Clinical application of volatile organic compound analysis for detecting infectious diseases. Clin Microbiol Rev. 2013;26:462-475. [PMID: 23824368 DOI: 10.1128/cmr.00020-13]

Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks

Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol.

Fecal Volatile Organic Compound Profiles are Not Influenced by Gestational Age and Mode of Delivery: A Longitudinal Multicenter Cohort Study

Fecal volatile organic compounds (VOC) reflect human and gut microbiota metabolic pathways and their interaction. VOC behold potential as non-invasive preclinical diagnostic biomarkers in various diseases, e.g., necrotizing enterocolitis and late onset sepsis. There is a need for standardization and assessment of the influence of clinical and environmental factors on the VOC outcome before this technique can be applied in clinical practice. The aim of this study was to investigate the influence of gestational age (GA) and mode of delivery on the fecal VOC pattern in preterm infants born below 30 weeks of gestation. Longitudinal fecal samples, collected on days 7, 14, and 21 postnatally, were analyzed by an electronic nose device (Cyranose 320^®). In total, 58 preterm infants were included (29 infants born at GA 24–26 weeks vs. 29 at 27–29 completed weeks, 24 vaginally born vs. 34 via C-section). No differences were identified at any predefined time point in terms of GA and delivery mode (p > 0.05). We, therefore, concluded that correction for these factors in this population is not warranted when performing fecal VOC analysis in the first three weeks of life.

Smell - Adding a New Dimension to Urinalysis

Background: Urinary tract infections (UTI) are among the most common infections in children. The primary tool to detect UTI is dipstick urinalysis; however, this has limited sensitivity and specificity. Therefore, urine culture has to be performed to confirm a UTI. Urinary volatile organic compounds (VOC) may serve as potential biomarker for diagnosing UTI. Previous studies on urinary VOCs focused on detection of UTI in a general population; therefore, this proof-of-principle study was set up in a clinical high-risk pediatric population. Methods: This study was performed at a tertiary nephro-urological clinic. Patients included were 0–18 years, clinically suspected of a UTI, and had abnormal urinalysis. Urine samples were divided into four groups, i.e., urine without bacterial growth, contamination, colonization, and UTI. VOC analysis was performed using an electronic nose (eNose) (Cyranose 320^®) and VOC profiles of subgroups were compared. Results: Urinary VOC analysis discriminated between UTI and non-UTI samples (AUC 0.70; p = 0.048; sensitivity 0.67, specificity 0.70). The diagnostic accuracy of VOCs improved when comparing urine without bacterial growth versus with UTI (AUC 0.80; p = 0.009, sensitivity 0.79, specificity 0.75). Conclusions: In an intention-to-diagnose high-risk pediatric population, UTI could be discriminated from non-UTI by VOC profiling, using an eNose. Since eNose can be used as bed-side test, these results suggest that urinary VOC analysis may serve as an adjuvant in the diagnostic work-up of UTI in children.

Nanomaterial-Enabled Flexible and Stretchable Sensing Systems: Processing, Integration, and Applications

Nanomaterial-enabled flexible and stretchable electronics have seen tremendous progress in recent years, evolving from single sensors to integrated sensing systems. Compared with nanomaterial-enabled sensors with a single function, integration of multiple sensors is conducive to comprehensive monitoring of personal health and environment, intelligent human-machine interfaces, and realistic imitation of human skin in robotics and prosthetics. Integration of sensors with other functional components promotes real-world applications of the sensing systems. Here, an overview of the design and integration strategies and manufacturing techniques for such sensing systems is given. Then, representative nanomaterial-enabled flexible and stretchable sensing systems are presented. Following that, representative applications in personal health, fitness tracking, electronic skins, artificial nervous systems, and human-machine interactions are provided. To conclude, perspectives on the challenges and opportunities in this burgeoning field are considered.

A "Brick" Mass Spectrometer with Photoionization for Direct Analysis of Trace Volatile Compounds

With high portability and favorable performance, miniature mass spectrometers have become one of the most attractive tools for on-site analysis of trace volatile compounds. Based on the "Brick" mass spectrometer (BMS) developed previously, a hand-held BMS integrated with a photoionization source (PI-BMS) was developed in this study for volatile compound analysis. With compact dimensions of 30 cm × 18.5 cm × 27.6 cm (length × width × height), the PI-BMS was equipped with a 10.6 eV UV lamp and capable of generating molecular ions. The capabilities of qualitative and quantitative analyses for different volatile samples were demonstrated and characterized. Under optimized conditions, high detection sensitivity in open air was obtained for the PI-BMS with a limit of detection (LOD) of ∼10 ppbv. As demonstrations of mixture analysis, four different fresh fruits were directly analyzed using PI-BMS, observing characteristic mass spectra for each type of fruit.

