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A new guideline for mitigating indoor airborne transmission of COVID-19 prescribes a limit on the time spent in a shared space with an infected individual (Bazant & Bush, Proceedings of the National Academy of Sciences of the United States of America , vol. 118, issue 17, 2021, e2018995118). Here, we rephrase this safety guideline in terms of occupancy time and mean exhaled carbon dioxide (${\\rm CO}_{2}$) concentration in an indoor space, thereby enabling the use of${\\rm CO}_{2}$monitors in the risk assessment of airborne transmission of respiratory diseases. While${\\rm CO}_{2}$concentration is related to airborne pathogen concentration (Rudnick & Milton, Indoor Air , vol. 13, issue 3, 2003, pp. 237–245), the guideline developed here accounts for the different physical processes affecting their evolution, such as enhanced pathogen production from vocal activity and pathogen removal via face-mask use, filtration, sedimentation and deactivation. Critically, transmission risk depends on the total infectious dose, so necessarily depends on both the pathogen concentration and exposure time. The transmission risk is also modulated by the fractions of susceptible, infected and immune people within a population, which evolve as the pandemic runs its course. A mathematical model is developed that enables a prediction of airborne transmission risk from real-time${\\rm CO}_{2}$measurements. Illustrative examples of implementing our guideline are presented using data from${\\rm CO}_{2}$monitoring in university classrooms and office spaces.

Accurate and efficient simulation of airflows in human airways is critical for advancing the understanding of respiratory physiology, disease diagnostics, and inhalation drug delivery. Traditional computational fluid dynamics (CFD) provides detailed predictions but is often mesh-sensitive and computationally expensive for complex geometries. In this study, we explored the usage of physics-informed neural networks (PINNs) to simulate airflows in three geometries with increasing complexity: a duct, a simplified mouth–lung model, and a patient-specific upper airway. Key procedures to implement PINN training and testing were presented, including geometry preparation/scaling, boundary/constraint specification, training diagnostics, nondimensionalization, and inference mapping. Both the laminar PINN and SDF–mixing-length PINN were tested. PINN predictions were validated against high-fidelity CFD simulations to assess accuracy, efficiency, and generalization. The results demonstrated that nondimensionalization of the governing equations was essential to ensure training accuracy for respiratory flows at 1 m/s and above. Hessian-matrix-based diagnosis revealed a quick increase in training challenges with flow speed and geometrical complexity. Both the laminar and SDF–mixing-length PINNs achieved comparable accuracy to corresponding CFD predictions in the duct and simplified mouth–lung geometry. However, only the SDF–mixing-length PINN adequately captured flow details unique to respiratory morphology, such as obstruction-induced flow diversion, recirculating flows, and laryngeal jet decay. The results of this study highlight the potential of PINNs as a flexible alternative to conventional CFD for modeling respiratory airflows, with adaptability to patient-specific geometries and promising integration with static or real-time imaging (e.g., 4D CT/MRI).

Continuous respiration monitoring is an important tool in assessing the patient’s health and diagnosing pulmonary, cardiovascular, and sleep-related breathing disorders. Various techniques and devices, both contact and contactless, can be used to monitor respiration. Each of these techniques can provide different types of information with varying accuracy. Thermal cameras have become a focal point in research due to their contactless nature, affordability, and the type of data they provide, i.e., information on respiration motion and respiration flow. Several studies have demonstrated the feasibility of this technology and developed robust algorithms to extract important information from thermal camera videos. This paper describes the current state-of-the-art in respiration monitoring using thermal cameras, dividing the system into acquiring data, defining and tracking the region of interest, and extracting the breathing signal and respiration rate. The approaches taken to address the various challenges, the limitations of these methods, and possible applications are discussed.

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea–hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Over the past few decades, our understanding of the fluid mechanics characterizing the pulmonary acinus of the lungs has been fundamentally revisited. In the present paper, we review the current knowledge of acinar convective airflows and their role in determining the fate of inhaled aerosols in the distal regions of the lungs. We discuss the influential body of computational and experimental efforts following the revealing bolus studies initiated by Heyder et al. (1988) that have dramatically advanced our description of acinar flow phenomena. In particular, we characterize the range of complex flow topologies that exist locally in alveolar cavities and describe the ensuing convective mechanisms known to generate kinematic irreversibility in the acinus, despite low-Reynolds-number flows. By using dimensional analysis, we shed some light on the intimate coupling that arises in the pulmonary acinus between diffusive, convective and sedimentation mechanisms for aerosol deposition. Finally, we evoke some of the critical challenges that lie ahead in predicting accurately the deposition of inhaled particles across the acinar region and give a brief outlook toward novel approaches for resolving acinar flow dynamics at the real scale.

In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q3e) is estimated from the differential pressure (ΔP) and battery voltage (Vb), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q3e, calculated solely from ΔP and Vb, showed high agreement with the measured flow Q3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and Vb. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information.

