Breathing Conditions Research Articles

Brief Analysis of the Establishment of Singing State in Vocal Music Learning

As a form of musical expression, vocal music mainly shows the emotions to be expressed when singing songs by training pronunciation skills. Therefore, the psychological factors and emotional expression behind the singing skills play a key role in the complete performance of the song. For vocal singing, it is not enough to have a beautiful sound. It is also necessary to add strong inner emotions to the beating notes, and at the same time have good psychological coordination ability. Only in this way can the singing be performed vividly and vividly. In addition to the psychological state, there are also vocal state, breathing state, etc. The state of various organs of the human body when singing will affect the accuracy and completeness of the sound when singing, and will also affect people's most sincere and natural feelings.

Validation of a Human Upper Airway Computational Fluid Dynamics Model for Turbulent Mixing

Abstract Validation of a computational fluid dynamics (CFD) model used to simulate turbulent exchange in an anatomically detailed human upper airway with realistic breathing states is provided. Proper model validation is vital in confirming that temporal mixing and species distribution are accurate, therefore making the model useful in generalized turbulent mixing studies of the upper airway. Numerous levels of refinement were tested for time-step and mesh independence. Higher and lower rigor groups of modeling methodologies involved spatial discretization schemes, gradient reconstruction methods, transient formulations, and turbulence frameworks. A dual mesh independence study revealed that the rate of approach to mesh independence is a function of computational rigor and that multiple mesh independence studies should be carried out in parallel. The final validated model consisted of the finest mesh used in this study (8 × 106 cells), a time-step equating to 4000 timesteps per breath cycle, and higher rigor modeling methodologies. While its results were within the acceptable deviation from the experimental data, it was not as close as the model that utilized the coarsest mesh (∼2 × 106 cells), the fewest timesteps per breath cycle (128 timesteps per breath cycle), and lower rigor methodologies. Though the latter model was closer to the experimental data, it was proven to not be numerically independent, highlighting the importance of utilizing a myriad of metrics to prove numerical independence. Restricting independence studies to only using metrics from experimental comparisons is insufficient for proper validation.

Protective facemask-induced facial thermal stress and breathing burden during exercise in gyms

During the COVID-19 pandemic, more than 90% of infections occurred in indoor environments such as restaurants and gyms. In particular, gym attendees are facing challenges of both infection risks and extra heat and breathing burdens with the use of protective facemasks (PFMs) during intensive exercise. To evaluate the PFM-induced thermal stress and breathing discomfort during high-intensity exercise, this study investigated the mechanisms of heat and air exchange between environment of inside and outside PFM via experiments and computational fluid dynamics simulations. In the experimental phase, the effects of the activity intensity and type of PFM on thermal and breathing comfort were tested. An uncomfortable microenvironment of the face covered by a PFM was found when the air temperature and absolute humidity increased by 4.1% and 23.1%, respectively, during high-intensity exercise, relative to the sitting scenario (30.8 °C, 0.026 kg/m3), together with a higher CO2 concentration (31.0% increase) and lower O2 concentration (8.3% decrease). Moreover, the masks newly designed provided better thermal and breathing conditions than traditional masks due to the larger surface area and lower thickness. In simulation phase, the model sensitivity analyses were conducted to assess the effects of eight factors on thermal and breathing comfort. The mask surface area is found to most highly affect not only the thermal comfort indices (temperature, absolute humidity) but also breathing comfort indices (the concentration of CO2 and O2). The study results provide an important reference for government policymaking, mask design, and public health.

Preparation of high performance Fe-doped SnO2 humidity sensor and its application in respiration detection

A humidity sensor with high performance based on Fe3+ ions doped with SnO2 (Fe/SnO2) was synthesized by hydrothermal method and its mechanism was studied by the first principle based on density functional theory. Experimental results show that doping Fe3+ ions can effectively improve the performance of SnO2 humidity sensor. Compared with SnO2, the resistance of Fe/SnO2 humidity sensor increases from 4.5 to 5.5 orders of magnitude, the humidity hysteresis error decreases from 42.4% to 3.8% and the response/recovery time changes from 11 s/48 s–10 s/8 s. Calculations show that Fe3+ ions can enhance the chemical and physical adsorption energy, improving the sensitivity and linearity and reducing the hysteresis error and recovery time of SnO2, respectively. Moreover, a simple circuit for respiratory detection was designed based on Fe/SnO2, and the number of LED lights can distinguish different breathing states through resistance changes of the Fe/SnO2 humidity sensor. This study indicate Fe/SnO2 humidity sensor can be used in non-contact and visual breath detection, which has potential applications in preventing respiratory disease.

