Healthy Breathing Research Articles

Comparative Study of Air Pollution with Ayurveda and its Management

Background Environmental pollution is increasing day by day and imposing severe and irreversible damage to the world. Air pollution is one of the biggest threats for the environment and affecting humans, animals, crops, cities, forests, aquatic ecosystems. Environmental Toxicology is the science concerned with study of harmful effects of various chemical, biological and physical agents on living organisms. In Ayurveda Charakacharya has briefly described the environmental health in chapter named Janpadodhvansaniyavimanam. Ayurveda has a dedicated branch called Agadatantra which deals with the toxicological aspects of living entities of environment. Aims and objectives We aim to relate and validate this knowledge pertaining to the air pollution. Material and method the references available regarding the air pollution in classical texts were studied and compared with the available information in the contemporary medical literatures. Signs of air pollution given in the text compared and studied with signs of Air pollution. Discussion Ayurveda terms of Vikrut and Prakrut Vayu described in Granthas are found to be relatable with contemporary findings. Conclusion Air pollution described in Ayurveda are very precise and explanatory. Management of air pollution with this ancient science are the new open windows for healthy air breathing for every living organism.

Co3O4/CQDs composite for selective ammonia detection in exhaled human breath analysis

Co3O4 was synthesized using electrospinning method and different amounts of carbon quantum dots (CQDs) were subsequently doped into the Co3O4 matrix via grinding, resulting in Co3O4/CQDs composite materials with various ratios. Gas sensing tests revealed that the resulting composite Co3O4/CQDs sensors exhibited excellent performance at a low temperature of 50°C, with outstanding selectivity towards ammonia gas and rapid response and recovery characteristics. Specifically, response and recovery time for 50 ppm ammonia gas were 14 s and 164 s, respectively. The outcomes of the exhalation simulation test utilizing sensors composed of this material demonstrate that the sensor's response to simulated chronic kidney disease breath closely approximates that of pure biological samples (1.08), exceeding the average response value of healthy exhaled breath samples (1.0). This demonstrates the sensor's capability to differentiate between healthy individuals and chronic kidney disease patients despite significant water vapor and carbon dioxide interference. This study provides valuable insights for the development of more efficient ammonia sensors and their role in the early detection of chronic kidney disease.

Morphological and molecular aspects of lung development.

Healthy breathing relies on normal morphological and functional development of the lung. This includes different prenatal and postnatal developmental stages. Depending on species and postnatal behavior as nest escapers or nest squatters, the duration of individual developmental phases and the state of differentiation of the lungs at birth differ. However, the sequence and morphology of the lung developmental stages are similar in all mammals, so knowledge gained from animal models about development-specific genetic control and regulatory mechanisms can be translated in principle to the human lung. Functional lung development comprises the maturation of the surfactant system, which is closely linked to the morphological development of the pulmonary acini. Although a number of reviews are found in the literature, a presentation that integrates the morphological and molecular regulatory mechanisms is missing. Therefore, the aim of this article was to provide an up-to-date comprehensive review of the main morphological steps and regulatory mechanisms of lung development, including clinical aspects related to developmental disorders.

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring.

Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors. Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers. A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals. The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

Defining a core breath profile for healthy, non-human primates

Non-human primates remain the most useful and reliable pre-clinical model for many human diseases. Primate breath profiles have previously distinguished healthy animals from diseased, including non-human primates. Breath collection is relatively non-invasive, so this motivated us to define a healthy baseline breath profile that could be used in studies evaluating disease, therapies, and vaccines in non-human primates. A pilot study, which enrolled 30 healthy macaques, was conducted. Macaque breath molecules were sampled into a Tedlar bag, concentrated onto a thermal desorption tube, then desorbed and analyzed by comprehensive two-dimensional gas chromatography-time of flight mass spectrometry. These breath samples contained 2,017 features, of which 113 molecules were present in all breath samples. The core breathprint was dominated by aliphatic hydrocarbons, aromatic compounds, and carbonyl compounds. The data were internally validated with additional breath samples from a subset of 19 of these non-human primates. A critical core consisting of 23 highly abundant and invariant molecules was identified as a pragmatic breathprint set, useful for future validation studies in healthy primates.

