Breath Concentration Research Articles

Variation among individual beef cattle in methane-to-carbon dioxide ratio measured under on-farm conditions using the sniffer method.

This study proposed a method for measuring the methane (CH4 )/carbon dioxide (CO2 ) ratio from individual beef cattle under on-farm conditions and estimated the variance components of the CH4 /CO2 ratio. Gas measurements were conducted using 166 Japanese Black cattle group-housed in pens equipped with individual feed bins. The gas containing the animal's breath was measured individually after concentrate feeding by covering the feed bin with a sheet with sampling inlets. Measurements were performed six times (three consecutive days, twice daily) per individual. Most of the sampled gas contained more than 1000 ppm of the mean background-corrected CO2 , suggesting that the method proposed in this study successfully collected sufficient breath concentration to accurately measure the CH4 /CO2 ratio. The between-animal variance accounted for 31.7% of the total variance in the CH4 /CO2 ratio. The results showed that the gas collection method proposed in this study could be a useful tool for measuring the CH4 /CO2 ratio under on-farm conditions. The variance component obtained from this study will help to establish protocols for generating data for genetic evaluation and performing dietary experiments with sufficient statistical power.

Octane in exhaled breath to diagnose acute respiratory distress syndrome in invasively ventilated intensive care unit patients.

The concentration of exhaled octane has been postulated as a reliable biomarker for acute respiratory distress syndrome (ARDS) using metabolomics analysis with gas chromatography and mass spectrometry (GC-MS). A point-of-care (POC) breath test was developed in recent years to accurately measure octane at the bedside. The aim of the present study was to validate the diagnostic accuracy of exhaled octane for ARDS using a POC breath test in invasively ventilated intensive care unit (ICU) patients. This was an observational cohort study of consecutive patients receiving invasive ventilation for at least 24 h, recruited in two university ICUs. GC-MS and POC breath tests were used to quantify the exhaled octane concentration. ARDS was assessed by three experts following the Berlin definition and used as the reference standard. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy. 519 patients were included and 190 (37%) fulfilled the criteria for ARDS. The median (interquartile range) concentration of octane using the POC breath test was not significantly different between patients with ARDS (0.14 (0.05-0.37) ppb) and without ARDS (0.11 (0.06-0.26) ppb; p=0.64). The AUC for ARDS based on the octane concentration in exhaled breath using the POC breath test was 0.52 (95% CI 0.46-0.57). Analysis of exhaled octane with GC-MS showed similar results. Octane in exhaled breath has insufficient diagnostic accuracy for ARDS. This disqualifies the use of octane as a biomarker in the diagnosis of ARDS and challenges most of the research performed up to now in the field of exhaled breath metabolomics.

Low-Order Mechanistic Models for Volumetric and Temporal Capnography: Development, Validation, and Application.

The airflow model's single "alveolar" compartment has compliance and inertance, and feeds a resistive unperfused airway comprising a laminar-flow region followed by a turbulent-mixing region. The gas-mixing model tracks mixing-region CO 2 concentration, which is fitted breath-by-breath to the measured capnogram, yielding estimates of model parameters that characterize the capnogram. For the 17 examined records (310 breaths) of airflow, airway pressure and Tcap from ventilated adult patients, the models fit closely (mean rmse 1% of end-tidal cŞoncentration on Vcap; 1.7% on Tcap). The associated parameters (4 for Vcap, 5 for Tcap) for each exhalation, and airflow parameters for the corresponding forced inhalation, are robustly estimated, and consonant with each other and literature values. The models also allow, using Tcap alone, estimation of the entire exhaled airflow waveform to within a scaling. This suggests new Tcap-based tests, analogous to spirometry but with normal breathing, for discriminating chronic obstructive pulmonary disease (COPD) from congestive heart failure (CHF). A version trained on 15 exhalations from each of 24 COPD/24 CHF Tcap records from one hospital, then tested 100 times with 15 random exhalations from each of 27 COPD/31 CHF Tcap records at another, gave mean accuracy 80.6% (stdev 2.1%). Another version, tested on 29 COPD/32 CHF, yielded AUROC 0.84. Our mechanistic models closely fit Tcap and Vcap measurements, and yield subject-specific parameter estimates. This can inform cardiorespiratory care.