Volatile disease markers of American foulbrood-infected larvae in Apis mellifera

The honey bee, which lives in the crowded environment of a social hive, is vulnerable to disease infection and spread. Despite efforts to develop various diagnostic methods, American foulbrood (AFB) caused by Paenibacillus larvae infection has caused enormous damage to the apicultural industry. Here, we investigated the volatile organic compounds derived from AFB. After inoculation of the AFB pathogen in honey bee larvae under lab conditions, we identified propionic acid, valeric acid, and 2-nonanone as volatile disease markers (VDMs) of AFB infection using GC/MS. Electrophysiological recordings demonstrated that middle-aged bees, the hygienic-aged bees, are more sensitive to these VDMs than the foragers. Thus, these VDMs have the potential to be efficient and significant cues for worker detection of AFB infected larvae in bee hives. This study supports the idea that the specific olfactory sensitivity of different worker bees depends on their tasks. Taken together, our finding is crucial and sufficient to develop novel disease volatile markers associated with honey bee diseases to diagnose and study the molecular and neural correlates of given hygienic behavior detecting these volatile chemicals by honey bees.

Fabrication of SnO2 Composite Nanofiber-Based Gas Sensor using the Electrospinning Method for Tetrahydrocannabinol (THC) Detection

This paper presents the development of a metal oxide semiconductor (MOS) sensor for the detection of volatile organic compounds (VOCs) which are of great importance in many applications involving either control of hazardous chemicals or noninvasive diagnosis. In this study, the sensor is fabricated based on tin dioxide (SnO₂) and poly(ethylene oxide) (PEO) using electrospinning. The sensitivity of the proposed sensor is further improved by calcination and gold doping. The gold doping of composite nanofibers is achieved using sputtering, and the calcination is performed using a high-temperature oven. The performance of the sensor with different doping thicknesses and different calcination temperatures is investigated to identify the optimum fabrication parameters resulting in high sensitivity. The optimum calcination temperature and duration are found to be 350 °C and 4 h, respectively and the optimum thickness of the gold dopant is found to be 10 nm. The sensor with the optimum fabrication process is then embedded in a microchannel coated with several metallic and polymeric layers. The performance of the sensor is compared with that of a commercial sensor. The comparison is performed for methanol and a mixture of methanol and tetrahydrocannabinol (THC) which is the primary psychoactive constituent of cannabis. It is shown that the proposed sensor outperforms the commercial sensor when it is embedded inside the channel.

Rapid Extraction and Analysis of Volatile Solutes with an Effervescent Tablet

Extraction of volatile compounds from complex liquid matrices is a critical step in volatile compound analysis workflows. Recently, green chemistry principles are increasingly implemented in extraction processes. Some of the available approaches are solvent-free but still require concentration or trapping of analytes. Here, we propose effervescent tablet-induced extraction (ETIE) as a method of transferring volatile/semivolatile compounds from liquid matrices to the gas phase for analysis. This technique relies on the release of carbon dioxide produced in situ during a neutralization reaction, which occurs when a tablet is inserted into an aqueous sample matrix. In this process, many bubbles of carbon dioxide are instantly formed in the sample matrix. The bubbles rapidly extract and liberate volatile compounds from the sample. The gaseous effluent is then immediately transferred to a detector (atmospheric pressure chemical ionization mass spectrometry (MS) or gas chromatography (GC) hyphenated with MS). ETIE-GC-MS can be used for analysis of volatile compounds present in real samples. The method was validated for analysis of selected ethyl esters present in a yogurt drink. The calibration data set was linear over a range from 5 × 10^-7 to 1 × 10^-5 M. The limits of detection ranged from 1.51 × 10^-7 to 6.82 × 10^-7 M, while the recoveries ranged from 71 to 118%. Inter- and intraday precision of selected ethyl esters in aqueous solution was satisfactory (relative standard deviation, 3.6-18.3%). Furthermore, it is shown that ETIE improves the performance of headspace solid-phase microextraction while eliminating the need for heating and shaking samples.

