The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva

Changes in the urine volatile metabolome throughout growth of transplanted hepatocarcinoma

Trained detection dogs distinguish between urine samples from healthy organisms and organisms with malignant tumors, suggesting that the volatile urine metabolome contains information about tumor progression. The aim of this study was to determine whether the stage of tumor growth affects the chemical differences in the urine of mice and to what extent the "olfactory image of disease" perceived by dogs coincides with the "image of disease" recorded by the mass spectrometer. We used a novel laser ionization mass spectrometry method and propose a mass spectrometric analysis without detailed interpretation of the spectrum of volatile metabolomes in urine. The mass spectrometer we use works without sample preparation and registers volatile organic compounds in air at room temperature without changing the pH of the sample, i.e. under conditions similar to those in which dogs solve the same problem. The experimental cancer models were male BDF-f1 hybrid mice transplanted with hepatocarcinoma tissue, and similar mice transplanted with healthy liver tissue were used as controls. Our data show that both dogs and our proposed laser mass spectrometry method are able to detect both the entire spectrum of volatile organic compounds associated with the disease and minor changes in this spectrum during its course.

Relationship between lung function and exhaled volatile organic compounds in healthy infants

OBJECTIVE

The aim of this study is to assess, for the first time, the relationship between the volatilome and lung function in healthy infants, which may be of help for the early detection of certain respiratory diseases. Lung function tests are crucial in chronic respiratory diseases diagnosis. Moreover, volatile organic compounds (VOCs) analysis in exhaled breath is a noninvasive technique that enables the monitorization of oxidative stress, typical of some forms of airway inflammation.

METHODS

Lung function was studied in 50 healthy infants of 3-8 months of age and the following parameters were obtained: forced vital capacity (FVC), forced expiratory volume at 0.5 s (FEV_0.5 ), forced expiratory flow at 75% of FVC (FEF₇₅ ), forced expiratory flow at 25%-75% of FVC (FEF_25-75 ), and FEV_0.5 /FVC. Lung function was measured according to the raised volume rapid thoracoabdominal compression technique. In addition, a targeted analysis of six endogenous VOCs (acetone, isoprene, decane, undecane, tetradecane, and pentadecane) in the exhaled breath of the children was carried out by means of thermal desorption coupled gas chromatography-single quadrupole mass spectrometry system.

RESULTS

A negatively significant relationship has been observed between levels of acetone, tetradecane, and pentadecane in exhaled breath and several of the lung function parameters. Levels of acetone (feature m/z = 58) were significantly negatively associated with FVC and FVE_0.5 , levels of tetradecane (feature m/z = 71) with FEV_0.5, and levels of pentadecane (feature m/z = 71) with FEV_0.5 and FEF_25-75 .

CONCLUSION

The findings of this study highlight a significant association between VOCs related to oxidative stress and lung function in healthy infants.

Smartphone-Based Platforms for Clinical Detections in Lung-Cancer-Related Exhaled Breath Biomarkers: A Review

Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging indices, volatile organic compounds (VOCs), and exhaled breath condensates (EBCs), have been discovered for application in screening, diagnosis, and prognosis. However, the detection of markers still relies on bulky and professional instruments, which are limited to training personnel or laboratories. This seriously hinders population screening for early diagnosis of lung cancer. Advanced smartphones integrated with powerful applications can provide easy operation and real-time monitoring for healthcare, which demonstrates tremendous application scenarios in the biomedical analysis region from medical institutions or laboratories to personalized medicine. In this review, we propose an overview of lung-cancer-related noninvasive markers from exhaled breath, focusing on the novel development of smartphone-based platforms for the detection of these biomarkers. Lastly, we discuss the current limitations and potential solutions.

Modelling of Breath and Various Blood Volatilomic Profiles—Implications for Breath Volatile Analysis

Researchers looking for biomarkers from different sources, such as breath, urine, or blood, frequently search for specific patterns of volatile organic compounds (VOCs), often using pattern recognition or machine learning techniques. However, they are not generally aware that these patterns change depending on the source they use. Therefore, we have created a simple model to demonstrate that the distribution patterns of VOCs in fat, mixed venous blood, alveolar air, and end-tidal breath are different. Our approach follows well-established models for the description of dynamic real-time breath concentration profiles. We start with a uniform distribution of end-tidal concentrations of selected VOCs and calculate the corresponding target concentrations. For this, we only need partition coefficients, mass balance, and the assumption of an equilibrium state, which avoids the need to know the volatiles’ metabolic rates and production rates within the different compartments.

Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners

OBJECTIVES

This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored.

METHODS

398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test.

RESULTS

Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy.

CONCLUSION

Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.

Corylus avellana L. Aroma Blueprint: Potent Odorants Signatures in the Volatilome of High Quality Hazelnuts

The volatilome of hazelnuts (Corylus avellana L.) encrypts information about phenotype expression as a function of cultivar/origin, post-harvest practices, and their impact on primary metabolome, storage conditions and shelf-life, spoilage, and quality deterioration. Moreover, within the bulk of detectable volatiles, just a few of them play a key role in defining distinctive aroma (i.e., aroma blueprint) and conferring characteristic hedonic profile. In particular, in raw hazelnuts, key-odorants as defined by sensomics are: 2,3-diethyl-5-methylpyrazine (musty and nutty); 2-acetyl-1,4,5,6-tetrahydropyridine (caramel); 2-acetyl-1-pyrroline (popcorn-like); 2-acetyl-3,4,5,6-tetrahydropyridine (roasted, caramel); 3-(methylthio)-propanal (cooked potato); 3-(methylthio)propionaldehyde (musty, earthy); 3,7-dimethylocta-1,6-dien-3-ol/linalool (citrus, floral); 3-methyl-4-heptanone (fruity, nutty); and 5-methyl-(E)-2-hepten-4-one (nutty, fruity). Dry-roasting on hazelnut kernels triggers the formation of additional potent odorants, likely contributing to the pleasant aroma of roasted nuts. Whiting the newly formed aromas, 2,3-pentanedione (buttery); 2-propionyl-1-pyrroline (popcorn-like); 3-methylbutanal; (malty); 4-hydroxy-2,5-dimethyl-3(2H)-furanone (caramel); dimethyl trisulfide (sulfurous, cabbage) are worthy to be mentioned. The review focuses on high-quality hazelnuts adopted as premium primary material by the confectionery industry. Information on primary and secondary/specialized metabolites distribution introduces more specialized sections focused on volatilome chemical dimensions and their correlation to cultivar/origin, post-harvest practices and storage, and spoilage phenomena. Sensory-driven studies, based on sensomic principles, provide insights on the aroma blueprint of raw and roasted hazelnuts while robust correlations between non-volatile precursors and key-aroma compounds pose solid foundations to the conceptualization of aroma potential.