Breathing pattern changes in response to bronchoconstriction in physically active adults

Objectives To determine whether Opto-Electronic Plethysmography (OEP) can distinguish Exercise-Induced Bronchoconstriction (EIB) breathing patterns by comparing individuals with and without EIB, and between broncho-constriction and recovery. Breathing pattern was quantified in terms of regional contribution, breathing timing, and the phase between chest sub-compartments which indicates the synchronization in movement of the different sub-compartments. Methods Individuals (n = 47) reporting no respiratory symptoms and no history of any respiratory disease or disorder were assumed to have a healthy breathing pattern. Of 38 participants reporting respiratory symptoms during exercise, and/or a previous diagnosis of asthma or EIB, 10 participants had a positive result to the Eucapnic Voluntary Hyperpnea test, defined as a fall of at least 10% in FEV1 from baseline at two consecutive time points and were classified into the EIB group. OEP data was obtained from 89 markers and an 11-camera motion capture system operating at 100 Hz as follows: pre- and post-EVH challenge, and post-inhaler in participants who experienced a bronchoconstriction, and 2) for the healthy group during tidal breathing. Results RCpRCa-Phase (upper versus lower ribcage), RCaS-Phase (lower ribcage versus shoulders), and RCpS-Phase (upper ribcage versus shoulders) differed between bronchoconstriction and rest in athletes with EIB and rest in healthy participants (p < 0.05), in all cases indicating greater asynchrony post-bronchoconstriction, and later movement of the abdominal ribcage (RCa) post-bronchoconstriction. RCpS-Phase was different (p < 0.05) between all conditions (rest, post-bronchoconstriction, and post-inhaler) in EIB. Conclusions OEP can characterize and distinguish EIB-associated breathing patterns compared to rest and individuals without EIB at rest.

A Comparative Study between Conventional Diaphragmatic Breathing Exercise and Virtual Reality-based Diaphragmatic Breathing Exercise on Quality of Life in Chronic Obstructive Pulmonary Disease Patients: A Randomized Control Trial

Background and Aim: Breathing exercise is an exercise intended to promote effective and healthy breathing and breathing control. Virtual reality (VR) is defined as a computer-generated simulation, such as a set of images and sounds that represent a real place or situation that can be interacted within a seemingly real or physical way by a person using special electronic equipment. Breathing exercises with VR have shown benefits over conventional diaphragmatic breathing exercises in normal individuals in many new studies. We propose the use of a novel VR-based approach as a respiratory biofeedback to improve breathing patterns in chronic obstructive pulmonary disease (COPD) patients, decrease their work of breathing, and improve breathing control. Materials and Methods: In a randomized controlled study involving 72 COPD patients divided into two groups of 36 in each, the control and experimental groups performed a short 5 min breathing exercise for 6 consecutive days. In experimental group patients performed breathing exercise with VR goggles while in controls they performed without VR goggles. Results: Both the control and experimental groups showed improved changes in vitals and better quality of life, but not statistically as significant when compared to each other. Conclusion: Future studies need to address the long-term effects and stability of the results with a larger sample size, compare the approach with the existing treatments, and make the required smaller changes to the program for better outcomes.

An Individual Barrier Enclosure Actively Removing Aerosols for Airborne Isolation: A Vacuum Tent.

Aerosol barrier enclosure systems have been designed to prevent airborne contamination, but their safety has been questioned. A vacuum tent was designed with active continuous suctioning to minimize risks of aerosol dispersion. We tested its efficacy, risk of rebreathing, and usability on a bench, in healthy volunteers, and in an ergonomic clinical assessment study. First, a manikin with airway connected to a breathing simulator was placed inside the vacuum tent to generate active breathing, cough, and CO2 production; high-flow nasal cannula (HFNC) was applied in the manikin's nares. Negative pressure was applied in the vacuum tent's apex port using wall suction. Fluorescent microparticles were aerosolized in the vacuum tent for qualitative assessment. To quantify particles inside and around vacuum tent (aerosol retention), an airtight aerosol chamber with aerosolized latex microparticles was used. The vacuum tent was tested on healthy volunteers breathing with and without HFNC. Last, its usability was assessed in 5 subjects by 5 different anesthesiologists for delivery of full anesthesia, including intubation and extubation. The vacuum tent was adjusted until no leak was visualized using fluorescent particles. The efficacy in retaining microparticles was confirmed quantitatively. CO2 accumulation inside the vacuum tent showed an inverse correlation with the suction flow in all conditions (normal breathing and HFNC 30 or 60 L/min) in bench and healthy volunteers. Particle removal efficacy and safe breathing conditions (CO2, temperature) were reached when suctioning was at least 60 L/min or 20 L/min > HFNC flow. Five subjects were successfully intubated and anesthetized without ergonomic difficulties and with minimal interference with workflow and an excellent overall assessment by the anesthesiologists. The vacuum tent effectively minimized aerosol dispersion. Its continuous suction system set at a high suction flow was crucial to avoid the spread of aerosol particles and CO2 rebreathing.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry

BackgroundAcetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS. ResultsA system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature. SignificanceResults indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

AI‐Assisted Multimodal Breath Sensing System with Semiconductive Polymers for Accurate Monitoring of Ammonia Biomarkers

AbstractBreath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain‐responsive mesostructure. The miniaturized construction and module‐based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine‐learning algorithm based on K‐means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on‐body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers.