Quartz tuning fork (QTF) coating enhanced Mid-Infrared laser Induced-Thermoacoustic spectroscopy (LITES) for human exhaled methane detection

Human exhaled methane (CH4) has essential significance in detecting intestinal flora. In this study, a high-precision quartz tuning fork (QTF) based light-induced thermoelastic spectroscopy (LITES) system was developed, a mid-infrared interband cascade laser (ICL) and a QTF detector coated with polydimethylsiloxane (PDMS) and reduced graphene oxide (rGO) were employed for the CH4 detection. Experimental results indicate that the composite coating resulted in a 3-fold increase in 2f signal amplitude and signal-to-noise ratio (SNR), when compared to the bare QTF detector. The system has achieved a minimum detection limit (MDL) of 58.65 ppbv (part per billion volume) and a normalized noise equivalent absorption coefficient (NNEA) of 2.069 × 10−10 cm−1·W·Hz−1/2, Allan deviation analysis indicates that the optimal measurement accuracy is 36.85 ppbv. 24 healthy college students and 10 overweight/obese students were recruited to measure the CH4 concentration in the exhaled breath. The results show that exhaled CH4 concentrations of healthy individuals range from 0.5 to 3 ppmv (part per million volume), with slightly higher levels in males than females, and higher levels in the obese individuals than those in the normal individuals. The mid-infrared LITES CH4 measurement system holds promise as a new non-invasive method for assessing human metabolic states, with potential applications in medical and life sciences.

1-Year Prospective Study of the Relationship of Serial Exhaled Nitric Oxide Level and Asthma Control.

Previous studies found that the fractional nitric oxide concentration in exhaled breath (FeNO) levels in healthy Chinese adults was higher than in White adults. More understanding of serial changes of FeNO levels with asthma control in a real-life clinical setting would be important to explore the utility of this biomarker in routine asthma management. This study assessed the FeNO levels of Chinese asthma subjects with different levels of asthma control and the serial changes with respect to the changes in asthma control over 1 year. A 12-month prospective study (subjects recruited between November 2019 and January 2021) with serial measurement of FeNO levels at baseline, 4, 8 and 12 months. Asthma control was assessed by the Global Initiative for Asthma classification, Asthma Control Test (ACT) and Asthma Control Questionnaire (ACQ). Altogether, 136 subjects (mean age 51.51±15.09 years, 46[33.8%] male) had successful baseline FeNO measurements. At baseline, the FeNO levels did not show a statistically significant difference for controlled, partly controlled and uncontrolled asthma according to GINA classification, ACT and ACQ. FeNO levels decreased with improving asthma control and stayed at similar levels with unchanged or worsening asthma control for all subjects. For subjects with baseline blood eosinophil levels ≥300 cells/µL(n=59), FeNO levels decreased with improving asthma control, stayed similar without change for asthma control and increased with worsening asthma control. Receiver operating characteristic (ROC) analysis with the highest area under curve (AUC) for changes in FeNO levels for improving asthma control was between ≤ -10 to -25 ppb at various time points in the 12-month study. Changes in FeNO levels over time were associated with changes in clinical asthma control, particularly in those with higher blood eosinophil count and are likely more useful than a single time point measurement in managing asthma.