Volatile Organic Compounds as Potential Biomarkers for Noninvasive Disease Detection by Nanosensors: A Comprehensive Review

Biomarkers are biological molecules associated with physiological changes of the body and aids in the detecting the onset of disease in patients. There is an urgent need for self-monitoring and early detection of cardiovascular and other health complications. Several blood-based biomarkers have been well established in diagnosis and monitoring the onset of diseases. However, the detection level of biomarkers in bed-side analysis is difficult and complications arise due to the endothelial dysfunction. Currently single volatile organic compounds (VOCs) based sensors are available for the detection of human diseases and no dedicated nanosensor is available for the elderly. Moreover, accuracy of the sensors based on a single analyte is limited. Hence, breath analysis has received enormous attention in healthcare due to its relatively inexpensive, rapid, and noninvasive methods for detecting diseases. This review gives a detailed analysis of how biomarker imprinted nanosensor can be used as a noninvasive method for detecting VOC to health issues early using exhaled breath analysis.

Identifying SARS-COV-2 infected patients through canine olfactive detection on axillary sweat samples; study of observed sensitivities and specificities within a group of trained dogs

There is an increasing need for rapid, reliable, non-invasive, and inexpensive mass testing methods as the global COVID-19 pandemic continues. Detection dogs could be a possible solution to identify individuals infected with SARS-CoV-2. Previous studies have shown that dogs can detect SARS-CoV-2 on sweat samples. This study aims to establish the dogs’ sensitivity (true positive rate) which measures the proportion of people with COVID-19 that are correctly identified, and specificity (true negative rate) which measures the proportion of people without COVID-19 that are correctly identified. Seven search and rescue dogs were tested using a total of 218 axillary sweat samples (62 positive and 156 negative) in olfaction cones following a randomised and double-blind protocol. Sensitivity ranged from 87% to 94%, and specificity ranged from 78% to 92%, with four dogs over 90%. These results were used to calculate the positive predictive value and negative predictive value for each dog for different infection probabilities (how likely it is for an individual to be SARS-CoV-2 positive), ranging from 10–50%. These results were compared with a reference diagnostic tool which has 95% specificity and sensitivity. Negative predictive values for six dogs ranged from ≥98% at 10% infection probability to ≥88% at 50% infection probability compared with the reference tool which ranged from 99% to 95%. Positive predictive values ranged from ≥40% at 10% infection probability to ≥80% at 50% infection probability compared with the reference tool which ranged from 68% to 95%. This study confirms previous results, suggesting that dogs could play an important role in mass-testing situations. Future challenges include optimal training methods and standardisation for large numbers of detection dogs and infrastructure supporting their deployment.

Effects of COVID-19 protective face-masks and wearing durations onto respiratory-haemodynamic physiology and exhaled breath constituents

Background

While assumed to protect against coronavirus transmission, face masks may have effects on respiratory–haemodynamic parameters. Within this pilot study, we investigated immediate and progressive effects of FFP2 and surgical masks on exhaled breath constituents and physiological attributes in 30 adults at rest.

Methods

We continuously monitored exhaled breath profiles within mask space in older (age 60–80 years) and young to middle-aged (age 20–59 years) adults over the period of 15 and 30 min by high-resolution real-time mass-spectrometry. Peripheral oxygen saturation (S_pO2) and respiratory and haemodynamic parameters were measured (noninvasively) simultaneously.

Results

Profound, consistent and significant (p≤0.001) changes in S_pO2 (≥60_FFP2-15 min: 5.8±1.3%↓, ≥60_surgical-15 min: 3.6±0.9%↓, <60_FFP2-30 min: 1.9±1.0%↓, <60_surgical-30 min: 0.9±0.6%↓) and end-tidal carbon dioxide tension (P_ETCO2) (≥60_FFP2-15 min: 19.1±8.0%↑, ≥60_surgical-15 min: 11.6±7.6%↑, <60_FFP2- 30 min: 12.1±4.5%↑, <60_surgical- 30 min: 9.3±4.1%↑) indicate ascending deoxygenation and hypercarbia. Secondary changes (p≤0.005) to haemodynamic parameters (e.g. mean arterial pressure (MAP) ≥60_FFP2-15 min: 9.8±10.4%↑) were found. Exhalation of bloodborne volatile metabolites, e.g. aldehydes, hemiterpene, organosulfur, short-chain fatty acids, alcohols, ketone, aromatics, nitrile and monoterpene mirrored behaviour of cardiac output, MAP, S_pO2, respiratory rate and P_ETCO2. Exhaled humidity (e.g. ≥60_FFP2-15 min: 7.1±5.8%↑) and exhaled oxygen (e.g. ≥60_FFP2-15 min: 6.1±10.0%↓) changed significantly (p≤0.005) over time.

Conclusions

Breathomics allows unique physiometabolic insights into immediate and transient effects of face mask wearing. Physiological parameters and breath profiles of endogenous and/or exogenous volatile metabolites indicated putative cross-talk between transient hypoxaemia, oxidative stress, hypercarbia, vasoconstriction, altered systemic microbial activity, energy homeostasis, compartmental storage and washout. FFP2 masks had a more pronounced effect than surgical masks. Older adults were more vulnerable to FFP2 mask-induced hypercarbia, arterial oxygen decline, blood pressure fluctuations and concomitant physiological and metabolic effects.