Hypoxic pulmonary vasoconstriction does not limit maximal exercise capacity in healthy volunteers breathing 12% oxygen at sea level.

Maximal exercise capacity is reduced at altitude or during hypoxia at sea level. It has been suggested that this might reflect increased right ventricular afterload due to hypoxic pulmonary vasoconstriction. We have shown previously that the pulmonary vascular sensitivity to hypoxia is enhanced by sustained isocapnic hypoxia, and inhibited by intravenous iron. In this study, we tested the hypothesis that elevated pulmonary artery pressure contributes to exercise limitation during acute hypoxia. Twelve healthy volunteers performed incremental exercise tests to exhaustion breathing 12% oxygen, before and after sustained (8-h) isocapnic hypoxia at sea level. Intravenous iron sucrose (n = 6) or saline placebo (n = 6) was administered immediately before the sustained hypoxia. In the placebo group, there was a substantial (12.6 ± 1.5 mmHg) rise in systolic pulmonary artery pressure (SPAP) during sustained hypoxia, but no associated fall in maximal exercise capacity breathing 12% oxygen. In the iron group, the rise in SPAP during sustained hypoxia was markedly reduced (3.4 ± 1.0 mmHg). There was a small rise in maximal exercise capacity following sustained hypoxia within the iron group, but no overall effect of iron, compared with saline. These results do not support the hypothesis that elevated SPAP inhibits maximal exercise capacity during acute hypoxia in healthy volunteers.

Toward the development of sensors for lung cancer: The adsorption of 1-propanol on hydrophobic zeolites.

A healthy breath is mainly composed of water, carbon dioxide, molecular nitrogen, and oxygen and it contains many species, in small quantities, which are related to the ambient atmosphere and the metabolism. The breath of a person affected by lung cancer presents a concentration of 1-propanol higher than usual. In this context, the development of specific sensors to detect 1-propanol from breath is of high interest. The amount of propanol usually detected on the breath is of few ppb; this small quantity is a handicap for a reliable diagnostic. This limitation can be overcome if the sensor is equipped with a pre-concentrator. Our studies aim to provide an efficient material playing this role. This will contribute to the development of reliable and easy to use lung cancer detectors. For this, we investigate the properties of a few hydrophobic porous materials (chabazite, silicalite-1, and dealuminated faujasite). Hydrophobic structures are used to avoid saturation of materials by the water present in the exhaled breath. Our experimental and simulation results suggest that silicalite -1 (MFI) is the most suitable structure to be used as a pre-concentrator.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

The effect of the gold nanoparticles and gold nano-clusters on the behavior of natural lung surfactant

One of the initial barriers encountered by the airborne nanoparticles (NPs) upon entry into the body is the lung surfactant (LS), which plays a vital role in maintaining the normal respiratory cycle of the lungs. Interaction between gold nanoparticles (AuNPs), which are commonly used in many areas, especially in pharmaceutical applications due to their functional features and LS has been evaluated by combining Langmuir Trough (2D) and Pendant-Drop tensiometer (3D) approaches. AuNPs were synthesized via Turkevich method and AuNP clusters were prepared by using dithiothreitol (DTT) with the different concentrations. Model LS was extracted from a bovine lung and brought into vesicular form after purification. Our results showed that AuNP-DTT clusters are able to penetrate into the LS monolayer and vesicles, change the normal behavior of air-buffer interface, organization of the molecules and phase behavior of the system. In 2D studies, it is seen that the interaction of AuNPs and AuNP-DTT clusters decreases the surface pressure of phase transition region which indicates decreasing the elasticity with a similar manner for first 3 h. In the longer periods, the monolayer presented an increased resistance to compression, indicating the low compressibility of monolayer. After 7 h interaction with AuNP-DTT clusters, at the same surface area (48 cm2), the π of LS films can just reach ∼30 mN·m−1 instead of ∼45 mN·m−1 which proves the induction of the isotherm to form a more disordered structure with lower cohesion between the molecules. In 3D studies, it is found that there becomes a competitive adsorption between LS vesicles and AuNP-DTT clusters in the long-term interaction which may enhance the pulmonary toxicity of the inhaled nanostructure. The ultimate insight provided by this study is to evaluate the toxicity of the interaction of asymmetrical NPs on biological barriers with a realistic approach. As a result of our investigations, it is proved that long-term exposure of anisotropic clusters can cause potential adverse effects on the healthy environment and breathing cycle of lungs more than smaller individual counterparts with the same concentration.