ZIF-8/MWCNT-nanocomposite based-resistive sensor for highly selective detection of acetone in parts-per-billion: Potential noninvasive diagnosis of diabetes

Zeolitic imidazolate framework-8 (ZIF-8) is a promising metal-organic framework for gas sensing applications in humid conditions due to its inherent hydrophobicity. However, its low sorption capacity for alcohols and acetone at room temperature and atmospheric pressure limits its applications. To overcome this, a novel composite of ZIF-8 and bare multiwall-carbon-nanotubes (MWCNTs) was developed, significantly enhancing its sorption capacity for alcohols and acetone. A ZIF-8/MWCNT-coated quartz crystal microbalance (QCM) sensor successfully detected these volatile organic compounds (VOCs) at 0.62 parts-per-million (ppm) under ambient conditions, a marked improvement over ZIF-8 alone. However, selectivity towards acetone, a crucial biomarker for diabetes, is lacking when other VOCs are present. This study investigates the effect of different sensing platforms with varying response mechanisms on achieving high selectivity for acetone using a ZIF-8/MWCNT-coated QCM and a chemiresistive sensor. The chemiresistive-sensor demonstrated excellent acetone detection performance, with high selectivity, low humidity response, good repeatability, complete reversibility, and rapid response. Remarkably, it achieved a limit of detection of 1.7 parts-per-billion (ppb) at ambient conditions, three orders of magnitude lower than the concentration in the exhaled breath of diabetic patients (1.8 ppm). This breakthrough highlights the high potential of the sensor for diabetes diagnosis and monitoring through breath analysis.

Biomarkers of particulate matter exposure in patients with chronic obstructive pulmonary disease: a systematic review.

In recent years, ambient particulate matter (PM) exposure has been strongly linked with health effects. Elevated levels of PM in polluted air have been correlated with the onset and development of chronic obstructive pulmonary disease (COPD). This systematic review was conducted to evaluate biomarkers that could reflect the effects of PM exposure in patients with COPD. We performed a systematic review of studies published on biomarkers associated with PM exposure in patients with COPD between January 01, 2012 and June 30, 2022 in PubMed/MEDLINE, EMBASE, and Cochrane databases. Studies that included data on biomarkers with COPD exposed PM were eligible for inclusion. Biomarkers were classified into 4 groups according to their mechanisms. Of the 105 studies identified, 22 were included in this study. Nearly 50 biomarkers have been proposed in the studies included in this review, and the most studied in relation to PM are several interleukins. Various mechanisms have been reported by which PM induces and aggravates COPD. Six studies related to oxidative stress, one related to direct effect of innate and adaptive immune systems, 16 associated with genetic regulation of inflammation, and two related to epigenetic regulation of physiology and susceptibility were found. Biomarkers related to these mechanisms were detected in serum, sputum, urine, exhaled breath concentration (EBC), and showed various correlations with PM in COPD. Various biomarkers have shown potential in predicting the extent of PM exposure in COPD patients. Future studies are needed to establish recommendations for regulation to reduce airborne PM, which could be used to develop strategies for prevention and management of environmental respiratory diseases.

Study of Changes over Time in Breath Alcohol Concentration and Brain Function Induced by Low-Impact Drinking

Currently, Japan’s Road Traffic Act uses the breath alcohol concentration (BrAC), which is the concentration of alcohol in 1 L of breath, to enforce the law. However, 400 drunk driving accidents occurred in 2020 when the BrAC was below 0.15 mg/L, which is legal limit in Japan, and cognitive function may be impaired at this concentration. Therefore, we investigated the effects of alcohol consumption at levels below a BrAC of 0.15 mg/L on cognitive function and its change over time using P300, a type of event-related potential, and the concomitant negative variation (CNV). Beer with a 5% alcohol content was used as the drinking load, and the amount of alcohol consumed was determined using a derivation formula that varied according to the subject’s body weight. Electroencephalogram (EEG) measurements were obtained five times before drinking and 10, 30, 50, and 70 min after, and participants were given a go/no-go task to induce event-related potentials during the measurements. The BrAC was measured immediately before the EEG measurement was performed. The results revealed that the alcohol concentration in the exhaled breath increased significantly (p<0.05) at 10, 30, and 50 min after drinking compared with immediately before. In addition, the P300 latency was significantly prolonged (p<0.05), and the early and late CNV amplitudes were significantly increased (p<0.05) in the event-related potentials during these time periods. Moreover, no significant difference was observed in expiratory alcohol concentration at 70 min post-drinking, but significant changes in event-related potentials still occurred. Therefore, our results suggest that cognitive function is impaired in patients with low levels of alcohol consumption, when the BrAC is below the legal limit, and even when the BrAC recovers after the end of alcohol consumption.