A physical exertion inspired multi-task learning framework for detecting out-of-breath speech

Physical exertion is a stress condition that affects how we normally produce speech. It alters both the temporal and spectral pattern of speech characteristics. Therefore, speech utterances can be used as a cost-effective telehealth solution to detect whether a person is under physical exertion. This paper deals with the detection of shortness of breath (or out-of-breath) condition from speech under physical load by using an multi-task learning (MTL) framework. The primary classification targets are the neutral and the out-of-breath classes. Naturally these are binary targets and do not reflect the actual extent of exertion. This leads to the creation of a novel auxiliary target learning in collaboration with a pre-trained expert system. The targets here indicate the level of exertion under physical load. The MTL framework for both the convolutional neural network (CNN) and CNN-long short-term memory (CLSTM) network performs nearly 1.5% (F1-score) better than the single task learning (STL) framework. In addition to that, out-of-breath speech has more influence on the lower frequency spectrum. Warped spectrograms are given as input to the networks, enabling the deep networks to focus on lower spectral regions. The non-linear frequency warping is achieved by Mel-scale transformation and constant-Q-transform (CQT). CQT, being less dependent on fixed-window size, shows at least 6.57% (F1-score) improvement over Mel-based inputs. The MTL framework, combined with the warped spectrum, performs better in classifying out-of-breath speech from neutral.

A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor.

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

Computational modelling of airflow in distal airways using hybrid lung model

ABSTRACT A major challenge in computational fluid dynamics (CFD) modelling of the human respiratory system is allowing for the variation of geometric scale. A methodology is proposed that enables CFD analysis from the middle through the distal airways to the alveolar level. The methodology relies on a hybrid lung geometry integrating a primary tracheobronchial tree (TBT) up to the fifth generation (section 1) and distal airway (section 2). Section 1 comprises of the middle airways reconstructed from patient CT scans. Section 2 comprises of the distal airway branching tree generated based on deterministic algorithm. The hybrid model allows the application of physiologically relevant boundary conditions in lieu of simplistic approximations used in previous studies. Two different breathing conditions are considered adapted from previous studies, representing resting and exercise activity. The predicted flow variables are assessed for numerical accuracy and validated by comparison with published experimental and numerical data.

Autonomic synchrony induced by hyperscanning interoception during interpersonal synchronization tasks.

According to previous research, people influence each other's emotional states during social interactions via resonance mechanisms and coordinated autonomic rhythms. However, no previous studies tested if the manipulation of the interoceptive focus (focused attention on the breath for a given time interval) in hyperscanning during synchronized tasks may have an impact on autonomic synchrony. Thus, this study aims to assess the psychophysiological synchrony through autonomic measures recording during dyadic linguistic and motor synchronization tasks performed in two distinct interoceptive conditions: the focus and no focus on the breath condition. 26 participants coupled in 13 dyads were recruited. Individuals' autonomic measures [electrodermal: skin conductance level and response (SCL, SCR); cardiovascular indices: heart rate (HR) and HR variability (HRV)] was continuously monitored during the experiment and correlational coefficients were computed to analyze dyads physiological synchrony. Inter-subject analysis revealed higher synchrony for HR, HRV, SCL, and SCR values in the focus compared to no focus condition during the motor synchronization task and in general more for motor than linguistic task. Higher synchrony was also found for HR, SCL, and SCR values during focus than no focus condition in linguistic task. Overall, evidence suggests that the manipulation of the interoceptive focus has an impact on the autonomic synchrony during distinct synchronization tasks and for different autonomic measures. Such findings encourage the use of hyperscanning paradigms to assess the effect of breath awareness practices on autonomic synchrony in ecological and real-time conditions involving synchronization.

Salbutamol transport and deposition in healthy cat airways under different breathing conditions and particle sizes.