DeepBreath—automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] ± 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 ± 0.10), wheezing disorders (AUROC 0.91 ± 0.03), and bronchiolitis (AUROC 0.94 ± 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology.

Breathing Pattern Disorders Distinguished from Healthy Breathing Patterns Using Optoelectronic Plethysmography.

There is no gold standard diagnostic method for breathing pattern disorders (BPD) which is commonly diagnosed through the exclusion of other pathologies. Optoelectronic plethysmography (OEP) is a 3D motion capture technique that provides a comprehensive noninvasive assessment of chest wall during rest and exercise. The purpose of this study was to determine if OEP can distinguish between active individuals classified with and without BPD at rest and during exercise. Forty-seven individuals with a healthy breathing pattern (HBP) and twenty-six individuals with a BPD performed a submaximal exercise challenge. OEP measured the movement of the chest wall through the calculation of timing, percentage contribution, and phase angle breathing pattern variables. A mixed model repeated measures ANOVA analysed the OEP variables between the groups classified as HBP and BPD at rest, during exercise, and after recovery. At rest, regional contribution variables including ribcage percentage contribution (HBP: 71% and BPD: 69%), abdominal ribcage contribution (HBP: 13% and BPD: 11%), abdomen percentage contribution (HBP: 29% and BPD: 31%), and ribcage and abdomen volume index (HPB: 2.5 and BPD: 2.2) were significantly (p < 0.05) different between groups. During exercise, BPD displayed significantly (p < 0.05) more asynchrony between various thoracic compartments including the ribcage and abdomen phase angle (HBP: -1.9 and BPD: -2.7), pulmonary ribcage and abdomen phase angle (HBP: -0.5 and BPD, 0.5), abdominal ribcage and shoulders phase angle (HBP: -0.3 and BPD: 0.6), and pulmonary ribcage and shoulders phase angle (HBP: 0.2 and BPD: 0.6). Additionally, the novel variables inhale deviation (HBP: 8.8% and BPD: 19.7%) and exhale deviation (HBP: -10.9% and BPD: -17.6%) were also significantly (p < 0.05) different between the groups during high intensity exercise. Regional contribution and phase angles measured via OEP can distinguish BPD from HBP at rest and during exercise. Characteristics of BPD include asynchronous and thoracic dominant breathing patterns that could form part of future objective criteria for the diagnosis of BPD.

Comparing heart rate variability biofeedback and simple paced breathing to inform the design of guided breathing technologies

IntroductionA goal of inbodied interaction is to explore how tools can be designed to provide external interactions that support our internal processes. One process that often suffers from our external interactions with modern computing technology is our breathing. Because of the ergonomics and low-grade-but-frequent stress associated with computer work, many people adopt a short, shallow breathing pattern that is known to have a negative effect on other parts of our physiology. Breathing guides are tools that help people match their breathing patterns to an external (most often visual) cue to practice healthy breathing exercises.However, there are two leading protocols for how breathing cues are offered by breathing guides used in non-clinical settings: simple paced breathing (SPB) and Heart Rate Variability Biofeedback (HRV-b). Although these protocols have separately been demonstrated to be effective, they differ substantially in their complexity and design. Paced breathing is a simpler protocol where a user is asked to match their breathing pattern with a cue paced at a predetermined rate and is simple enough to be completed as a secondary task during other activities. HRV-b, on the other hand, provides adaptive, real-time guidance derived from heart rate variability, a physiological signal that can be sensed through a wearable device. Although the benefits of these two protocols have been well established in clinical contexts, designers of guided breathing technology have little information about whether one is better than the other for non-clinical use.MethodsTo address this important gap in knowledge, we conducted the first comparative study of these two leading protocols in the context of end-user applications. In our N=28 between-subject design, participants were trained in either SPB or HRV-b and then completed a 10-minute session following their training protocol. Breathing rates and heart rate variability scores were recorded and compared between groups.Results and discussionOur findings indicate that the exercises did not significantly differ in their immediate outcomes – both resulted in significantly slower breathing rates than their baseline and both provided similar relative increases in HRV. Therefore, there were no observed differences in the acute physiological effects when using either SPB or HRV-b. Our paper contributes new findings suggesting that simple paced breathing – a straightforward, intuitive, and easy-to-design breathing exercise – provides the same immediate benefits as HRV-b, but without its added design complexities.