The effect of activity and face masks on exhaled particles in children.

Despite the high burden of respiratory infections among children, the production of exhaled particles during common activities and the efficacy of face masks in children have not been sufficiently studied. To determine the effect of type of activity and mask usage on exhaled particle production in children. Healthy children were asked to perform activities that ranged in intensity (breathing quietly, speaking, singing, coughing, and sneezing) while wearing no mask, a cloth mask, or a surgical mask. The concentration and size of exhaled particles were assessed during each activity. Twenty-three children were enrolled in the study. Average exhaled particle concentration increased by intensity of activity, with the lowest particle concentration during tidal breathing (1.285 particles/cm3 [95% CI 0.943, 1.627]) and highest particle concentration during sneezing (5.183 particles/cm3 [95% CI 1.911, 8.455]). High-intensity activities were associated with an increase primarily in the respirable size (≤ 5 µm) particle fraction. Surgical and cloth masks were associated with lower average particle concentration compared to no mask (P = 0.026 for sneezing). Surgical masks outperformed cloth masks across all activities, especially within the respirable size fraction. In a multivariable linear regression model, we observed significant effect modification of activity by age and by mask type. Similar to adults, children produce exhaled particles that vary in size and concentration across a range of activities. Production of respirable size fraction particles (≤ 5 µm), the dominant mode of transmission of many respiratory viruses, increases significantly with coughing and sneezing and is most effectively reduced by wearing surgical face masks.

Early Chronic Pulmonary Diseases Prediction System

Smart breath analyzers are developed as sensing terminals of a telemedicine architecture devoted to remote monitoring of patients suffering from Chronic Obstructive Pulmonary Disease (COPD). The devices based on different sensors CO2, smoke sensor and Volatile Organic Compounds (VOCs), relative humidity and temperature (R.H. & T) and heart rate, respiration sensors) monitor the breath air exhaled into the expiratory line of the bi-tube patient breathing circuit. The sensor raw signals are transmitted to national health service units / Doctor by TCP/IP communication through a cloud remote platform using IoT module space.The work is a proof-of-concept of a sensors-based IoT system with the perspective to predict continuously the effectiveness of monitoring and/or any state of exacerbation of the disease requiring healthcare. By a gas-mixing bench towards gas, smoke in environment and acetone concentrations in exhaled breath collected in a sampling bag were carried out to test the realized prototypes. The paper reports on the longitudinal use of health tags among high- risk COPD patients in the indoor & outdoor environment using PIC16F877A microcontroller. Key Word: Chronic Obstructive Pulmonary Disease (COPD); exacerbation ; Buprenorphine; Nalbuphine; Postoperative.

A Strategy to Enhance Humidity Robustness of p‐Type CuO Sensors for Breath Acetone Quantification

Low‐cost metal oxide sensors are highly attractive for emerging applications such as breath analysis. Particularly promising are p‐type sensors that can operate at low temperatures, a key requirement for compact and low‐power devices. To date, however, these sensors lack sufficient sensitivity, selectivity, and humidity robustness to fulfil stringent requirements faced in real applications. Herein, a flame‐made and low‐power sensor (operated at 150 °C) that consists of CeO2‐decorated CuO nanoparticles is introduced, as determined by X‐ray diffraction and X‐ray photoelectron spectroscopy analysis. Most remarkably, this sensor features excellent robustness to 10–90% relative humidity. This is attributed to the presence of CeO2 nanoclusters, which may act by scavenging OH− and allow the readsorption of oxygen onto the CuO surface. To demonstrate its immediate impact, this sensor is investigated for the detection of acetone, a biomarker for fat burning. It detects acetone with high sensitivity (i.e., 50 ppb) and features excellent acetone selectivity (>9.8) toward key inorganic interferants (i.e., NH3, H2, and CO). Most importantly, the CeO2–CuO sensor accurately quantifies acetone concentrations in the exhaled breath of 16 volunteers (bias and precision of 90 and 457 ppb). As a result, it is attractive for low‐power and humidity robust detection of volatiles in breath analysis.

