, and Materials Engineering, University of Miami, Miami, Florida 33146, United States.
This study employed high-time-resolution systems to examine the transient properties of aerosols and gases emitted from electronic cigarette (EC) puffs. Using a fast aerosol sizer, we measured particle size distributions (PSDs) across various EC brands (JUUL, VUSE, VOOPOO), revealing sizes ranging from 5 to 1000 nm at concentrations of 10 to 10 cm. Most aerosols were found to be in the ultrafine range (below 100 nm), with JUUL-, VUSE-, and VOOPOO-producing aerosols with geometric mean sizes of 19.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Faculty of Engineering Sciences, Kyushu University, Fukuoka, Japan.
Background And Objective: Coughing events are eruptive sources of virus-laden droplets/droplet nuclei. These increase the risk of infection in susceptible individuals during airborne transmission. The oral cavity functions as an exit route for exhaled droplets.
View Article and Find Full Text PDFJ Virol
January 2025
Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
Unlabelled: Respiratory and encephalitic virus infections represent a significant risk to public health globally. Detailed investigations of immunological responses and disease outcomes during sequential virus infections are rare. Here, we define the impact of influenza virus infection on a subsequent virus encephalitis.
View Article and Find Full Text PDFCochrane Database Syst Rev
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, IL, USA.
This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of transtracheal ultrasound for detecting endotracheal intubation in adult patients. Secondary objectives Secondary objectives include assessing the diagnostic accuracy of transtracheal ultrasound amongst the following subgroups: setting (e.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!