Exhaled breath analysis in disease detection

A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm-1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc-/-/Il2rg-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats

Offline techniques are adopted for studying air pollution health impacts, thus failing to provide in situ observations. Here, we have demonstrated their real-time monitoring by online analyzing an array of gaseous biomarkers from rats' exhaled breath using an integrated exhaled breath array sensor (IEBAS) developed. The biomarkers include total volatile organic compounds (TVOC), CO2, CO, NO, H2S, H2O2, O2, and NH3. Specific breath-borne VOCs were also analyzed by a gas chromatography-ion mobility spectrometer (GC-IMS). After real-life ambient air pollution exposures (2 h), the pollution levels of PM2.5 and O3 were both found to significantly affect the relative levels of multiple gaseous biomarkers in rats' breath. Eleven biomarkers, especially NO, H2S, and 1-propanol, were detected as significantly correlated with PM2.5 concentration, while heptanal was shown to be significantly correlated with O3. Likewise, significant changes were also detected in multiple breath-borne biomarkers from rats under lab-controlled O3 exposures with levels of 150, 300, and 1000 μg/m3 (2 h), compared to synthetic air exposure. Importantly, heptanal was experimentally confirmed as a reliable biomarker for O3 exposure, with a notable dose-response relationship. In contrast, conventional biomarkers of inflammation and oxidative stress in rat sera exhibited insignificant differences after the 2 h exposures. The results imply that breath-borne gaseous biomarkers can serve as an early and sensitive indicator for ambient pollutant exposure. This work pioneered a new research paradigm for online monitoring of air pollution health impacts while obtaining important candidate biomarker information for PM2.5 and O3 exposures.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Effect of ambient temperature and respiration rate on nasal dominance: preliminary findings from a nostril-specific wearable

The nasal dominance (ND) determination is crucial for nasal synchronized ventilator, optimum nasal drug delivery, identifying brain hemispheric dominance, nasal airway obstruction surgery, mindfulness breathing, and for possible markers of a conscious state. Given these wider applications of ND, it is interesting to understand the patterns of ND with varying temperature and respiration rates. In this paper, we propose a method which measures peak-to-peak temperature oscillations (difference between end-expiratory and end-inspiratory temperature) for the left and right nostrils during nasal breathing. These nostril-specific temperature oscillations are further used to calculate the nasal dominance index (NDI), nasal laterality ratio (NLR), inter-nostril correlation, and mean of peak-to-peak temperature oscillation for inspiratory and expiratory phase at (1) different ambient temperatures of 18 °C, 28 °C, and 38 °C and (2) at three different respiration rate of 6 bpm, 12 bpm, and 18 bpm. The peak-to-peak temperature (Tpp) oscillation range (averaged across participants;n= 8) for the left and right nostril were 3.80 ± 0.57 °C and 2.34 ± 0.61 °C, 2.03 ± 0.20 °C and 1.40 ± 0.26 °C, and 0.20 ± 0.02 °C and 0.29 ± 0.03 °C at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively (averaged across participants and respiration rates). The NDI and NLR averaged across participants and three different respiration rates were 35.67 ± 5.53 and 2.03 ± 1.12; 8.36 ± 10.61 and 2.49 ± 3.69; and -25.04 ± 14.50 and 0.82 ± 0.54 at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively. The Shapiro-Wilk test, and non-parametric Friedman test showed a significant effect of ambient temperature conditions on both NDI and NLR. No significant effect of respiration rate condition was observed on both NDI and NLR. The findings of the proposed study indicate the importance of ambient temperature while determining ND during the diagnosis of breathing disorders such as septum deviation, nasal polyps, nosebleeds, rhinitis, and nasal fractions, and in the intensive care unit for nasal synchronized ventilator.

Detection of abused drugs in human exhaled breath using mass spectrometry: A review

RATIONALE

Human breath analysis has been attracting increasing interest in the detection of abused drugs in forensic and clinical applications because of its noninvasive sampling and distinctive molecular information. Mass spectrometry (MS)-based approaches have been proven to be powerful tools for accurately analyzing exhaled abused drugs. The major advantages of MS-based approaches include high sensitivity, high specificity, and versatile couplings with various breath sampling methods.

METHODS

Recent advances in the methodological development of MS analysis of exhaled abused drugs are discussed. Breath collection and sample pretreatment methods for MS analysis are also introduced.

RESULTS

Recent advances in technical aspects of breath sampling methods are summarized, highlighting active and passive sampling. MS methods for detecting different exhaled abused drugs are reviewed, emphasizing their features, advantages, and limitations. The future trends and challenges in MS-based breath analysis of exhaled abused drugs are also discussed.

CONCLUSIONS

The coupling of breath sampling methods with MS approaches has been proven to be a powerful tool for the detection of exhaled abused drugs, offering highly attractive results in forensic investigations. MS-based detection of exhaled abused drugs in exhaled breath is a relatively new field and is still in the early stages of methodological development. New MS technologies promise a substantial benefit for future forensic analysis.