While assumed to protect against SARS-CoV-2 transmission, face masks cause various physiometabolic side-effects and changes in exhaled VOC profiles. Effects are more pronounced in FFP2 masks and are profound at age ≥60 years.https://bit.ly/33fzOMA

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning

Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject's breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

Non-invasive breath collection in murine models using a newly developed sampling device

Volatile organic compounds (VOCs) in exhaled breath have the potential to be used as biomarkers for screening and diagnosis of diseases. Clinical studies are often complicated by both modifiable and non-modifiable factors influencing the composition of VOCs in exhaled breath. Small laboratory animal studies contribute in obtaining fundamental insight in alterations in VOC composition in exhaled breath and thereby facilitate the design and analysis of clinical research. However, long term animal experiments are often limited by invasive breath collection methods and terminal experiments. To overcome this problem, a novel device was developed for non-invasive breath collection in mice using glass nose-only restrainers thereby omitting the need of anesthetics. C57Bl/6J mice were used to test reproducibility and different air sampling settings for air-flow (ml/min) and time (minutes). Exhaled air was collected on desorption tubes and analysed for VOCs by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). In total 27 compounds were putatively identified and used to assess the variability of the VOC measurements in the breath collections. Best reproducibility is obtained when using an air flow of 185 ml/min and a collection time of 20 minutes. Due to the non-invasive nature of breath collections in murine models, this device has the potential to facilitate VOC research in relation to disturbed metabolism and or disease pathways.

Taking leads out of nature, can nano deliver us from COVID-like pandemics?

The COVID-19 crisis has alerted the research community to re-purpose scientific tools that can effectively manage emergency pandemic situations. Researchers were never so desperate to discover a 'magic bullet' that has significant clinical benefits with minimal or no side effects. At the beginning of the pandemic, due to restricted access to traditional laboratory techniques, many research groups delved into computational screening of thousands of lead molecules that could inhibit SARS-CoV-2 at one or more stages of its infectious cycle. Several in silico studies on natural derivatives point out their potency against SARS-CoV-2 proteins. However, theoretical predictions and existing knowledge on related molecules reflect their poor oral bioavailability due to biotransformation in the gut and liver. Nanotechnology has evolved into a key field for precise and controlled delivery of various drugs that lack aqueous solubility, have low oral bioavailability and possess pronounced toxicity in their native form. In this review, we discuss various nanoformulations of natural products with favorable ADME properties, and also briefly explore nano-drug delivery to lungs, the primary site of SARS-CoV-2 infection. Natural products are also envisioned to augment nanotechnology-based 1) personnel protective equipment for ex vivo viral inactivation and 2) wearable sensors that perform rapid and non-invasive analysis of volatile organic compounds in exhaled breath of the infected person after therapeutic food consumption.

Quantitative GC-TCD Measurements of Major Flatus Components: A Preliminary Analysis of the Diet Effect

The impact of diet and digestive disorders in flatus composition remains largely unexplored. This is partially due to the lack of standardized sampling collection methods, and the easy atmospheric contamination. This paper describes a method to quantitatively determine the major gases in flatus and their application in a nutritional intervention. We describe how to direct sample flatus into Tedlar bags, and simultaneous analysis by gas chromatography–thermal conductivity detection (GC–TCD). Results are analyzed by univariate hypothesis testing and by multilevel principal component analysis. The reported methodology allows simultaneous determination of the five major gases with root mean measurement errors of 0.8% for oxygen (O₂), 0.9% for nitrogen (N₂), 0.14% for carbon dioxide (CO₂), 0.11% for methane (CH₄), and 0.26% for hydrogen (H₂). The atmospheric contamination was limited to 0.86 (95% CI: [0.7–1.0])% for oxygen and 3.4 (95% CI: [1.4–5.3])% for nitrogen. As an illustration, the method has been successfully applied to measure the response to a nutritional intervention in a reduced crossover study in healthy subjects.

Simple, efficient and accurate method toward the monitoring of ethyl butanoate traces

We introduce in this research a simple, accurate, safe, and efficient design for the detection of ethyl butanoate that be present in the dry exhaled breath. In particular, the presence of ethyl butanoate in the dry exhaled breath could be utilized as a platform for the diagnosing of COVID 19. The main idea of this theoretical investigation is based on the inclusion of a cavity layer between a thin layer of Au and the well-known one-dimension photonic crystals. Accordingly, the cavity layer is filled with dry exhaled breath. The numerical results are investigated in the vicinity of the Drude model and transfer matrix method. The investigated results show the appearance of Tamm plasmon resonance in the reflectance spectrum of our design through the IR region. Such resonant mode provides very high sensitivity with the change in the concentration of ethyl butanoate. We have examined the performance of the proposed sensor by calculating its sensitivity, detection limit, detection accuracy, quality factor and figure of merit. The designed sensor could receive sensitivity of 0.3 nm/ppm or 260,486 nm/RIU, resolution of 7 ppm and quality factor of 969.

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

Motivation

Analysis of volatile organic compounds (VOCs) in exhaled breath by proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) is of increasing interest for real-time, non-invasive diagnosis, phenotyping and therapeutic drug monitoring in the clinics. However, there is currently a lack of methods and software tools for the processing of PTR-TOF-MS data from cohorts and suited for biomarker discovery studies.

Results

We developed a comprehensive suite of algorithms that process raw data from patient acquisitions and generate the table of feature intensities. Notably, we included an innovative two-dimensional peak deconvolution model based on penalized splines signal regression for accurate estimation of the temporal profile and feature quantification, as well as a method to specifically select the VOCs from exhaled breath. The workflow was implemented as the ptairMS software, which contains a graphical interface to facilitate cohort management and data analysis. The approach was validated on both simulated and experimental datasets, and we showed that the sensitivity and specificity of the VOC detection reached 99% and 98.4%, respectively, and that the error of quantification was below 8.1% for concentrations down to 19 ppb.

Availability and implementation

The ptairMS software is publicly available as an R package on Bioconductor (doi: 10.18129/B9.bioc.ptairMS), as well as its companion experiment package ptairData (doi: 10.18129/B9.bioc.ptairData).

Supplementary information

Supplementary data are available at Bioinformatics online.

