Expiratory flow limitation during mechanical ventilation: real-time detection and physiological subtypes.

BACKGROUND: Tidal expiratory flow limitation (EFLT) complicates the delivery of mechanical ventilation but is only diagnosed by performing specific manoeuvres. Instantaneous analysis of expiratory resistance (Rex) can be an alternative way to detect EFLT without changing ventilatory settings. This study aimed to determine the agreement of EFLT detection by Rex analysis and the PEEP reduction manoeuvre using contingency table and agreement coefficient. The patterns of Rex were explored. METHODS: Medical patients ≥ 15-year-old receiving mechanical ventilation underwent a PEEP reduction manoeuvre from 5 cmH2O to zero for EFLT detection. Waveforms were recorded and analyzed off-line. The instantaneous Rex was calculated and was plotted against the volume axis, overlapped by the flow-volume loop for inspection. Lung mechanics, characteristics of the patients, and clinical outcomes were collected. The result of the Rex method was validated using a separate independent dataset. RESULTS: 339 patients initially enrolled and underwent a PEEP reduction. The prevalence of EFLT was 16.5%. EFLT patients had higher adjusted hospital mortality than non-EFLT cases. The Rex method showed 20% prevalence of EFLT and the result was 90.3% in agreement with PEEP reduction manoeuvre. In the validation dataset, the Rex method had resulted in 91.4% agreement. Three patterns of Rex were identified: no EFLT, early EFLT, associated with airway disease, and late EFLT, associated with non-airway diseases, including obesity. In early EFLT, external PEEP was less likely to eliminate EFLT. CONCLUSIONS: The Rex method shows an excellent agreement with the PEEP reduction manoeuvre and allows real-time detection of EFLT. Two subtypes of EFLT are identified by Rex analysis. TRIAL REGISTRATION: Clinical trial registered with www.thaiclinicaltrials.org (TCTR20190318003). The registration date was on 18 March 2019, and the first subject enrollment was performed on 26 March 2019.

User authentication system based on human exhaled breath physics.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

Neuroendocrine cells initiate protective upper airway reflexes.

Airway neuroendocrine (NE) cells have been proposed to serve as specialized sensory epithelial cells that modulate respiratory behavior by communicating with nearby nerve endings. However, their functional properties and physiological roles in the healthy lung, trachea, and larynx remain largely unknown. In this work, we show that murine NE cells in these compartments have distinct biophysical properties but share sensitivity to two commonly aspirated noxious stimuli, water and acid. Moreover, we found that tracheal and laryngeal NE cells protect the airways by releasing adenosine 5'-triphosphate (ATP) to activate purinoreceptive sensory neurons that initiate swallowing and expiratory reflexes. Our work uncovers the broad molecular and biophysical diversity of NE cells across the airways and reveals mechanisms by which these specialized excitable cells serve as sentinels for activating protective responses.

A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images.

OBJECTIVES: To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. METHODS: Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. RESULTS: The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. CONCLUSIONS: A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. ADVANCES IN KNOWLEDGE: The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.

Isoproterenol modulates expiratory activities in the brainstem spinal cord preparation in neonatal mice in vitro.

Motor behaviors such as breathing required temporal coordination of different muscle groups to insured efficient ventilation and provide oxygen to the body. This action is the result of interactions between neural networks located within the brainstem. Inspiration and expiration depend at least in part on interactions between two separate oscillators: inspiration is driven by a neural network located in the preBötzinger complex (PreBötC) and active expiration is driven by a network in the parafacial respiratory group (pFRG). Neurons of the pFRG are silent at rest and become active when the respiratory drive increased. This study investigated the temporal coordination between the brainstem respiratory network and the lumbar spinal network that generates spontaneous activities that is different of the induced fictive locomotion. The remaining question is how these activities coordinate early during the development. Results of this study show that brainstem networks contribute to the temporal coordination of the lumbar spontaneous activity during inspiration since lumbar motor activity occurs exclusively during the expiratory time. This study also investigated the role of the ß-noradrenergic modulation on the respiratory activities. ß-noradrenergic receptors activation increased the frequency of the double bursts and increased expiratory activity at the lumbar level. These results suggest interactions between brainstem and spinal networks and reveal a descending drive that may contribute to the coordination of the respiratory and lumbar spontaneous activities.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Instrumental Assessment of Aero-Resistive Expiratory Muscle Strength Rehabilitation Devices.