Robin Sequence (RS) is a potentially fatal craniofacial condition characterized by undersized jaw, posteriorly displaced tongue, and resultant upper airway obstruction (UAO). Accurate assessment of UAO severity is crucial for management and diagnosis of RS, yet current evaluation modalities have significant limitations and no quantitative measures of airway resistance exist. In this study, we combine 4-dimensional computed tomography and computational fluid dynamics (CFD) to assess, for the first time, UAO severity using fluid dynamic metrics in RS patients. Dramatic intrapopulation differences are found, with the ratio between most and least severe patients in breathing resistance, energy loss, and peak velocity equal to 40:1, 20:1, and 6:1, respectively. Analysis of local airflow dynamics characterized patients as presenting with primary obstructions either at the location of the tongue base, or at the larynx, with tongue base obstructions resulting in a more energetic stenotic jet and greater breathing resistance. Finally, CFD-derived flow metrics are found to correlate with the level of clinical respiratory support. Our results highlight the large intrapopulation variability, both in quantitative metrics of UAO severity (resistance, energy loss, velocity) and in the location and intensity of stenotic jets for RS patients. These results suggest that computed airflow metrics may significantly improve our understanding of UAO and its management in RS.

Airborne transmission has been recognised as an important route of transmission for SARS-CoV-2, the virus responsible for the COVID-19 pandemic. While coughing and sneezing are major aerosol sources, asymptomatic transmission highlights the need to study other exhalation modes in social settings. Gathering around a table, a common scenario for human interactions, may influence airborne transmission by modifying the airflows. Here, we employ high-fidelity large-eddy simulations to investigate the effect of a table for periodic breathing conditions (Reynolds number$Re\\approx 10^3$–$3\\times 10^3$, Froude number$Fr\\approx 17$–$50$) as well as during sudden, forceful exhalations at peak values of$Re\\approx 1.2\\times 10^4$and$Fr\\approx 70$, mimicking laughter. During downward exhalations, the distance between the source and the table defines a new length scale that constrains the natural spread of buoyant puffs and jets. The table limits forward particle transport but, in doing so, may increase particle concentrations reaching a recipient, raising transmission risks. Simulations of forceful exhalations, such as laughter, further show that the table acts as an inertial filter – intercepting medium-sized particles that would otherwise remain airborne. This introduces a cutoff size dependent on puff inertia, altering the resulting airborne particle size distribution.

Convective respiratory flows in the pulmonary acinus and their influence on the fate of inhaled particles are typically studied using computational fluid dynamics (CFD) or scaled-up experimental models. However, experiments that replicate several generations of the acinar tree while featuring cyclic wall motion have not yet been realized. Moreover, current experiments generally capture only flow dynamics, without inhaled particle dynamics, due to difficulties in simultaneously matching flow and particle dynamics. In an effort to overcome these limitations, we introduce a novel microfluidic device mimicking acinar flow characteristics directly at the alveolar scale. The model features an anatomically-inspired geometry that expands and contracts periodically with five dichotomously branching airway generations lined with alveolar-like cavities. We use micro-particle image velocimetry with a glycerol solution as the carrying fluid to quantitatively characterize detailed flow patterns within the device and reveal experimentally for the first time a gradual transition of alveolar flow patterns along the acinar tree from recirculating to radial streamlines, in support of hypothesized predictions from past CFD simulations. The current measurements show that our microfluidic system captures the underlying characteristics of the acinar flow environment, including Reynolds and Womersley numbers as well as cyclic wall displacements and alveolar flow patterns at a realistic length scale. With the use of air as the carrying fluid, our miniaturized platform is anticipated to capture both particle and flow dynamics and serve in the near future as a promising in vitro tool for investigating the mechanisms of particle deposition deep in the lung.

When you reach with your straw for the final drops of a milkshake, the liquid forms a train of plugs that flow slowly initially because of the high viscosity. They then suddenly rupture and are replaced with a rapid airflow with the characteristic slurping sound. Trains of liquid plugs also are observed in complex geometries, such as porous media during petroleum extraction, in microfluidic two-phase flows, or in flows in the pulmonary airway tree under pathological conditions. The dynamics of rupture events in these geometries play the dominant role in the spatial distribution of the flow and in determining how much of the medium remains occluded. Here we show that the flow of a train of plugs in a straight channel is always unstable to breaking through a cascade of ruptures. Collective effects considerably modify the rupture dynamics of plug trains: Interactions among nearest neighbors take place through the wetting films and slow down the cascade, whereas global interactions, through the total resistance to flow of the train, accelerate the dynamics after each plug rupture. In a branching tree of microchannels, similar cascades occur along paths that connect the input to a particular output. This divides the initial tree into several independent subnetworks, which then evolve independently of one another. The spatiotemporal distribution of the cascades is random, owing to strong sensitivity to the plug divisions at the bifurcations.