The Impact of a Graded Maximal Exercise Protocol on Exhaled Volatile Organic Compounds: A Pilot Study

Exhaled volatile organic compounds (VOCs) are of interest due to their minimally invasive sampling procedure. Previous studies have investigated the impact of exercise, with evidence suggesting that breath VOCs reflect exercise-induced metabolic activity. However, these studies have yet to investigate the impact of maximal exercise to exhaustion on breath VOCs, which was the main aim of this study. Two-litre breath samples were collected onto thermal desorption tubes using a portable breath collection unit. Samples were collected pre-exercise, and at 10 and 60 min following a maximal exercise test (VO2MAX). Breath VOCs were analysed by thermal desorption-gas chromatography-mass spectrometry using a non-targeted approach. Data showed a tendency for reduced isoprene in samples at 10 min post-exercise, with a return to baseline by 60 min. However, inter-individual variation meant differences between baseline and 10 min could not be confirmed, although the 10 and 60 min timepoints were different (p = 0.041). In addition, baseline samples showed a tendency for both acetone and isoprene to be reduced in those with higher absolute VO2MAX scores (mL(O2)/min), although with restricted statistical power. Baseline samples could not differentiate between relative VO2MAX scores (mL(O2)/kg/min). In conclusion, these data support that isoprene levels are dynamic in response to exercise.

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time

Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.

Detection of transdermal biomarkers using gradient-based colorimetric array sensor

Accurate assessment of dietary macronutrients intake is critical for the effective management of multiple diseases, such as obesity, diabetes, cardiovascular disease, metabolic disease, and cancer. Conventional self-reporting method is burdensome, inaccurate, and often biased. Though blood analysis and breath analysis can provide evidence-based information, they are either invasive or subject to human errors. Here we reported a wearable transdermal volatile biomarkers detection system based on novel colorimetric sensing technology for dietary macronutrients intake assessment. This technique quantifies the emission rates of transdermal volatile biomarkers via a gradient-based colorimetric array sensor (GCAS). The optical system of the GCAS device tracks the localized color development associated with the chemical reaction between the volatile biomarkers and the porous sensing probes, and determines the biomarkers emission rates through image processing algorithms. The localized chemical reaction and the image-based signal processing also make the GCAS capable for multiplexed detection of multiple analytes simultaneously. The GCAS sensor has been applied for transdermal acetone detection on 5 subjects in a keto diet intervention. The study indicates that the transdermal acetone increases after the subjects consuming keto diets and it decreases to basal level after intaking carb-rich diets. The transdermal acetone response from the GCAS sensor correlates well with breath acetone concentration in the range between 0 and 40 ppm and the correlation factor (R²) is as high as 0.8877. This method provides a noninvasive, low-cost, and wearable tool for assessing dietary macronutrients intake outside of lab or hospital settings. It could be widely applied in disease management, weight control, and nutrition management.

Exhaled phospholipid transfer protein and hepatocyte growth factor receptor in lung adenocarcinoma

BACKGROUND

Screening decreases mortality among lung cancer patients but is not widely implemented, thus there is an unmet need for an easily accessible non-invasive method to enable early diagnosis. Particles in exhaled air offer a promising such diagnostic tool. We investigated the validity of a particles in exhaled air device (PExA) to measure the particle flow rate (PFR) and collect exhaled breath particles (EBP) to diagnose primary lung adenocarcinoma (LUAD).

METHODS

Seventeen patients listed for resection of LUAD stages IA-IIIA and 18 non-cancer surgical control patients were enrolled. EBP were collected before and after surgery for LUAD, and once for controls. Proteomic analysis was carried out using a proximity extension assay technology. Results were validated in both plasma from the same cohort and with microarray data from healthy lung tissue and LUAD tissue in the GSE10072 dataset.

RESULTS

Of the 92 proteins analyzed, levels of five proteins in EBP were significantly higher in the LUAD patients compared to controls. Levels of phospholipid transfer protein (PLTP) and hepatocyte growth factor receptor (MET) decreased in LUAD patients after surgery compared to control patients. PFR was significantly higher in the LUAD cohort at all timepoints compared to the control group. MET in plasma correlated significantly with MET in EBP.

CONCLUSION

Collection of EBP and measuring of PFR has never been performed in patients with LUAD. In the present study PFR alone could distinguish between LUAD and patients without LUAD. PLTP and MET were identified as potential biomarkers to evaluate successful tumor excision.

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

BACKGROUND AND AIMS

In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.

MATERIALS AND METHODS

A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.

RESULTS

Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI_95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.

CONCLUSIONS

Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.

Sensor-Embedded Face Masks for Detection of Volatiles in Breath: A Proof of Concept Study

The correlation between breath volatilome and health is prompting a growing interest in the development of sensors optimized for breath analysis. On the other hand, the outbreak of COVID-19 evidenced that breath is a vehicle of infection; thus, the introduction of low-cost and disposable devices is becoming urgent for a clinical implementation of breath analysis. In this paper, a proof of concept about the functionalization of face masks is provided. Porphyrin-based sensors are among the most performant devices for breath analysis, but since porphyrins are scarcely conductive, they make use of costly and bulky mass or optical transducers. To overcome this drawback, we introduce here a hybrid material made of conducting polymer and porphyrins. The resulting material can be easily deposited on the internal surface of standard FFP face masks producing resistive sensors that retain the chemical sensitivity of porphyrins implementing their combinatorial selectivity for the identification of volatile compounds and the classification of complex samples. The sensitivity of sensors has been tested with respect to a set of seven volatile compounds representative of diverse chemical families. Sensors react to all compounds but with a different sensitivity pattern. Functionalized face masks have been tested in a proof-of-concept test aimed at identifying changes of breath due to the ingestion of beverages (coffee and wine) and solid food (banana- and mint-flavored candies). Results indicate that sensors can detect volatile compounds against the background of normal breath VOCs, suggesting the possibility to embed sensors in face masks for extensive breath analysis

Hyphenated Mass Spectrometry versus Real-Time Mass Spectrometry Techniques for the Detection of Volatile Compounds from the Human Body