PURPOSE: Expiratory muscle strength training (EMST) is increasingly being used to treat voice, cough, and swallowing deficits in a wide range of conditions. However, a multitude of aero-resistive EMST models are commercially available, and the absence of side-by-side comparative data interferes with clinicians' ability to assess which model is best suited to a particular client's needs. The primary aim of this research was to test and compare the pressure and flow parameters of six currently available EMST models to help inform clinical decision making. METHOD: We identified and tested five devices of each of six different EMST models to generate benchmark data for minimum trigger pressures across settings. The reliability was tested within each device and between five devices of the same model across settings using coefficient of variation. RESULTS: All six models required higher pressures to initiate flow at the highest setting compared to the lowest setting, as expected. Detailed descriptive statistics for each model/setting combination include average flow-triggering pressure for each model/setting and the variability across trials within a device and across devices of the same model. From these, ranked order of the least to most stable EMST model was derived. CONCLUSIONS: Systematic testing of several commercially available expiratory resistance training devices yielded clinical benchmarks and reliability data to aid clinicians in selecting an appropriate therapy device and regimen for a client based on their available airflow and air pressure as well as reliability of the device. These findings allow clinicians to directly compare key parameters across EMST devices.

Self-powered triboelectric nanogenerator sensor for detecting humidity level and monitoring ethanol variation in a simulated exhalation environment.

Respiration stands as a vital process reflecting physiological and pathological human health status. Exhaled breath analysis offers a facile, non-invasive, swift, and cost-effective approach for diagnosing and monitoring diseases by detecting concentration changes of specific biomarkers. In this study, we employed Polyethylene oxide/copper (I) oxide composite nanofibers (PCNFs), synthesized via the electrospinning method as the sensing material to measure ethanol levels (1-200 ppm) in an exhaled breath simulator environment. The integrated contact-separation triboelectric nanogenerator was utilized to power the self-powered PCNFs exhaled breath sensor. The PCNFs-based gas sensor demonstrates promising results with values of 0.9 and 3.2 for detecting 5 ppm and 200 ppm ethanol, respectively, in the presence of interfering gas at 90% relative humidity (RH). Notably, the sensor displayed remarkable ethanol selectivity, with ratios of 10:1 to methanol and 25:1 to acetone. Response and recovery times for 200 ppm ethanol at 90 RH% were rapid, at 2.7 s and 5.8 s, respectively. The PCNFs-based exhaled breath sensor demonstrated consistent and stable performance in practical conditions, showcasing its potential for integration into wearable devices. This self-powered breath sensor enabling continuous monitoring of lung cancer symptoms and facilitating compliance checks with legal alcohol consumption limits.

Body positioning-related laryngeal narrowing pattern and expiratory mechanical constraints in advanced chronic obstructive pulmonary disease: A case report.

We previously reported that laryngeal widening led to improved exercise tolerance in COPD. However, it is not clear whether laryngeal narrowing occurs as a compensatory response to tracheal movement or is affected by posture. Here, we report the case of an advanced COPD patient whose more prolonged expiration in a head-forward leaning position compared with that in a neck-extended position occurred with an excessive duration of severe laryngeal narrowing without tracheal obstruction, which led to exercise intolerance with expiratory mechanical constraints. This case provided useful insights into the regulation of the upper airway with body positioning for improving exercise tolerance.

Effects of the intermittent intrapulmonary deflation technique on expiratory flow limitation: an in vitro study.

INTRODUCTION: The intermittent intrapulmonary deflation (IID) technique is a recent airway clearance technique that intends to delay the onset of expiratory flow limitation (EFL) during exhalation. We showed in a previous study that IID increased the expiratory volume of COPD patients compared to quiet breathing and positive expiratory pressure (PEP) therapy. We hypothesized that it was due to the attenuation of the EFL. OBJECTIVES: To verify the physiologic effects of IID and PEP techniques on EFL with a mechanical lung model. METHODS: A mechanical lung model was created to assess the effects of IID and PEP techniques. The thorax was simulated by a plexiglas box in which an adult test lung was connected. A calibration syringe simulated the inspiratory phase. Later, with activation of the IID, the expiratory phase was driven by the deflation generated by the device. With PEP, the expiration occurred maintaining an expiratory pressure between 5 and 10 cmH2O. A pneumotachograph and a pressure transducer were placed in series for flow, volumes and pressure measurements. RESULTS: The model reproduced physiological characteristics of EFL. However, the deflation of the model was slowed by IID and PEP, and flow remained almost constant, so flow limitation was reduced. CONCLUSION: The IID and PEP attenuate EFL and increase exhaled volume in the in vitro model.

