Breath Samples Research Articles

The science behind the nose: correlating volatile organic compound characterization with canine biodetection of COVID-19

BackgroundThe SARS-CoV-2 pandemic stimulated the advancement and research in the field of canine scent detection of COVID-19 and volatile organic compound (VOC) breath sampling. It remains unclear which VOCs are associated with positive canine alerts. This study aimed to confirm that the training aids used for COVID-19 canine scent detection were indeed releasing discriminant COVID-19 VOCs detectable and identifiable by gas chromatography (GC-MS).MethodsInexperienced dogs (two Labradors and one English Springer Spaniel) were trained over 19 weeks to discriminate between COVID-19 infected and uninfected individuals and then independently validated. Getxent® tubes, impregnated with the odours from clinical gargle samples, used during the canines’ maintenance training process were also analysed using GC-MS.ResultsThree dogs were successfully trained to detect COVID-19. A PCA-X model was created and confirmed the ability to discriminate between VOCs from positive and negative COVID-19 Getxent® tubes with a sensitivity of 78% and a specificity of 77%. Two VOCs were found to be very predictive of positive COVID-19 cases. When comparing the dogs with GC-MS, F1 and MCC correlation scores of 0.69 and 0.37, were observed respectively, demonstrating good concordance between the two methods.InterpretationThis study provides analytical confirmation that canine training aids can be safely and reliably produced with good discrimination between positive samples and negative controls. It is also a further step towards better understanding of canine odour discrimination of COVID-19 as the scent of interest and defining what VOC elements the canines interpret as “essential”.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data.

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

347 Aerodynamic Size Distribution of SARS-CoV-2 Aerosol Shedding

OBJECTIVES/GOALS: We designed the Biocascade Exhaled Breath Sampler (BEBS) to characterize viral aerosol shedding among individuals with influenza and other respiratory virus infections. We first aimed to test the BEBS on volunteer COVID-19 cases and report the aerodynamic size distribution of exhaled breath aerosol particles carrying SARS-CoV-2 RNA. METHODS/STUDY POPULATION: From June 15 through December 15, 2022, we recruited 27 PCR-confirmed COVID-19 cases from a college campus and the surrounding community to provide 30-minute breath samples into a well-validated Gesundheit-II (G-II) exhaled breath aerosol sampler. Among these individuals, 17 provided an additional exhaled breath sample into the newly designed BEBS. We quantified samples for viral RNA using reverse transcription digital polymerase chain reaction (RT-dPCR) and determined the viral RNA copies collected within two aerosol size fractions (≤5 µm and >5 µm in diameter) from the G-II, and four aerosol size fractions (<1.15 µm, 1.15–3.2 µm, 3.3–8.2 µm, and >8.2 µm) from the BEBS. RESULTS/ANTICIPATED RESULTS: Individuals with a SARS-CoV-2 Omicron BA.4 or BA.5 infection shed virus in aerosols at an average rate of 7.5x103 RNA copies per 30-minute G-II sample, with 78% of the total RNA in aerosols ≤5 µm in diameter. Among the BEBS samples, 10% of the total viral RNA was detected in aerosols <1.15 µm, 43% in 1.15–3.2 µm, 37% in 3.3–8.2 µm, and 10% in the >8.2 µm size fraction. Based on viral RNA loads, our results indicate that exhaled aerosols ≤3.2 µm contribute the majority of SARS-CoV-2 inhalation exposure. DISCUSSION/SIGNIFICANCE: Our data provide additional evidence that respirable aerosols contribute to the spread of SARS-CoV-2. Thus, our data suggest that mitigation measures designed to reduce infectious aerosol inhalation, such as ventilation and the use of air cleaners and respirators, are needed to control the spread.

Enhanced selectivity of novel sea urchin-like h-/m-WO3 hetero nanoflowers for highly sensitive detection of ammonia by double filtration method

Real-time monitoring of toxic gases can be performed by metal oxide semiconductor-based chemo-resistive gas sensors due to their simple structures, low cost, and faster and better recovery and response. Due to the effective electron-hole separation, tuning the development of phase junctions of the metal oxide is an efficient method for improving their gas sensing properties. In this study, two distinct hydrothermal methods were used to synthesize h-/m-WO3 hetero-nanoflowers. In the first method, L-Cysteine was used to facilitate the formation of phase junctions between h-WO3 and m-WO3 nanostructures, whereas Thiourea was used in the second. Various characterisation approaches validated the hexagonal-monoclinic phase junction development in the nanoflowers. The sensing material fabricated using Thiourea with a concentration of 0.01M performed better than other sensors. At 350°C, the selected sample displayed outstanding ammonia sensing capacity with the experimental detection limit of 1 ppm (response=3.33). The theoretical detection limit is determined to be 27 ppb. The selectivity of the sensor towards NH3 over H2S and acetone is increased by combining the devices with a double filtration setup. The effectiveness of the selected h-/m-WO3 sensor for diagnosing kidney dysfunction was evaluated by analyzing simulated breath samples.

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.