Mass spectrometry (MS) is an analytical technique that can be used for various applications in a number of scientific areas including environmental, security, forensic science, space exploration, agri-food, and numerous others. MS is also continuing to offer new insights into the proteomic and metabolomic fields. MS techniques are frequently used for the analysis of volatile compounds (VCs). The detection of VCs from human samples has the potential to aid in the diagnosis of diseases, in monitoring drug metabolites, and in providing insight into metabolic processes. The broad usage of MS has resulted in numerous variations of the technique being developed over the years, which can be divided into hyphenated and real-time MS techniques. Hyphenated chromatographic techniques coupled with MS offer unparalleled qualitative analysis and high accuracy and sensitivity, even when analysing complex matrices (breath, urine, stool, etc.). However, these benefits are traded for a significantly longer analysis time and a greater need for sample preparation and method development. On the other hand, real-time MS techniques offer highly sensitive quantitative data. Additionally, real-time techniques can provide results in a matter of minutes or even seconds, without altering the sample in any way. However, real-time MS can only offer tentative qualitative data and suffers from molecular weight overlap in complex matrices. This review compares hyphenated and real-time MS methods and provides examples of applications for each technique for the detection of VCs from humans.

Discrimination of SARS-CoV-2 Infections From Other Viral Respiratory Infections by Scent Detection Dogs

Background: Testing of possibly infected individuals remains cornerstone of containing the spread of SARS-CoV-2. Detection dogs could contribute to mass screening. Previous research demonstrated canines' ability to detect SARS-CoV-2-infections but has not investigated if dogs can differentiate between COVID-19 and other virus infections. Methods: Twelve dogs were trained to detect SARS-CoV-2 positive samples. Three test scenarios were performed to evaluate their ability to discriminate SARS-CoV-2-infections from viral infections of a different aetiology. Naso- and oropharyngeal swab samples from individuals and samples from cell culture both infected with one of 15 viruses that may cause COVID-19-like symptoms were presented as distractors in a randomised, double-blind study. Dogs were either trained with SARS-CoV-2 positive saliva samples (test scenario I and II) or with supernatant from cell cultures (test scenario III). Results: When using swab samples from individuals infected with viruses other than SARS-CoV-2 as distractors (test scenario I), dogs detected swab samples from SARS-CoV-2-infected individuals with a mean diagnostic sensitivity of 73.8% (95% CI: 66.0-81.7%) and a specificity of 95.1% (95% CI: 92.6-97.7%). In test scenario II and III cell culture supernatant from cells infected with SARS-CoV-2, cells infected with other coronaviruses and non-infected cells were presented. Dogs achieved mean diagnostic sensitivities of 61.2% (95% CI: 50.7-71.6%, test scenario II) and 75.8% (95% CI: 53.0-98.5%, test scenario III), respectively. The diagnostic specificities were 90.9% (95% CI: 87.3-94.6%, test scenario II) and 90.2% (95% CI: 81.1-99.4%, test scenario III), respectively. Conclusion: In all three test scenarios the mean specificities were above 90% which indicates that dogs can distinguish SARS-CoV-2-infections from other viral infections. However, compared to earlier studies our scent dogs achieved lower diagnostic sensitivities. To deploy COVID-19 detection dogs as a reliable screening method it is therefore mandatory to include a variety of samples from different viral respiratory tract infections in dog training to ensure a successful discrimination process.

Analysis of 2D nanomaterial BC3 for COVID-19 biomarker ethyl butyrate sensor

Ethyl butyrate (EB) was identified in recent research as a prominent biomarker of COVID-19, as concentrations of EB were higher in exhaled breath of COVID-19 patients. Electronic sensitivities of pristine, Al- and Si-doped BC₃ nanosheets to the EB molecule were investigated in this study using density functional theory. It is found that the pure BC₃ was ineffective in sensing EB due to low adsorption energy and sensitivity. Aluminum- and silicon-doped BC₃ nanosheets were effective in forming a strong interaction with EB and were also sensitive. Our calculations show that the band gaps of the Al-doped and Si-doped BC₃ sheets were significantly decreased upon EB adsorption, which increased the electrical conductance of the sheets and the sensitivity. However, Si-doped BC₃ had a recovery time of almost 22 hours, making it less potent than Al-doped BC₃, which had a recovery time of just 7.7 minutes. The shorter recovery time of the Al-doped BC₃ sheet is due to its moderate adsorption energy of 25.8 kcal mol^-1. These results can help facilitate the development of an EB biosensor for COVID-19 testing and other similar applications.

Cancer nanotechnology: current status and perspectives

Modern medicine has been waging a war on cancer for nearly a century with no tangible end in sight. Cancer treatments have significantly progressed, but the need to increase specificity and decrease systemic toxicities remains. Early diagnosis holds a key to improving prognostic outlook and patient quality of life, and diagnostic tools are on the cusp of a technological revolution. Nanotechnology has steadily expanded into the reaches of cancer chemotherapy, radiotherapy, diagnostics, and imaging, demonstrating the capacity to augment each and advance patient care. Nanomaterials provide an abundance of versatility, functionality, and applications to engineer specifically targeted cancer medicine, accurate early-detection devices, robust imaging modalities, and enhanced radiotherapy adjuvants. This review provides insights into the current clinical and pre-clinical nanotechnological applications for cancer drug therapy, diagnostics, imaging, and radiation therapy.