Effects of three physical exercise modalities on respiratory function of older adults with Parkinson's disease: A randomized clinical trial.

INTRODUCTION: Deficits in respiratory function of patients with Parkinson's disease contribute to aspiration pneumonia, one of the main causes of mortality in this population. The aim of this study was to evaluate the effects of functional training, bicycle exercise, and exergaming on respiratory function of elderly with Parkinson's disease. METHODS: A randomized clinical trial with single blinding was conducted in a public reference outpatient clinic for the elderly. The participants were randomly assigned to three groups. Group 1 was submitted to functional training (n = 18); group 2 performed bicycle exercise (n = 20), and group 3 trained with Kinect Adventures exergames (n = 20). The sessions performed lasted 8 weeks with a frequency of three 50-min sessions per week. The primary outcome was the forced expiratory volume in the first second; and the secondary outcomes were forced vital capacity, peak expiratory flow, and maximum inspiratory and expiratory pressures. RESULTS: The interventions performed did not improve the forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow. However, group 2 improved (p = 0.03) maximum expiratory pressure (from 65.5cmH2O to 73.1cmH2O) (effect size 0.47), and group 3 increased (p = 0.03) maximum inspiratory pressure (from -61.3cmH2O to -71.6cmH2O) (effect size 0.53). CONCLUSIONS: No effect was found on lung volume, forced respiratory flow and capacity of the participants with Parkinson's disease submitted to three different modalities of motor training. However, bicycle exercise and exergaming have improved expiratory and inspiratory muscle strength, respectively. NCT02622737.

Exhaled breath particles as a novel tool to study lipid composition of epithelial lining fluid from the distal lung.

BACKGROUND: Surfactant phospholipid (PL) composition plays an important role in lung diseases. We compared the PL composition of non-invasively collected exhaled breath particles (PEx) with bronchoalveolar lavage (BAL) and induced sputum (ISP) at baseline and following endotoxin (LPS) challenges. METHODS: PEx and BAL were collected from ten healthy nonsmoking participants before and after segmental LPS challenge. Four weeks later, PEx and ISP were sampled in the week before and after a whole lung LPS inhalation challenge. PL composition was analysed using mass spectrometry. RESULTS: The overall PL composition of BAL, ISP and PEx was similar, with PC(32:0) and PC(34:1) representing the largest fractions in all three sample types (baseline PC(32:0) geometric mean mol%: 52.1, 56.9, and 51.7, PC(34:1) mol%: 11.7, 11.9 and 11.4, respectively). Despite this similarity, PEx PL composition was more closely related to BAL than to ISP. For most lipids comparable inter-individual differences in BAL, ISP, and PEx were found. PL composition of PEx was repeatable. The most pronounced increase following segmental LPS challenge was detected for SM(d34:1) in BAL (0.24 to 0.52 mol%) and following inhalation LPS challenge in ISP (0.45 to 0.68 mol%). An increase of SM(d34:1) following segmental LPS challenge was also detectable in PEx (0.099 to 0.103 mol%). The inhalation challenge did not change PL composition of PEx. CONCLUSION: Our data supports the peripheral origin of PEx. The lack of PL changes in PEx after inhalation challenge might to be due to the overall weaker response of inhaled LPS which primarily affects the larger airways. Compared with BAL, which always contains lining fluid from both peripheral lung and central airways, PEx analysis might add value as a selective and non-invasive method to investigate peripheral airway PL composition. TRIAL REGISTRATION: NCT03044327, first posted 07/02/2017.

Artificial neural network identification of exercise expiratory flow-limitation in adults.