Exhaled volatolomics profiling facilitates personalized screening for gastric cancer

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87 to 0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89 to 0.94, 95%CI) in discovery cohort and 0.88 (0.83 to 0.91, 95%CI) accuracy with an AUC of 0.91 (0.87 to 0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

The Identification of Acetic Acid-Ethanol Mixture Using Gas Sensor Array and Ensemble Regression

Identification of acetic acid-ethanol mixtures using a commercial gas sensor array equipped with ensemble regression has been carried out. The gas sensor analysis was simple, rapid, and fast since it did not require any sample preparation. A quantitative analysis of the acetic acid-ethanol mixture was carried out to determine the sensitivity and selectivity of the sensor in distinguishing the concentration of the acetic acid and ethanol mixture. This study focuses on the coefficient of determination of 80% of the calibration data set and recovery of 20% of the testing data set. The models showed excellent performance,specifically, the Bagging and Random Forest r2 for the ethanol calibration data reached 0.91 and 0.94, respectively. The corresponding ethanol test recoveries were 99.95% and 97.84%, indicating the robustness of the model in accurately predicting ethanol concentration. Acetic acid test recoveries were 100.56% and 101.38% with r2 of 0.89 and 0.93 for Bagging and Random Forest regression, respectively. Hence, the commercial gas sensor array equipped with ensemble regression can be applied to the quantification of the acetic acid – ethanol mixture and demonstrate opportunities for the practical use of this gas sensor array in analyzing real samples, i.e. human breath or environmental monitoring samples.

Can scent-detection dogs detect the stress associated with trauma cue exposure in people with trauma histories? A proof-of-concept study.

Post-traumatic stress disorder (PTSD) is an impairing mental health condition with high prevalence among military and general populations alike. PTSD service dogs are a complementary and alternative intervention needing scientific validation. We investigated whether dogs can detect putative stress-related volatile organic compounds (VOCs) in the breath of people with trauma histories (54% with PTSD) exposed to personalized trauma cues. Breath samples were collected from 26 humans over 40 experimental sessions during a calm (control breath sample) and stressed state induced by trauma cue exposure (target breath sample). Two scent detection canines were presented with the samples in a two alternative forced choice (2AFC) discrimination and yes/no detection task. The 2AFC task assessed the dogs' ability to discriminate between the two states within the breath samples of one individual. The detection task determined their ability to generalize the target odour across different individuals and different stressful events of one individual. Signal Detection Theory was applied to assess dogs' sensitivity, specificity, precision, and response bias. The dogs performed at ∼90% accuracy across all sample sets in the discrimination experiment, and at 74% and 81% accuracy, respectively, in the detection experiment. Further analysis of dog olfactory performance in relation to human donor self-reported emotional responses to trauma cue exposure suggested the dogs may have been detecting distinct endocrine stress markers. One dog's performance correlated with the human donors' self-reported fear responses and the other dog's performance correlated with the human donors' self-reported shame responses. Based on these correlations between dog performance and donor self-report measures, we speculate that the VOCs each dog was detecting likely originated from the sympathetico-adreno-medullary axis (SAM; adrenaline, noradrenaline) in the case of the first dog and the hypothalamo-pituitary-adrenal axis (HPA; glucocorticoids) in the case of the second dog. Our proof-of-concept study is the first to demonstrate that some dogs can detect putative VOCs emitted by people with trauma histories when experiencing distress theoretically associated with the intrusion and arousal/reactivity symptoms of PTSD. Results have potential to improve the effectiveness and training protocol of PTSD service dogs with a focus on enhancing their alert function.

Detection of Clostridioides difficile infection by assessment of exhaled breath volatile organic compounds

Clostridioides difficile infection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity and Clostridioides difficile polymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negative C.difficile testing (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Wearable sampling of proteins from human exhaled aerosols for nano-liquid chromatography-tandem mass spectrometry analysis: A pilot study.

Human exhaled breath usually contains unique proteins that may provide clues to characterize individual physiological activities and many diseases. However, the concentration of exhaled proteins in exhaled breath is extremely low and usually does not reach the detection limits of all online breath mass spectrometry instruments. Therefore, developing a new breath sampler for collecting and characterizing exhaled proteins is important. In this study, a new mask-based wearable sampler was developed by fixing metal materials into the inner surface of the KN95 mask. Human exhaled proteins could be directly adsorbed onto the metal material while wearing the mask. After sampling, the collected proteins were eluted, digested, and identified using nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS). The adsorption of exhaled proteins was evaluated, showing that modified gold foil is an effective material for collecting exhaled proteins. Various endogenous proteins were successfully identified from exhaled breath, many of which can be potential biomarkers for disease diagnosis. By coupling the newly developed mask sampler with nano-LC-MS/MS, human exhaled proteins were successfully collected and identified. Our results show that the mask sampler is wearable, simple, and convenient, and the method is noninvasive for investigating disease diagnosis and human health.

An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone

In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) – providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm−1Hz−0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.

Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%–81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%–74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring.

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800ppb) to people at high risk of DKA (>75ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations insimulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

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.