Targeted volatolomics of human monocytes: Comparison of 2D-GC/TOF-MS and 1D-GC/Orbitrap-MS methods

Blood is a complex biological matrix providing valuable information on nutritional, metabolic, and immune status. The detection of blood biomarkers requires sensitive analytical methods because analytes are at very low concentrations. Peripheral blood monocytes play a crucial role in inflammatory processes, and the metabolites released by monocytes during these processes might serve as important signalling molecules and biomarkers of particular physiological states. Headspace solid-phase microextraction (HS-SPME) combined with two different mass spectrometric platforms, two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (2D-GC/TOF-MS) and one-dimensional gas chromatography coupled to Orbitrap mass spectrometry (GC/Orbitrap-MS), were applied for the investigation of volatile organic compounds (VOCs) produced by human peripheral blood monocytes. An optimized method was subsequently applied for the characterization of changes in VOCs induced by lipopolysaccharides (LPS) and zymosan (ZYM) stimulation. Overall, the 2D-GC/TOF-MS and the 1D-GC/Orbitrap-MS analyses each yielded about 4000 and 400 peaks per sample, respectively. In total, 91 VOCs belonging to eight different chemical classes were identified. The samples were collected in two fractions, conditioned media for monitoring extracellularly secreted molecules and cell pellet samples to determine the intracellular composition of VOCs. Alcohols, ketones, and hydrocarbons were the main chemical classes of the metabolic profile identified in cell fractions. Aldehydes, acids and cyclic compounds were characteristic of the conditioned media fraction. Here we demonstrate that HS-SPME-2D-GC/TOF-MS is more suitable for the identification of specific VOC profiles produced by human monocytes than 1D-GC/Orbitrap-MS. We define the signature of VOCs occurring early after monocyte activation and characterise the signalling compounds released by immune cells into media.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

The Role of Oxidative Stress in Sarcoidosis

Sarcoidosis is a rare, systemic inflammatory disease whose diagnosis and management can pose a challenge for clinicians and specialists. Scientific knowledge on the molecular pathways that drive its development is still lacking, with no standardized therapies available and insufficient strategies to predict patient outcome. In recent years, oxidative stress has been highlighted as an important factor in the pathogenesis of sarcoidosis, involving several enzymes and molecules in the mechanism of the disease. This review presents current data on the role of oxidative stress in sarcoidosis and its interaction with inflammation, as well as the application of antioxidative therapy in the disease.

Effect of Ozone, Clothing, Temperature, and Humidity on the Total OH Reactivity Emitted from Humans

People influence indoor air chemistry through their chemical emissions via breath and skin. Previous studies showed that direct measurement of total OH reactivity of human emissions matched that calculated from parallel measurements of volatile organic compounds (VOCs) from breath, skin, and the whole body. In this study, we determined, with direct measurements from two independent groups of four adult volunteers, the effect of indoor temperature and humidity, clothing coverage (amount of exposed skin), and indoor ozone concentration on the total OH reactivity of gaseous human emissions. The results show that the measured concentrations of VOCs and ammonia adequately account for the measured total OH reactivity. The total OH reactivity of human emissions was primarily affected by ozone reactions with organic skin-oil constituents and increased with exposed skin surface, higher temperature, and higher humidity. Humans emitted a comparable total mixing ratio of VOCs and ammonia at elevated temperature-low humidity and elevated temperature-high humidity, with relatively low diversity in chemical classes. In contrast, the total OH reactivity increased with higher temperature and higher humidity, with a larger diversity in chemical classes compared to the total mixing ratio. Ozone present, carbonyl compounds were the dominant reactive compounds in all of the reported conditions.

Updates, Controversies, and Emerging Approaches in Colorectal Screening

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths. Despite the threatening statistics, the US burden for CRC has been decreasing, which is likely multifactorial and has partial contribution from widespread timely screening, more advanced CRC treatment, and daily aspirin use in some patients. While overall death rate from CRC decreased by approximately a half between 1975 and 2012, epidemiologic studies demonstrate that CRC incidence is increasing in the younger population. This pattern has prompted the American Cancer Society (ACS) to revise their guidelines. In this review, we plan to discuss the most recent changes in guidelines, data to support them, controversies concerning CRC screening methods, age to start and to stop screening, and post-colonoscopy/polypectomy surveillance guidelines.

Exhaled Volatile Organic Compounds for Identifying Patients With Chronic Pulmonary Aspergillosis

Background: Diagnosing chronic pulmonary aspergillosis is a major challenge in clinical practice. The development and validation of a novel, sensitive and specific assay for diagnosing chronic pulmonary aspergillosis is urgently needed.

Methods: From April 2018 to June 2019, 53 patients with chronic pulmonary aspergillosis (CPA), 32 patients with community-acquired pneumonia (CAP) and 48 healthy controls were recruited from the First Affiliated Hospital of Guangzhou Medical University. Clinical characteristics and samples were collected at enrollment. All exhaled breath samples were analyzed offline using thermal desorption single-photon ionization time-of-flight mass spectrometry; to analyze the metabolic pathways of the characteristic volatile organic compounds, serum samples were subjected to ultrahigh-performance liquid chromatography.

Results: We identified characteristic volatile organic compounds in patients with chronic pulmonary aspergillosis, which mainly consisted of phenol, neopentyl alcohol, toluene, limonene and ethylbenzene. These compounds were assessed using a logistic regression model. The sensitivity and specificity were 95.8 and 96.9% for discriminating patients in the CPA group from those in the CAP group and 95.8 and 97.9% for discriminating patients in the CPA group from healthy controls, respectively. The concentration of limonene (m/z 136) correlated significantly positively with anti-Aspergillus fumigatus IgG antibody titers (r = 0.420, P < 0.01). After antifungal treatment, serum IgG and the concentration of limonene (m/z 136) decreased in the subgroup of patients with chronic pulmonary aspergillosis.

Conclusions: We identified VOCs that can be used as biomarkers for differential diagnosis and therapeutic response prediction in patients with chronic pulmonary aspergillosis.

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background

The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC).

Methods

Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning.

Results

Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86–90%.

Conclusions

Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01862-z.

Blood, urine and semen Volatile Organic Compound (VOC) pattern analysis for assessing health environmental impact in highly polluted areas in Italy

Volatile Organic Compound (VOC) analysis is usually applied in pollution assessment by checking for toxic or harmful volatile compounds in air, water and soil samples. In this study, exogenous VOCs and their derivatives, metabolized by cells, were valued into specific body fluids. In particular, the VOC profiles of blood, urine and human semen samples collected from young men living in two high pollution areas in Italy, i.e. Land of Fires and Valley of Sacco River, were fingerprinted. The analysis is based on Headspace Solid Phase MicroExtraction (HS-SPME) followed by Gas Chromatography-Mass Spectrometric detection (GC-MS). The volatile composition of the three body fluids showed that some VOCs are in common between blood, urine and human semen samples, whereas others are present only in a body fluid. Some compounds, as well as also some chemical classes show a higher affinity for a specific body fluid. Statistical analysis allowed to discriminate the two contaminated areas and identify those compounds which significantly contribute to the two areas classification. Some of these compounds are toxic and found prevalently in Valley of Sacco River samples, correspondingly to sperm analysis results for young men living in this zona worse than those living in Land of Fires.