Nanomaterial-based gas sensors used for breath diagnosis

Gas-sensing applications commonly use nanomaterials (NMs) because of their unique physicochemical properties, including a high surface-to-volume ratio, enormous number of active sites, controllable morphology, and potential for miniaturisation.

Comparison of Stir Bar Sorptive Extraction and Solid Phase Microextraction of Volatile and Semi-Volatile Metabolite Profile of Staphylococcus Aureus

For the analysis of volatile bacterial compounds, solid phase microextraction (SPME) is currently the most widely used metabolite concentration technique. Recently, the potential of stir bar sorptive extraction (SBSE) for this use has been demonstrated. These two approaches were therefore used in combination with gas-chromatography coupled with mass-spectrometry (GC–MS) for the analysis of volatile and semi-volatile bacterial compounds produced by Staphylococcus aureus. In both cases, SPME and SBSE/headspace sorptive extraction (HSSE) enrichment was carried out in two coating phases. A whole analytical and statistical process was developed to differentiate the metabolites produced from the metabolites consumed. The results obtained with SBSE/HSSE and SPME were compared and showed the recovery of 90% of the compounds by SBSE/HSSE. In addition, we were able to detect the production of 12 volatile/semi-volatile compounds by S. aureus, six of which had never been reported before. The extraction by SBSE/HSSE showed higher concentration capacities and greater sensitivity than SPME concerning bacterial compounds, suggesting that this technique may therefore become the new preferred option for bacterial volatile and semi-volatile compound analysis.

Non-invasive Biodiversified Sensors: A Modernized Screening Technology for Cancer

Background:

Biological sensors revolutionize the method of diagnoses of diseases from early to final stages using the biomarkers present in the body. Biosensors are advantageous due to the involvement of minimal sample collection with improved specificity and sensitivity for the detection of biomarkers.

Methods:

Conventional biopsies restrict problems like patient non-compliance, cross-infection and high cost and to overcome these issues biological samples like saliva, sweat, urine, tears and sputum progress into clinical and diagnostic research for the development of non-invasive biosensors. This article covers various non-invasive measurements of biological samples, optical-based, mass-based, wearable and smartphone-based biosensors for the detection of cancer.

Results:

The demand for non-invasive, rapid and economic analysis techniques escalated due to the modernization of the introduction of self-diagnostics and miniature forms of devices. Biosensors have high sensitivity and specificity for whole cells, microorganisms, enzymes, antibodies, and genetic materials.

Conclusion:

Biosensors provide a reliable early diagnosis of cancer, which results in faster therapeutic outcomes with in-depth fundamental understanding of the disease progression.

On-line profiling of volatile compounds produced in vitro by pathogenic oral bacteria

Infections by oral pathogens are one of the most common health problems worldwide. Due to the intimate connection between exhaled breath and the oral cavity, breath analysis could potentially be used to diagnose these infections. However, little is known about the volatile emissions of important oral pathogens that are connected with gingivitis and periodontitis. In this study, we have performed in vitro headspace measurements on four important oral pathogens (P. gingivalis, T. forsythia, P. intermedia and P. nigrescens) using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). Some of the most abundant compounds produced by the bacteria include hydrogen sulphide, methanethiol, acetone, dimethylsulphide, isoprene, cyclopentanone and indole as tentatively assigned from the mass spectra. Several other abundant mass signals were recorded but the assignment of these is less certain. Some of the bacterial species can be separated from each other by the emitted volatile fingerprints. The results of this study can be used in potential development of a diagnostic breath test for oral infections. In addition, as several of the measured compounds are known to be toxic, the results point to an intriguing possibility of studying the connection between the bacterial virulence and the emitted volatile compounds.

Volatile scents of influenza A and S. pyogenes (co-)infected cells

Influenza A is a serious pathogen itself, but often leads to dangerous co-infections in combination with bacterial species such as Streptococcus pyogenes. In comparison to classical biochemical methods, analysis of volatile organic compounds (VOCs) in headspace above cultures can enable destruction free monitoring of metabolic processes in vitro. Thus, volatile biomarkers emitted from biological cell cultures and pathogens could serve for monitoring of infection processes in vitro. In this study we analysed VOCs from headspace above (co)-infected human cells by using a customized sampling system. For investigating the influenza A mono-infection and the viral-bacterial co-infection in vitro, we analysed VOCs from Detroit cells inoculated with influenza A virus and S. pyogenes by means of needle-trap micro-extraction (NTME) and gas chromatography mass spectrometry (GC-MS). Besides the determination of microbiological data such as cell count, cytokines, virus load and bacterial load, emissions from cell medium, uninfected cells and bacteria mono-infected cells were analysed. Significant differences in emitted VOC concentrations were identified between non-infected and infected cells. After inoculation with S. pyogenes, bacterial infection was mirrored by increased emissions of acetaldehyde and propanal. N-propyl acetate was linked to viral infection. Non-destructive monitoring of infections by means of VOC analysis may open a new window for infection research and clinical applications. VOC analysis could enable early recognition of pathogen presence and in-depth understanding of their etiopathology.