Salbutamol is a bronchodilatator commonly used for the treatment of feline inflammatory lower airway disease, including asthma or acute bronchospasm. As in humans, a pressurized metered dose inhaler (pMDI) is used in conjunction with a spacer and a spherical mask to facilitate salbutamol administration. However, efficacy of inhalation therapy is influenced by different factors including the non-cooperative character of cats. In this study, the goal was to use computational fluid dynamics (CFD) to analyze the impact of breathing patterns and salbutamol particle size on overall drug transport and deposition using a specific spherical mask and spacer designed for cats. A model incorporating three-dimensional cat airway geometry, a commercially available spherical mask, and a 10 cm spacer, was used for CFD analysis. Two peak inspiratory flows were tested: 30 mL/s and 126 mL/s. Simulations were performed with 30s breathing different inspiratory and expiratory times, respiratory frequencies and peaks. Droplet spray transport and deposition were simulated with different particle sizes typical of the drug delivery therapies (1, 5, 10, and 15 μm). The percentage of particle deposition into the device and upper airways decreased with increasing particle diameter during both flows imposed in this cat model. During increased mean ventilatory rate (MVR) conditions, most of the salbutamol was lost in the upper airways. And during decreased MVR conditions, most of the particles remained in suspension (still in hold-up) between the mask and the carina, indicating the need for more than 30 s to be transported. In both flows the percentage of particles traveling to the lung was low at 1.5%-2.3%. In conclusion, in contrast to what has been described in the human literature, the results from this feline model suggest that the percentage of particles deposited on the upper airway decreases with increasing particle diameter.

Influence of Pulsatility and Inflow Waveforms on Tracheal Airflow Dynamics in Healthy Older Adults.

Tracheal collapsibility is a dynamic process altering local airflow dynamics. Patient-specific simulation is a powerful technique to explore the physiological and pathological characteristics of human airways. One of the key considerations in implementing airway computations is choosing the right inlet boundary conditions that can act as a surrogate model for understanding realistic airflow simulations. To this end, we numerically examine airflow patterns under the influence of different profiles, i.e., flat, parabolic, and Womersley, and compare these with a realistic inlet obtained from experiments. Simulations are performed in ten patient-specific cases with normal and rapid breathing rates during the inhalation phase of the respiration cycle. At normal breathing, velocity and vorticity contours reveal primary flow structures on the sagittal plane that impart strength to cross-plane vortices. Rapid breathing, however, encounters small recirculation zones. Quantitative flow metrics are evaluated using time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI). Overall, the flow metrics encountered in a real velocity profile are in close agreement with parabolic and Womersley profiles for normal conditions, however, the Womersley inlet alone conforms to a realistic profile under rapid breathing conditions.

FBG-Based Mattress for Heart Rate Monitoring in Different Breathing Conditions

In recent years, extensive investigations have been geared toward finding unobtrusive solutions for monitoring cardiorespiratory activity as an alternative to traditional clinical methods. Among others, the ones based on fiber Bragg grating sensors (FBGs) reveal remarkable promise for monitoring respiratory (RR) and heart rate (HR). The present study investigates the performance of a mattress based on a thirteen FBG-array for HR continuous estimation. Firstly, a metrological characterization was performed to assess system characteristics under frequencies simulating typical HR values (i.e., 60 beats per minute -bpm-, 90 bpm, and 120 bpm). Then, the proposed device was tested on eight healthy volunteers (both males and females) in the presence of different breathing stages (i.e., quiet breathing and tachypnea) and while mimicking common sleeping postures (i.e., supine, left side, and prone). The assessment of HR measurements under different breathing regimes and postures has rarely been addressed in FBG-based technologies. The achieved results suggest the proposed mattress has promising capability in reliably estimating HR values. These results together with the ones obtained in terms of RR monitoring in a recent study reveal the high potential for monitoring cardiorespiratory activity.

Domestic cat nose functions as a highly efficient coiled parallel gas chromatograph.