Liver Impairment-The Potential Application of Volatile Organic Compounds in Hepatology

Liver diseases are currently diagnosed through liver biopsy. Its invasiveness, costs, and relatively low diagnostic accuracy require new techniques to be sought. Analysis of volatile organic compounds (VOCs) in human bio-matrices has received a lot of attention. It is known that a musty odour characterises liver impairment, resulting in the elucidation of volatile chemicals in the breath and other body fluids such as urine and stool, which may serve as biomarkers of a disease. Aims: This study aims to review all the studies found in the literature regarding VOCs in liver diseases, and to summarise all the identified compounds that could be used as diagnostic or prognostic biomarkers. The literature search was conducted on ScienceDirect and PubMed, and each eligible publication was qualitatively assessed by two independent evaluators using the SANRA critical appraisal tool. Results: In the search, 58 publications were found, and 28 were kept for inclusion: 23 were about VOCs in the breath, one in the bile, three in urine, and one in faeces. Each publication was graded from zero to ten. A graphical summary of the metabolic pathways showcasing the known liver disease-related VOCs and suggestions on how VOC analysis on liver impairment could be applied in clinical practice are given.

Cheap and easy human breath collection system for trace volatile organic compounds screening using thermal desorption - gas chromatography mass spectrometry

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By analyzing the VOCs presents in our breath, we could identify if some components should not be present in our bodies, or their concentration is higher or lower than normal.

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To collect breath samples for VOC analysis, we looked into the current available methodologies and, due to their high prices, tried to develop our own easy and cheap device. A simple single use Minigrip LDPE plastic bag was used in this work and its efficiency and performance were tested.

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After breath collection, samples were analyzed using Thermal Desorption (TD) system, coupled with Gas Chromatography Mass Spectrometer (GC-MS).

C. elegans-based chemosensation strategy for the early detection of cancer metabolites in urine samples

Chemosensory receptors play a crucial role in distinguishing the wide range of volatile/soluble molecules by binding them with high accuracy. Chemosensation is the main sensory modality in organisms lacking long-range sensory mechanisms like vision/hearing. Despite its low number of sensory neurons, the nematode Caenorhabditis elegans possesses several chemosensory receptors, allowing it to detect about as many odorants as mammals. Here, we show that C. elegans displays attraction towards urine samples of women with breast cancer, avoiding control ones. Behavioral assays on animals lacking AWC sensory neurons demonstrate the relevance of these neurons in sensing cancer odorants: calcium imaging on AWC increases the accuracy of the discrimination (97.22%). Also, chemotaxis assays on animals lacking GPCRs expressed in AWC allow to identify receptors involved in binding cancer metabolites, suggesting that an alteration of a few metabolites is sufficient for the cancer discriminating behavior of C. elegans, which may help identify a fundamental fingerprint of breast cancer.

Identification of volatile compounds from bacteria by spectrometric methods in medicine diagnostic and other areas: current state and perspectives

Diagnosis of bacterial infections until today mostly relies on conventional microbiological methods. The resulting long turnaround times can lead to delayed initiation of adequate antibiotic therapy and prolonged periods of empiric antibiotic therapy (e.g., in intensive care medicine). Therewith, they contribute to the mortality of bacterial infections and the induction of multidrug resistances. The detection of species specific volatile organic compounds (VOCs) emitted by bacteria has been proposed as a possible diagnostic approach with the potential to serve as an innovative point-of-care diagnostic tool with very short turnaround times. A range of spectrometric methods are available which allow the detection and quantification of bacterial VOCs down to a range of part per trillion. This narrative review introduces the application of spectrometric analytical methods for the purpose of detecting VOCs of bacterial origin and their clinical use for diagnosing different infectious conditions over the last decade. KEY POINTS: • Detection of VOCs enables bacterial differentiation in various medical conditions. • Spectrometric methods may function as point-of-care diagnostics in near future.

Canine olfactory detection and its relevance to medical detection

The extraordinary olfactory sense of canines combined with the possibility to learn by operant conditioning enables dogs for their use in medical detection in a wide range of applications. Research on the ability of medical detection dogs for the identification of individuals with infectious or non-infectious diseases has been promising, but compared to the well-established and-accepted use of sniffer dogs by the police, army and customs for substances such as money, explosives or drugs, the deployment of medical detection dogs is still in its infancy. There are several factors to be considered for standardisation prior to deployment of canine scent detection dogs. Individual odours in disease consist of different volatile organic molecules that differ in magnitude, volatility and concentration. Olfaction can be influenced by various parameters like genetics, environmental conditions, age, hydration, nutrition, microbiome, conditioning, training, management factors, diseases and pharmaceuticals. This review discusses current knowledge on the function and importance of canines' olfaction and evaluates its limitations and the potential role of the dog as a biomedical detector for infectious and non-infectious diseases.

The "olfactory fingerprint": can diagnostics be improved by combining canine and digital noses?

A sniffer (detecting) dog is conventionally defined as an animal trained to use its olfactory perceptions for detecting a vast array of substances, mostly volatile organic compounds (VOCs), including those exceptionally or exclusively generated in humans bearing specific pathologies. Such an extraordinary sniffing performance translates into the capability of detecting compounds close to the femtomolar level, with performance comparable to that of current mass spectrometry-based laboratory applications. Not only can dogs accurately detect "abnormal volatilomes" reflecting something wrong happening to their owners, but they can also perceive visual, vocal and behavioral signals, which altogether would contribute to raise their alertness. Although it seems reasonable to conclude that sniffer dogs could never be considered absolutely "diagnostic" for a given disorder, several lines of evidence attest that they may serve as efficient screening aids for many pathological conditions affecting their human companions. Favorable results have been obtained in trials on cancers, diabetes, seizures, narcolepsy and migraine, whilst interesting evidence is also emerging on the capability of early and accurately identifying patients with infectious diseases. This would lead the way to proposing an "olfactory fingerprint" loop, where evidence that dogs can identify the presence of human pathologies provides implicit proof of the existence of disease-specific volatilomes, which can be studied for developing laboratory techniques. Contextually, the evidence that specific pathologies are associated with abnormal VOC generation may serve as reliable basis for training dogs to detect these compounds, even (or especially) in patients at an asymptomatic phase.