A nanostructured Al-doped ZnO as an ultra-sensitive room-temperature ammonia gas sensor

Novel chemi-resistive gas sensors with strong detection capabilities operating at room temperature are desirable owing to their extended cycle life, high stability, and low power consumption. The current study focuses on detecting NH3 at room temperature using lower gas concentrations. The co-precipitation technique was employed to produce pure and Al-doped ZnO nanoparticles, which were calcined at 300 °C for three hours. The effect of aluminium (Al) doping on the structural, morphological, optical, and gas-sensing abilities was investigated and reported. The presence of aluminium was confirmed by XRD, EDX, and FTIR spectroscopy. Additionally, to assess the various characteristics of Al-doped ZnO nanoparticles, scanning electron microscopy (SEM), ultraviolet-visible diffuse reflectance spectroscopy (UV-DRS), atomic force microscopy (AFM), and Brunauer-Emmett-Teller (BET) techniques were used. The crystallite size increased from 14.82 to 17.49 nm in the XRD analysis; the SEM pictures showed a flower-like morphology; and the energy gap decreased from 3.240 to 3.210 eV when Al doping was raised from 1 wt% to 4 wt%. AFM studies revealed topographical information with significant roughness in the range of 230-43 nm. BET analysis showed a mesoporous nature with surface areas varying from 25.274 to 14.755 m2/g and pore diameters ranging from 8.34 to 7.00 nm. The sensing capacities of pure and Al-doped ZnO nanoparticles towards methanol (CH3OH), toluene (C7H8), ethanol (C2H5OH), and ammonia (NH3) were investigated at room temperature. The one-wt% Al-doped ZnO sensor demonstrated an ultrafast response and recovery times at one ppm compared to other AZO-based sensors towards NH3.

Hollow-Out Fe2O3-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

The analysis of exhaled breath has opened up new exciting avenues in medical diagnostics, sleep monitoring, and drunk driving detection. Nevertheless, the detection accuracy is greatly affected due to high humidity in the exhaled breath. Here, we propose a regulation method to solve the problem of humidity adaptability in the ethanol-monitoring process by building a heterojunction and hollow-out nanostructure. Therefore, large specific surface area hollow-out Fe₂O₃-loaded NiO heterojunction nanorods assembled by porous ultrathin nanosheets were prepared by a well-tailored interface reaction. The excellent response (51.2 toward 10 ppm ethanol at 80% relative humidity) and selectivity to ethanol under high relative humidity with a lower operating temperature (150 °C) were obtained, and the detection limit was as low as 0.5 ppb with excellent long-term stability. The superior gas-sensing performance was attributed to the high surface activity of the heterojunction and hollow-out nanostructure. More importantly, GC-MS, diffuse reflectance Fourier transform infrared spectroscopy, and DFT were utilized to analyze the mechanisms of heterojunction sensitization, ethanol-sensing reaction, and high-humidity adaptability. Our integrated low-power MEMS Internet of Things (IoT) system based on Fe₂O₃@NiO successfully demonstrates the functional verification of ethanol detection in human exhalation, and the integrated voice alarm and IoT positioning functions are expected to solve the problem of real-time monitoring and rapid initial screening of drunk driving. Overall, this novel method plays a vital role in areas such as control of material morphology and composition, breath analysis, gas-sensing mechanism research, and artificial olfaction.

Two-Dimensional Nitrogen-Doped Carbon Nanosheets Derived from g-C3N4 /ZIF-8 for Solid-Phase Microextraction in Exhalation of Esophageal Cancer Patients

Here, two-dimensional (2D) nitrogen-doped carbon nanosheets (CNSs) were prepared through carbonizing MOFs (ZIF-8) in-situ grown using graphitic carbon nitride (g-C₃N₄) as a template. The developed ZIF-8 CNS was then used as solid-phase microextraction (SPME) fiber coating for beneficiation of five biomarkers in exhalation of patients with esophageal cancer and in gas chromatography-mass spectrometry (GC-MS) for determination. The ZIF-8 CNS fiber exhibits satisfactory enrichment factors (3490-5631), wide linearity (5-1000 μg L^-1), and low detection limits (0.26-0.96 μg L^-1). The relative standard deviations (RSDs) for six replicate extractions of the same ZIF-8 CNS fiber were between 2.0-3.9% (intra-day) and 2.8-5.2% (inter-day). The reproducibility of three fibers prepared by the same approach was in the range 6.8-12.3% (RSD). The developed ZIF-8 CNS fiber can persist in 120 SPME cycles with no prominent loss of extraction efficiency and precision. The high enrichment factors of the 2D ZIF-8 CNS coatings are attributed to the high specific surface area, ultrathin thickness, and nano-pore or interlayer channels; moreover, nitrogen doping also endows the π system with a strong electron absorption ability, which will enhance the π-π interaction between the ZIF-8 CNS and the aromatic ring. Ultimately, the self-made ZIF-8 CNS-coated SPME fiber was applied to the analysis of exhaled breath samples. The recoveries of spiked analytes are between 84 and 105%.