Metabolomics-based Sleepiness Markers for Risk Prevention and Traffic Safety (ME-SMART): a monocentric, controlled, randomized, crossover trial

BackgroundToo little sleep and the consequences thereof are a heavy burden in modern societies. In contrast to alcohol or illicit drug use, there are no quick roadside or workplace tests for objective biomarkers for sleepiness. We hypothesize that changes in physiological functions (such as sleep–wake regulation) are reflected in changes of endogenous metabolism and should therefore be detectable as a change in metabolic profiles. This study will allow for creating a reliable and objective panel of candidate biomarkers being indicative for sleepiness and its behavioral outcomes.MethodsThis is a monocentric, controlled, randomized, crossover, clinical study to detect potential biomarkers. Each of the anticipated 24 participants will be allocated in randomized order to each of the three study arms (control, sleep restriction, and sleep deprivation). These only differ in the amount of hours slept per night. In the control condition, participants will adhere to a 16/8 h wake/sleep regime. In both sleep restriction and sleep deprivation conditions, participants will accumulate a total sleep deficit of 8 h, achieved by different wake/sleep regimes that simulate real-life scenarios. The primary outcome is changes in the metabolic profile (i.e., metabolome) in oral fluid. Secondary outcome measures will include driving performance, psychomotor vigilance test, d2 Test of Attention, visual attention test, subjective (situational) sleepiness, electroencephalographic changes, behavioral markers of sleepiness, changes in metabolite concentrations in exhaled breath and finger sweat, and correlation of metabolic changes among biological matrices.DiscussionThis is the first trial of its kind that investigates complete metabolic profiles combined with performance monitoring in humans over a multi-day period involving different sleep–wake schedules. Hereby, we aim to establish a candidate biomarker panel being indicative for sleepiness and its behavioral outcomes. To date, there are no robust and easily accessible biomarkers for the detection of sleepiness, even though the vast damage on society is well known. Thus, our findings will be of high value for many related disciplines.Trial registrationClinicalTrials.gov Identifier NCT05585515, released on 18.10.2022; Swiss National Clinical Trial Portal SNCTP000005089, registered on 12 August 2022.

Handheld device quantifies breath acetone for real-life metabolic health monitoring.

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm3), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

A Bilayer SnO2/MoS2-Coated Evanescent Wave Fiber Optic Sensor for Acetone Detection-An Experimental Study.

The need for sensors that measure the acetone content of exhaled breath for diabetes severity has recently increased. Clinical researchers have reported less than 0.8 ppm acetone concentration in the exhaled breath of an average individual, while that for a diabetic patient is higher than 1.8 ppm. This work reports the development of two sets of evanescent wave-based fiber optic sensor coated with SnO2 thin film and bilayer of SnO2/MoS2 to detect different acetone concentrations (0–250 ppm). In each set, we have studied the effect of clad thickness (chemical etch time 5min, 10 min, 15 min, 25 min, 40 min, and complete clad removal) to optimize the clad thickness for a better response. In Set 1, SnO2 thin film was used as the sensing layer, while in Set 2 a bilayer of SnO2 thin film/ MoS2 was used. Enhanced sensor response of ~23.5% is observed in the Set 2 probe with a response and recovery time of ~14 s/~17 s. A SnO2/MoS2-coated sensor prototype is developed using LEDs of different wavelength and intensity detector; its potential to detect different concentrations of acetone is tested. X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Ultraviolet (UV) Spectroscopy, and Ellipsometry were used to study the structural, morphological and optical properties of the sensing layers. The present study indicates that the SnO2/MoS2-coated sensor has the potential to create a handheld sensor system for monitoring diabetes.

Au modified PrFeO3 with hollow tubular structure can be efficient sensing material for H2S detection.