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis

Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%,I2= 62%), and specificity of 78% (95% CI, 70%-84%,I2= 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

Scent dog identification of SARS-CoV-2 infections in different body fluids

Background

The main strategy to contain the current SARS-CoV-2 pandemic remains to implement a comprehensive testing, tracing and quarantining strategy until vaccination of the population is adequate. Scent dogs could support current testing strategies.

Methods

Ten dogs were trained for 8 days to detect SARS-CoV-2 infections in beta-propiolactone inactivated saliva samples. The subsequent cognitive transfer performance for the recognition of non-inactivated samples were tested on three different body fluids (saliva, urine, and sweat) in a randomised, double-blind controlled study.

Results

Dogs were tested on a total of 5242 randomised sample presentations. Dogs detected non-inactivated saliva samples with a diagnostic sensitivity of 84% (95% CI: 62.5–94.44%) and specificity of 95% (95% CI: 93.4–96%). In a subsequent experiment to compare the scent recognition between the three non-inactivated body fluids, diagnostic sensitivity and specificity were 95% (95% CI: 66.67–100%) and 98% (95% CI: 94.87–100%) for urine, 91% (95% CI: 71.43–100%) and 94% (95% CI: 90.91–97.78%) for sweat, 82% (95% CI: 64.29–95.24%), and 96% (95% CI: 94.95–98.9%) for saliva respectively.

Conclusions

The scent cognitive transfer performance between inactivated and non-inactivated samples as well as between different sample materials indicates that global, specific SARS-CoV-2-associated volatile compounds are released across different body secretions, independently from the patient’s symptoms. All tested body fluids appear to be similarly suited for reliable detection of SARS-CoV-2 infected individuals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06411-1.

Pathophysiology of SARS-CoV-2 Infection in the Upper Respiratory Tract and Its Relation to Breath Volatile Organic Compounds

Among the many products of metabolic processes are volatile organic compounds (VOCs). In the airways, these volatile metabolites are emitted through breathing and thus are easily sampled for analysis. Recent work has connected the functions and structure of the human microbiome with health and disease. Alteration in microbial function in this context can result in differences in metabolite composition, including that of VOCs, presenting the possibility of a new noninvasive method for clinical diagnosis. Screening methods that assess VOCs arising from changes in the airway microbiome could be highly useful in diagnosing viral upper respiratory tract infections (URTIs), e.g., COVID-19, which are highly contagious and have an enormous public health impact worldwide. A rapid noninvasive screening test for URTIs would pose major advantages in containing the disease. As early evidence shows that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection alters the human microbiome (both in the gut and the respiratory tract), we propose that detection of a VOC signature of an altered nasal microbiome could be fruitful as a rapid noninvasive measure of URTI in general and of SARS-CoV-2 in particular.

Exhaled volatilome analysis as a useful tool to discriminate asthma with other coexisting atopic diseases in women of childbearing age

The prevalence of asthma is considerably high among women of childbearing age. Most asthmatic women also often have other atopic disorders. Therefore, the differentiation between patients with atopic diseases without asthma and asthmatics with coexisting diseases is essential to avoid underdiagnosis of asthma and to design strategies to reduce symptom severity and improve quality of life of patients. Hence, we aimed for the first time to conduct an analysis of volatile organic compounds in exhaled breath of women of childbearing age as a new approach to discriminate between asthmatics with other coexisting atopic diseases and non-asthmatics (with or without atopic diseases), which could be a helpful tool for more accurate asthma detection and monitoring using a noninvasive technique in the near future. In this study, exhaled air samples of 336 women (training set (n = 211) and validation set (n = 125)) were collected and analyzed by thermal desorption coupled with gas chromatography-mass spectrometry. ASCA (ANOVA (analysis of variance) simultaneous component analysis) and LASSO + LS (least absolute shrinkage and selection operator + logistic regression) were employed for data analysis. Fifteen statistically significant models (p-value < 0.05 in permutation tests) that discriminated asthma with other coexisting atopic diseases in women of childbearing age were generated. Acetone, 2-ethyl-1-hexanol and a tetrahydroisoquinoline derivative were selected as discriminants of asthma with other coexisting atopic diseases. In addition, carbon disulfide, a tetrahydroisoquinoline derivative, 2-ethyl-1-hexanol and decane discriminated asthma disease among patients with other atopic disorders. Results of this study indicate that refined metabolomic analysis of exhaled breath allows asthma with other coexisting atopic diseases discrimination in women of reproductive age.

COVID-19 sniffer dog experimental training: Which protocol and which implications for reliable sidentification?

The introduction of trained sniffer dogs for COVID‐19 detection could be an opportunity, as previously described for other diseases. Dogs could be trained to detect volatile organic compounds (VOCs), the whiff of COVID‐19. Dogs involved in the study were three, one male and two females from different breeds, Black German Shepherd, German Shepherd, and Dutch Shepherd. The training was performed using sweat samples from SARS‐CoV2 positive patients and from SARS‐Cov2 free patients admitted at the University Hospital Campus Bio‐medico of Rome. Gauze with sweat was collected in a glass jar with a metal top and put in metal boxes used for dog training. The dog training protocol was performed in two phases: the olfactory conditioning and the olfactory discrimination research. The training planning was focused on the switch moment for the sniffer dog, the moment when the dog was able to identify VOCs specific for COVID‐19. At this time, the dog was able to identify VOCs specific for COVID‐19 with significant reliability, in terms of the number of correct versus incorrect (p < 0.0001) reporting. In conclusion, this protocol could provide a useful tool for sniffer dogs' training and their introduction in a mass screening context. It could be cheaper and faster than a conventional testing method.