Identification of ventilatory constraint is a key objective of clinical exercise testing. Expiratory flow-limitation (EFL) is a well-known type of ventilatory constraint. However, EFL is difficult to measure, and commercial metabolic carts do not readily identify or quantify EFL. Deep machine learning might provide a new approach for identifying EFL. The objective of this study was to determine if a convolutional neural network (CNN) could accurately identify EFL during exercise in adults in whom baseline airway function varied from normal to mildly obstructed. 2931 spontaneous exercise flow-volume loops (eFVL) were placed within the baseline maximal expiratory flow-volume curves (MEFV) from 22 adults (15 M, 7 F; age, 32 yrs) in whom lung function varied from normal to mildly obstructed. Each eFVL was coded as EFL or non-EFL, where EFL was defined by eFVLs with expired airflow meeting or exceeding the MEFV curve. A CNN with seven hidden layers and a 2-neuron softmax output layer was used to analyze the eFVLs. Three separate analyses were conducted: (1) all subjects (n = 2931 eFVLs, [GRALL]), (2) subjects with normal spirometry (n = 1921 eFVLs [GRNORM]), (3) subjects with mild airway obstruction (n = 1010 eFVLs, [GRLOW]). The final output of the CNN was the probability of EFL or non-EFL in each eFVL, which is considered EFL if the probability exceeds 0.5 or 50%. Baseline forced expiratory volume in 1 s/forced vital capacity was 0.77 (94% predicted) in GRALL, 0.83 (100% predicted) in GRNORM, and 0.69 (83% predicted) in GRLOW. CNN model accuracy was 90.6, 90.5, and 88.0% in GRALL, GRNORM and GRLOW, respectively. Negative predictive value (NPV) was higher than positive predictive value (PPV) in GRNORM (93.5 vs. 78.2% for NPV vs. PPV). In GRLOW, PPV was slightly higher than NPV (89.5 vs. 84.5% for PPV vs. NPV). A CNN performed very well at identifying eFVLs with EFL during exercise. These findings suggest that deep machine learning could become a viable tool for identifying ventilatory constraint during clinical exercise testing.

Role of the postinspiratory complex in regulating swallow-breathing coordination and other laryngeal behaviors.

Breathing needs to be tightly coordinated with upper airway behaviors, such as swallowing. Discoordination leads to aspiration pneumonia, the leading cause of death in neurodegenerative disease. Here, we study the role of the postinspiratory complex (PiCo) in coordinating breathing and swallowing. Using optogenetic approaches in freely breathing anesthetized ChATcre:Ai32, Vglut2cre:Ai32 and intersectional recombination of ChATcre:Vglut2FlpO:ChR2 mice reveals PiCo mediates airway protective behaviors. Activation of PiCo during inspiration or the beginning of postinspiration triggers swallow behavior in an all-or-nothing manner, while there is a higher probability for stimulating only laryngeal activation when activated further into expiration. Laryngeal activation is dependent on stimulation duration. Sufficient bilateral PiCo activation is necessary for preserving the physiological swallow motor sequence since activation of only a few PiCo neurons or unilateral activation leads to blurred upper airway behavioral responses. We believe PiCo acts as an interface between the swallow pattern generator and the preBötzinger complex to coordinate swallow and breathing. Investigating PiCo's role in swallow and laryngeal coordination will aid in understanding discoordination with breathing in neurological diseases.

Inspiratory-Expiratory Muscle Training Improved Respiratory Muscle Strength in Dialysis Patients: A Pilot Randomised Trial.

End-stage kidney disease (ESKD) exposes patients to progressive physical deconditioning involving the respiratory muscles. The aim of this pilot randomized controlled trial was to determine the feasibility and effectiveness of low-intensity respiratory muscle training (RMT) learned at the hospital and performed at home. A group of ESKD patients (n = 22) were randomized into RMT or usual care (control group, CON) in a 1:1 ratio. The respiratory training was performed at home with an inspiratory-expiratory system for a total of 5 min of breathing exercises in a precise rhythm (8 breaths per minute) interspersed with 1 min of rest, two times per day on nondialysis days for a total of 4 weeks, with the air resistance progressively increasing. Outcome measures were carried out every 4 weeks for 3 consecutive months, with the training executed from the 5th to the 8th week. Primary outcomes were maximal inspiratory and expiratory pressure (MIP, MEP), while secondary outcomes were lung capacity (FEV1, FVC, MVV). Nineteen patients without baseline between-group differences completed the trial (T: n = 10; Age: 63 ± 10; Males: n = 12). Both MIP and MEP significantly improved at the end of training in the T group only, with a significant difference of MEP of 23 cmH2O in favor of the RMT group (p = 0.008). No significant variations were obtained for FVC, FEV1 or MVV in either group, but there was a greater decreasing trend over time for the CON group, particularly for FVC (t = -2.00; p = 0.046). Low-fatiguing home-based RMT, with a simple device involving both inspiratory and expiratory muscles, may significantly increase respiratory muscle strength.