Effects of Hyperoxia and Hyperoxic Oscillations on the Proteome of Murine Lung Microvascular Endothelium.

Patients presenting with insufficient tissue oxygenation and impaired lung function as in acute respiratory distress syndrome (ARDS) frequently require mechanical ventilation with supplemental oxygen. Despite the lung being used to experiencing the highest partial pressure of oxygen during healthy breathing, the organ is susceptible to oxygen-induced injury at supraphysiological concentrations. Hyperoxia-induced lung injury (HALI) has been regarded as a second hit to pre-existing lung injury and ventilator-induced lung injury (VILI) attributed to oxidative stress. The injured lung has a tendency to form atelectasis, a cyclic collapse and reopening of alveoli. The affected lung areas experience oxygen conditions that oscillate between hyperoxia and hypoxia rather than remaining in a constant hyperoxic state. Mechanisms of HALI have been investigated in many animal models previously. These studies provided insights into the effects of hyperoxia on the whole organism. However, cell type-specific responses have not been dissected in detail, but are necessary for a complete mechanistic understanding of ongoing pathological processes. In our study, we investigated the effects of constant and intermittent hyperoxia on the lung endothelium from a mouse by an in vitro proteomic approach. We demonstrate that these oxygen conditions have characteristic effects on the pulmonary endothelial proteome that underlie the physiological (patho)mechanisms.

COVID-19 Preventive Measure: A Study on "MASK-A Boon or a Bane".

Mask wearing can restrict the spread of respiratory viral transmission during the third wave of the COVID-19 pandemic. Globally, governments have emphasized its use in workplaces and public areas to prevent the transmission routes of corona virus. In spite of the current awareness in the general population, the stringency of wearing the mask lies as an individual's choices. Subject and Methods. This research work discusses available studies on the types and comparison of masks in the market for use. It includes a short survey conducted with 1,173 anonymized healthy participants primarily devoid of comorbidities. The survey includes the effects of mask wearing, while outdoor with minimal activities like walking and with mild activities like jogging and stretching. Our research further discusses various health effects of wearing a mask, including cardiac output, hypoxemia, hypoxia, and dyspnoea, and how such situations that pose a threat can be consciously avoided. It was found that the majority of people use cloth/fabric reusable mask. There remains scope for better-designed masks and improving health in the mass population by inculcating healthy breathing habits and other relevant exercises that can help people cope up better in this fight against the deadly virus at a larger scale. For most of the survey questions, there was significant correlation between gender and the related responses as there was no significantly observable difference in the nonparametric, unpaired analyses of responses. The main objective of this research work is to initiate more discussions and enhance awareness in natural ways of staying healthy during the pandemic emphasizing mask use. Further progress in this aspect remains a whole new area for future exploration.

Y and Al co-doped ZnO-nanopowder based ultrasensitive trace ethanol sensor: A potential breath analyzer for fatty liver disease and drunken driving detection

Excess ethanol in exhaled breath can be an indicator of intoxication and a biomarker for fatty liver disease. Herein, we report for the first time a highly sensitive and selective Al and Y co-doped ZnO nanopowder sensor for the detection of trace ethanol in exhaled breath. The nanopowder was synthesized by a facile sol-gel method and characterized by multiple sophisticated techniques, viz. XRD, XPS, FTIR, FESEM, EDX, BET surface area analysis, UV-Vis spectroscopy, photoluminescence, infrared imaging, and current-voltage (I-V) measurement. The developed sensors, especially 5% Y and 1% Al co-doped ZnO exhibited excellent n-type response to 1 ppm ethanol (62.8%). Further, appreciable selectivity to trace ethanol with respect to other interfering gases, viz. acetone, ammonia, CO, NO, NO2, formalin, acetylene, and saturated moisture was observed. Additionally, ultrafast response (0.77 s) and recovery (8.1 s) time, good repeatability, and long-term stability for at least 10 months were observed. Satisfactory resolution between healthy breath, and simulated breath with ethanol vapor excess was obtained. The optimized sensor could be very suitable for both the detection of liver problem as well as commercial breath ethanol analyzer for drunken driving detection.