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Development of an automated human scent olfactometer and its use to evaluate detection dog perception of human scent.

Working Dogs have shown an extraordinary ability to utilize olfaction for victim recovery efforts. Although instrumental analysis has chemically characterized odor volatiles from various human biospecimens, it remains unclear what perceptually constitutes human scent (HS) for dogs. This may be in part due to the lack of methodology and equipment to train and evaluate HS perception. The aims of this research were 1) to develop an automated human scent olfactometer (AHSO) to present HS to dogs in a controlled setting and 2) use the AHSO to evaluate dogs' response to different scented articles and individual components of HS. A human volunteer was placed in a clear acrylic chamber and using a vacuum pump and computer-controlled valves, the headspace of this chamber was carried to one of three ports in a different room. Dogs were trained to search all three ports of the olfactometer and alert to the one containing HS. In Experiment 1 and 2, the AHSO was validated by testing two dogs naïve to HS (Experiment 1) and five certified Search and Rescue (SAR) teams naïve to the apparatus (Experiment 2). All dogs showed sensitivity and specificity to HS > 95% in the apparatus. In Experiment 3, we used a spontaneous generalization paradigm to evaluate generalization from the HS chamber to different scented articles exposed to the same volunteer and to a breath sample. Dogs' response rate to the different scented articles was < 10% but exceeded 40% for the breath sample. In Experiment 4, we replicated this result by re-testing spontaneous generalization to breath and when the volunteer had breath exhausted/removed from the chamber. Dogs' response rate to breath alone was 88% and only 50% when breath was removed. Altogether, the data indicate that exhaled breath is an important and salient component of HS under these conditions.

[Changes in protein requirements of elderly people over 5 years assessed using the indicator amino acid oxidation method].

To evaluate the changes in protein requirements of the elderly during the past five years. Based on the previous study of protein requirements of 14 elderly in 2017, 4 of these elderly(70-80 y) were included as study participants and protein requirements were re-evaluated using the indicator amino acid oxidation method. There were seven protein levels: 0.1, 0.3, 0.6, 0.9, 1.2, 1.5 and 1.8 g/(kg·d). Maintenance diets were given for the first two days of each protein level. A stable isotope study was conducted on the day 3, using L-~(13)C-phenylalanine as an indicator on the basis of an amino acid rationed diet, which was orally ingested into the body along with the amino acid rationed diet, and breath and urine samples were collected when the metabolism of L-~(13)C-phenylalanine reached steady state in the body. By measuring the kinetic parameters of labeled amino acids in the samples, a nonlinear mixed-effects model was constructed for the protein intake to be tested and the oxidation rate of labeled amino acids. The mean protein requirement of the study population was determined by the protein intake corresponding to the inflection point of the curve. Based on the production rate of ~(13)CO_2 in exhaled breath of four elderly people at different protein levels, the mean protein requirement was 1.05(95%CI 0.51-1.60) g/(kg·d). The protein recommended nutrient intake was 1.31(95%CI 0.64-2.00) g/(kg·d) was estimated by applying the coefficient of variation of the mean protein requirement to derive the recommended nutrient intake. Protein requirements in the elderly have increased over a five-year period and sarcopenia may be the main cause of increased protein requirements.

Detection of COVID-19 through breath sample analysis with an electronic nose

Detection of COVID-19 through breath sample analysis with an electronic nose

Breath of Health: spectroscopy-based breath test for the detection of SARS-CoV-2

Background Nucleic acid amplification tests (NAAT) are considered the gold standard for COVID-19 diagnosis. These tests require professional manpower and equipment, long processing and swab sampling which is unpleasant to the patients. Several volatile organic compounds (VOCs) have been identified in the breath of COVID-19 patients. Detection of these VOCs using a breath test could help rapidly identify COVID-19 patients. Objective Assess the accuracy of ‘Breath of Health’ (BOH) COVID-19 Fourier-transform infra-red (FTIR) Spectroscopy-based breath test. Methods Breath samples from patients with or without symptoms suggestive for COVID-19 who had NAAT results were collected using Tedlar bags and were blindly analysed using BOH FTIR spectroscopy. BOH Measures several VOCs simultaneously and differentiating positive and negative results. BOH results were compared to NAAT results as gold standard. Results Breath samples from 531 patients were analysed. The sensitivity of BOH breath test was found to be 79.5% and specificity was 87.2%. Positive predictive value (PPV) was 74.7% and negative predictive value (NPV) 90.0%. Calculated accuracy rate was 84.8% and area under the curve 0.834. Subgroup analysis revealed that the NPV of patients without respiratory symptoms was superior over the NPV of symptomatic patients (94.7% vs 80.7%, P-value < 0.0001) and PPV of patients with respiratory symptoms outranks the PPV of individuals without symptoms (85.3% vs 69.2%, P-value 0.0196) Conclusion We found BOH COVID-19 breath test to be a patient-friendly, rapid, non-invasive diagnostic test with high accuracy rate and NPV that could efficiently rule out COVID-19 especially among individuals with low pre-test probability.