The H2S concentration in exhaled breath increases marginally with the progress of periodontal disease, and H2S is considered to be one of the most important gases related to meat and seafood decomposition; however, the concentration of H2S is low and difficult to detect in such scenarios. In this study, Au–PrFeO3 nanocrystalline powders with high specific surface areas and porosities were prepared using an electrospinning method. Our experimental results show that loading Au on the material provides an effective way to increase its gas sensitivity. Au doping can decrease the material’s resistance by adjusting its energy band, allowing more oxygen ions to be adsorbed onto the material’s surface due to a spillover effect. Compared with pure PrFeO3, the response of 3 wt% Au–PrFeO3 is improved by more than 10 times, and the response time is more than 10 s shorter. In addition, the concentration of H2S due to the decomposition of shrimp was detected using the designed gas sensor, where the error was less than 15%, compared with that obtained using a GC-MS method. This study fully demonstrates the potential of Au–PrFeO3 for H2S concentration detection.

A portable acetone detector based on SmFeO3 can pre-diagnose diabetes through breath analysis

Breath analysis using gas sensors is becoming one of the important supplementing ways for an early diagnosis of diseases. The feasibility of detecting acetone concentration in exhaled breath using a SmFeO3-based sensor has been studied herein. The detection limits of acetone using a SmFeO3-based sensor are extremely low (1.56–0.01 ppm; 1.87–0.02 ppm; 2.39–0.05 ppm; 3.22–0.1 ppm; 4.1–0.2 ppm; 4.79–0.5 ppm; 5.92–1 ppm). To remove the effects of carbon dioxide and relative humidity (RH), a mathematical model between the response and acetone concentration, carbon dioxide concentration and RH in a person’s exhaled breath is established via machine learning. Volunteer studies have proved the accuracy of this mathematical model. Compare with the acetone concentration obtained by GC-MS method, the accuracy of the proposed method exceeded 85 %. Finally, a portable acetone detector based on the SmFeO3 sensor is designed and realized to monitor the dynamic change in acetone concentration in the exhaled breath of volunteers before and after meals, and the experimental results have proved that the method based on the gas sensor and Machine learning (ML) is feasible and effective in accurately detecting acetone concentration in persons’ exhaled breath.

Airway inflammation in adolescents and elderly women: Chronic air pollution exposure and polygenic susceptibility

Background and aimThe fractional exhaled nitric oxide (FeNO) concentration in the exhaled breath is a biomarker for eosinophilic airway inflammation. We explored the interplay between chronic air pollution exposure and polygenic susceptibility to airway inflammation at different critical age stages. MethodsAdolescents (15 yr) enrolled in the GINIplus/LISA birth cohorts (n = 2434) and 220 elderly women (75 yr on average) enrolled in the SALIA cohort with FeNO measurements available were investigated. Environmental main effects of the mean of ESCAPE land-use regression air pollutant concentrations within a time window of 15 years and main effects of the polygenic risk scores (PRS) using internal weights from elastic net regression of genome-wide derived single nucleotide polymorphisms were investigated. Furthermore, we examined gene-environment interaction (GxE) effects on natural log-transformed FeNO levels by adjusted linear regression models. ResultsWhile we observed no significant environmental and polygenic main effects on airway inflammation in either age group, we found robust harmful effects of chronic nitrogen dioxide (NO2) exposure in the GxE models for elderly women (16.2 % increase in FeNO, p-value = 0.027). Stratified analyses found GxE effects between the PRS and chronic NO2 exposure in never-smoker elderly women and in adolescents without any inflammatory respiratory conditions. ConclusionsFeNO measurement is a useful biomarker to detect higher risk of NO2-induced eosinophilic airway inflammation in the elderly. There was limited evidence for GxE effects on airway inflammation in adolescents or the elderly. Further GxE studies in subpopulations should be conducted to investigate the assumption that susceptibility to airway inflammation differs between age stages.

Effect of airway clearance therapies on mucociliary clearance in adults with cystic fibrosis: A randomized controlled trial.