The peripheral structures of mammalian sensory organs often serve to support their functionality, such as alignment of hair cells to the mechanical properties of the inner ear. Here, we examined the structure-function relationship for mammalian olfaction by creating an anatomically accurate computational nasal model for the domestic cat (Felis catus) based on high resolution microCT and sequential histological sections. Our results showed a distinct separation of respiratory and olfactory flow regimes, featuring a high-speed dorsal medial stream that increases odor delivery speed and efficiency to the ethmoid olfactory region without compromising the filtration and conditioning purpose of the nose. These results corroborated previous findings in other mammalian species, which implicates a common theme to deal with the physical size limitation of the head that confines the nasal airway from increasing in length infinitely as a straight tube. We thus hypothesized that these ethmoid olfactory channels function as parallel coiled chromatograph channels, and further showed that the theoretical plate number, a widely-used indicator of gas chromatograph efficiency, is more than 100 times higher in the cat nose than an "amphibian-like" straight channel fitting the similar skull space, at restful breathing state. The parallel feature also reduces airflow speed within each coil, which is critical to achieve the high plate number, while feeding collectively from the high-speed dorsal medial stream so that total odor sampling speed is not sacrificed. The occurrence of ethmoid turbinates is an important step in the evolution of mammalian species that correlates to their expansive olfactory function and brain development. Our findings reveal novel mechanisms on how such structure may facilitate better olfactory performance, furthering our understanding of the successful adaptation of mammalian species, including F. catus, a popular pet, to diverse environments.

Accuracy of the estimations of respiratory mechanics using an expiratory time constant in passive and active breathing conditions: a bench study

BackgroundRespiratory mechanics monitoring provides useful information for guiding mechanical ventilation, but many measuring methods are inappropriate for awake patients. This study aimed to evaluate the accuracy of dynamic mechanics estimation using expiratory time constant (RCexp) calculation during noninvasive pressure support ventilation (PSV) with air leak in different lung models.MethodsA Respironics V60 ventilator was connected to an active breathing simulator for modeling five profiles: normal adult, restrictive, mildly and severely obstructive, and mixed obstructive/restrictive. Inspiratory pressure support was adjusted to maintain tidal volumes (VT), achieving 5.0, 7.0, and 10.0 ml/kg body weight. PEEP was set at 5 cmH2O, and the back-up rate was 10 bpm. Measurements were conducted at system leaks of 25–28 L/min. RCexp was estimated from the ratio at 75% exhaled VT and flow rate, which was then used to determine respiratory system compliance (Crs) and airway resistance (Raw).ResultsIn non-obstructive conditions (Raw ≤ 10 cmH2O/L/s), the Crs was overestimated in the PSV mode. Peak inspiratory and expiratory flow and VT increased with PS levels, as calculated Crs decreased. In passive breathing, the difference of Crs between different VT was no significant. Underestimations of inspiratory resistance and expiratory resistance were observed at VT of 5.0 ml/kg. The difference was minimal at VT of 7.0 ml/kg. During non-invasive PSV, the estimation of airway resistance with the RCexp method was accurately at VT of 7.0 ml/kg.ConclusionsThe difference between the calculated Crs and the preset value was influenced by the volume, status and inspiratory effort in spontaneously breathing.

The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing.

Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals. We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI. Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI). Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.

Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88.79% (without Stage 1 (data size reducing convolutional layer)), 90.58% (with 1 × 3 Stage 1), and 91.77% (with 1 × 5 Stage 1).

Exploring the role of nasal hair in inhaled airflow and coarse dust particle dynamics in a nasal cavity: A CFD-DEM study