In situ laser-assisted synthesis and patterning of graphene foam composites as a flexible gas sensing platform

Gas-sensitive semiconducting nanomaterials (e.g., metal oxides, graphene oxides, and transition metal dichalcogenides) and their heterojunctions hold great promise in chemiresistive gas sensors. However, they often require a separate synthesis method (e.g., hydrothermal, so-gel, and co-precipitation) and their integration on interdigitated electrodes (IDE) via casting is also associated with weak interfacial properties. This work demonstrates in situ laser-assisted synthesis and patterning of various sensing nanomaterials and their heterojunctions on laser-induced graphene (LIG) foam to form LIG composites as a flexible and stretchable gas sensing platform. The porous LIG line or pattern with nanomaterial precursors dispensed on top is scribed by laser to allow for in situ growth of corresponding nanomaterials. The versatility of the proposed method is highlighted through the creation of different types of gas-sensitive materials, including transition metal dichalcogenide (e.g., MoS2), metal oxide (e.g., CuO), noble metal-doped metal oxide (e.g., Ag/ZnO) and composite metal oxides (e.g., In2O3/Cr2O3). By eliminating the IDE and separate heaters, the LIG gas sensing platform with self-heating also decreases the device complexity. The limit of detection (LOD) of the LIG gas sensor with in situ synthesized MoS2, CuO, and Ag/ZnO to NO2, H2S, and trimethylamine (TMA) is 2.7, 9.8, and 5.6 ppb, respectively. Taken together with the high sensitivity, good selectivity, rapid response/recovery, and tunable operating temperature, the integrated LIG gas sensor array can identify multiple gas species in the environment or exhaled breath.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.

Utility of SIFT-MS to evaluate volatile organic compounds in nephropathic patients' breath

Several studies highlighted a correlation between exhaled air volatile organic compounds (VOCs) and some pathological conditions, such as chronic kidney disease (CKD), chronic liver disease, etc. In fact, in literature has been reported that CKD is characterized by an increased concentration of ammonia, trimethylamine (TMA) and isoprene compared to healthy subjects. Currently, there is not a validate and standardized method to detect VOCs. For this purpose, we examined the utility of selected ion flow tube-mass spectrometry (SIFT-MS) to measure VOCs in CKD patients and we evaluated the possible correlation between VOCs and the presence of CKD and its stage. We enrolled 68 CKD patients under conservative therapy and 54 healthy subjects. The analysis of the VOCs of the exhaled air of the enrolled subjects was performed by SIFT-MS. Among all the VOCs analyzed, the most relevant results by ROC curves were observed for TMA, acetone, ammonia and dimethyl sulfide. We found that a breath TMA concentration superior to 26 ppbv characterizes a 6.11 times greater risk of CKD, compared to subjects with lower levels. Moreover, we detected an increased concentration of acetone and ammonia in CKD patients compared to healthy subjects. We highlight the potential utility of SIFT-MS in CKD clinical management.

Clinical trial registry: R.S. 15.19 of 6 February 2019.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

A novel temperature-controlled open source microcontroller based sampler for collection of exhaled breath condensate in point-of-care diagnostics

Exhaled breath condensate (EBC) is an attractive, non-invasive sample for clinical diagnostics. During EBC collection, its composition is influenced by the collection temperature, a factor that is often not thoroughly monitored and controlled. In this study, we assembled a novel, simple, portable, and inexpensive device for EBC collection, able to maintain a stable temperature at any value between -7 °C and +12 °C. The temperature was controlled using a microcontroller and a thermoelectric cooler that was employed to cool the aluminum block holding the glass tube or the polypropylene syringe. The performance of the novel sampler was compared with the passively cooled RTube™ and a simple EBC sampler, in which the temperature was steadily increasing during sampling. The developed sampler was able to maintain a stable temperature within ±1 °C. To investigate the influence of different sampling temperatures (i.e., +12, -7, -80 °C) on the analyte content in EBC, inorganic ions and organic acids were analyzed by capillary electrophoresis with a capacitively coupled contactless conductivity detector. It was shown that the concentration of metabolites decreased significantly with decreasing temperature. The portability and the ability to keep a stable temperature during EBC sampling makes the developed sampler suitable for point-of-care diagnostics.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

A headspace collection chamber for whole body volatilomics

The human body secretes a complex blend of volatile organic compounds (VOCs) via the skin, breath and bodily fluids. In this study, we have developed a headspace collection chamber for whole body volatilome profiling.