Cystic fibrosis (CF) is an inherited disorder causing impaired mucociliary clearance within the respiratory tract, and is associated with bronchiectasis, chronic respiratory infections, and early death. Airway clearance therapies have long been a cornerstone of management of individuals with CF, although evidence supporting their use is lacking. We designed a randomized controlled trial to quantitatively compare the effects of different forms of airway clearance on mucociliary clearance. Three different physiotherapy methods to augment cough-clearance were studied in addition to cough-clearance alone: high-frequency chest-wall oscillating vest, oscillatory positive expiratory pressure, and whole-body vibration. We used gamma scintigraphy after inhalation of radiolabeled particles to quantify mucus clearance before, during, and after physiotherapy. As secondary endpoints, we measured concentrations of small molecules in exhaled breath that may impact mucus clearance. Ten subjects were enrolled and completed study procedures. No differences were identified between any method of airway clearance, including cough clearance alone. We did identify changes in certain small molecule concentrations in exhaled breath following airway clearance. Due to the limitations of this study, we do not believe the negative results suggest a change in clinical practice with regard to airway clearance. Findings pertaining to small molecules in exhaled breath may serve as future opportunities for study.

ANN based approach for selective detection of breath acetone by using hybrid GO-FET sensor array

This research used hybrid graphene oxide (GO) field effect transistors (FETs) based sensor array to design an electronic nose (e-nose) for identifying exhaled breath acetone to diagnose diabetes mellitus through noninvasive route. Six back gated FET sensors were fabricated with hybrid channel of GO, WO3 and noble metals (Au, Pd and Pt) nanoparticles. The experiment was carried out by using four distinct forms of synthetic breath, each with a different level of interference. Linear discriminant analysis (LDA) and artificial neural networks (ANN) were utilized to classify and analyze the sensor response vector. In contrast, partial least square (PLS) and multiple linear regression (MLR) were used to evaluate the exact acetone concentration in synthetic breath. First, LDA was used to lower the dimensionality of the response vector, which was then provided as an input to the ANN model. ANN was performed with ten perceptrons model in the hidden layer and highest accuracy of 99.1% was achieved. Additionally, by using the loading plot of PLS, three sensors (Pt/WO3/GO, Pd/WO3/GO, and WO3/GO) had the ample use to predict the concentration of breath acetone. Moreover, the MLR approach with correlation coefficient (R2) of 0.9572 and root mean square error (RMSE) of 5.63% were used for obtaining the exact concentration of acetone. Consequently, e-nose with matrix of hybrid GO-FET sensors and pattern recognition algorithms (LDA, ANN, PLS and MLR) exhibited considerable ability in selective detection of acetone in synthetic breath.

Parasympathetic cardiac control during attentional focus and worry in major depressive disorder

Worry, which refers to uncontrollable chains of thoughts and images with negative affective load, is a transdiagnostic symptom of various mental disorders including major depressive disorder (MDD). Current theories relate excessive worry to deficient top-down control of automatically occurring perseverative thinking. This study investigated parasympathetic cardiac control in MDD in the context of worry and cognitive control. Heart rate variability (HRV) was recorded, in the high frequency (HF) and low frequency (LF) bands, in 36 MDD patients and 36 healthy controls while they performed a breathing focus task. The task included two phases during which participants' ability to concentrate on their breathing was assessed before and after an instructed worry phase. In addition to higher self-reported worry, MDD patients exhibited lower HF and LF HRV at rest, and lower LF HRV during the task than controls. MDD was also associated with impaired breathing concentration ability, more negative and neutral thought intrusions, more negative mood during breathing focus and increased stress during instructed worry. In the total sample, LF HRV correlated negatively with self-reported worry and negative thought intrusions, and positively with mood ratings. The reduction of HRV confirms the notion of low parasympathetic cardiac control in MDD. Moreover, low HRV represents a correlate of blunted prefrontal activity and impaired cognitive control that characterize the disorder. Impaired cognitive control may exacerbate worry, which is in turn involved in the genesis of aversive emotional states and maintenance of MDD.