This study presents a novel coupled computational fluid dynamics-discrete element method (CFD-DEM) model for simulating airflow and inhaled particle dynamics in the nasal cavity, considering the fluid-structure interactions between nasal hair and airflow, which are often overlooked in existing CFD simulations. The CFD-DEM model, based on a virtual nasal cavity geometry, represents nasal hairs as flexible fibers implanted on the inner wall of the nasal cavity. Inhaled air-particle transport dynamics were simulated in the presence of nasal hairs with three different diameters (40, 95, and 120 μm), and the results were compared with simulations without nasal hairs. Polydisperse particles with diameters ranging from 5 to 15 μm, aligning with global coarse dust statistics, were inhaled at flow rates between 216 mL/s and 630 mL/s, representing human nose breathing conditions from rest to exercise. The CFD-DEM simulation results indicate that the differences in deposition fraction predictions between cases with and without nasal hairs exceed 20% for particle size ranges corresponding to impaction parameter values between 3E+3 and 2E+4 μm2cm3/s. This emphasizes the importance of accounting for nasal hair interactions in accurately simulating inhaled particle transport dynamics within the nasal cavity. Thinner nasal hairs, more flexible than thicker ones, lead to more significant deformation and allow more particles to penetrate the nasal vestibule without being trapped. Additionally, thinner hairs occupy a smaller cross-section area in the nasal vestibule, resulting in lower filtration capability due to decreased particle deposition from interception and inertial impaction. Thicker nasal hairs trap more coarse dust particles (> 5 μm) effectively than thinner nasal hairs. Moreover, as the nasal hair diameter increases, particle deposition on the nasal cavity wall decreases. Nasal hairs demonstrate higher efficiency in trapping smaller particles. In conclusion, the CFD-DEM model, capable of modeling nasal hair motion, can serve as a next-generation in silico tool for investigating biofluid dynamics in the nasal cavity.

Computational investigation of flow characteristics and particle deposition patterns in a realistic human airway model under different breathing conditions

Airborne particle pollution causes a range of respiratory and cardiovascular disorders by entering the human respiratory system through the breathing process. The administration of pharmaceutical particles by inhalation is another effective way to treat pulmonary illnesses. Studying particle deposition in the respiratory system during human breathing is crucial to maintaining human health. This necessity served as the impetus for this work, which aims to investigate how the airflow and particles' deposition are influenced by constant inhalation and circulatory breathing, particle diameter, and changes in airflow rate. The focus of this paper is to compare the particle deposition results of circulatory respiration with constant respiration. Based on computed tomography (CT) scan pictures, a precise human airway model from the mouth cavity to the fifth-generation bronchi was created. Flow fields and particle deposition inside the respiratory tract were examined at varied breathing rates (30, 60, and 90 L/min of constant and circulatory breathing) and varying haled particle sizes (5 and 10 µm). The results showed that the oropharyngeal area is often where the majority of particles are deposited. The particle distribution fraction is more significant in the bronchial area than the oropharyngeal region due to lower inhalation velocities and smaller particle sizes. For particles with a diameter of 5 µm, constant respiration and circulatory respiration have virtually identical particle distribution fractions in each region. For particles with a diameter of 10 µm, the particle distribution fraction for circulatory respiration is slightly higher than for constant respiration in the bronchial region as the flow rate increases. For both constant and circulatory respiration, particles are deposited more in the right lung and less in the left. These results contribute to further research on respiratory diseases caused by inhaled particles and guide inhalation therapy for better treatment outcomes.

Effect of cartilaginous rings in tracheal flow with stenosis

BackgroundIn respiratory fluid dynamics research, it is typically assumed that the wall of the trachea is smooth. However, the trachea is structurally supported by a series of cartilaginous rings that create undulations on the wall surface, which introduce perturbations into the flow. Even though many studies use realistic Computer Tomography (CT) scan data to capture the complex geometry of the respiratory system, its limited spatial resolution does not resolve small features, including those introduced by the cartilaginous rings.ResultsHere we present an experimental comparison of two simplified trachea models with Grade II stenosis (70% blockage), one with smooth walls and second with cartilaginous rings. The use a unique refractive index-matching method provides unprecedented optical access and allowed us to perform non-intrusive velocity field measurements close to the wall (e.g., Particle Image Velocimetry (PIV)). Measurements were performed in a flow regime comparable to a resting breathing state (Reynolds number ReD = 3350). The cartilaginous rings induce velocity fluctuations in the downstream flow, enhancing the near-wall transport of momentum flux and thus reducing flow separation in the downstream flow. The maximum upstream velocity in the recirculation region is reduced by 38%, resulting in a much weaker recirculation zone— a direct consequence of the cartilaginous rings.ConclusionsThese results highlight the importance of the cartilaginous rings in respiratory flow studies and the mechanism to reduce flow separation in trachea stenosis.

On a model-based approach to improve intranasal spray targeting for respiratory viral infections

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17 μm are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude.

Abstracts from The Aerosol Society Drug Delivery to the Lungs 33.

Abstracts from The Aerosol Society Drug Delivery to the Lungs 33.