The smell of longevity: a combination of Volatile Organic Compounds (VOCs) can discriminate centenarians and their offspring from age-matched subjects and young controls

Aging is characterized by dynamic changes at metabolic level that lead to modifications in the composition of the metabolome. Since the identification of biomarkers that can discriminate people of different age and health status has recently attracted a great interest, we wondered whether age-specific changes in the metabolome could be identified and serve as new and informative biomarkers of aging and longevity. In the last few years, a specific branch of metabonomics devoted to the study of volatile organic compounds (VOCs) has been developed. To date, little is known about the profile of specific VOCs in healthy aging and longevity in humans; therefore, we investigated the profile of VOCs in both urine and feces samples from 73 volunteers of different age including centenarians that represent useful "super-controls" to identify potential biomarkers of successful aging and footprints of longevity. To this purpose, we performed a discriminant analysis by which we were able to identify specific profiles of urinary and fecal VOCs. Such profiles can discriminate different age groups, from young to centenarians, and, even more interesting, centenarians' offspring from age-matched controls. Moreover, we were able to identify VOCs that are specific for the couples "centenarians - offspring" or the trios "centenarians - offspring - spouse," suggesting the possible existence of a familiar component also for VOCs profile.

Exhaled Volatile Organic Compounds Precedes Pulmonary Injury in a Swine Pulmonary Oxygen Toxicity Model

Purpose

Inspiring high partial pressure of oxygen (FiO₂ > 0.6) for a prolonged duration can lead to lung damage termed pulmonary oxygen toxicity (PO₂T). While current practice is to limit oxygen exposure, there are clinical and military scenarios where higher FiO₂ levels and partial pressures of oxygen are required. The purpose of this study is to develop a non-invasive breath-based biomarker to detect PO₂T prior to the onset of clinical symptoms.

Methods

Male Yorkshire swine (20–30 kg) were placed into custom airtight runs and randomized to air (0.209 FiO₂, n = 12) or oxygen (>0.95 FiO₂, n = 10) for 72 h. Breath samples, arterial blood gases, and vital signs were assessed every 12 h. After 72 h of exposure, animals were euthanized and the lungs processed for histology and wet-dry ratios.

Results

Swine exposed to hyperoxia developed pulmonary injury consistent with PO₂T. Histology of oxygen-exposed swine showed pulmonary lymphatic congestion, epithelial sloughing, and neutrophil transmigration. Pulmonary injury was also evidenced by increased interstitial edema and a decreased PaO₂/FiO₂ ratio in the oxygen group when compared to the air control group. Breath volatile organic compound (VOC) sample analysis identified six VOCs that were combined into an algorithm which generated a breath score predicting PO₂T with a ROC/AUC curve of 0.72 defined as a of PaO₂/FiO₂ ratio less than 350 mmHg.

Conclusion

Exposing swine to 72 h of hyperoxia induced a pulmonary injury consistent with human clinical endpoints of PO₂T. VOC analysis identified six VOCs in exhaled breath that preceded PO₂T. Results show promise that a simple, non-invasive breath test could potentially predict the risk of pulmonary injury in humans exposed to high partial pressures of oxygen.

Volatile metabolomic signatures of rabies immunization in two mesocarnivore species

Rabies is a zoonotic disease caused by infection with rabies virus, which circulates naturally in several wild carnivore and bat reservoirs in the United States (US). The most important reservoir in the US from an animal and public health perspective is the raccoon (Procyon lotor). To prevent the westward expansion of a significant raccoon rabies epizootic along the eastern seaboard, an operational control program implementing oral rabies vaccination (ORV) has existed in the US since the 1990s. Recently, two vaccine efficacy studies conducted with raccoons and striped skunks (Mephitis mephitis) provided the opportunity to determine if volatile fecal metabolites might be used to non-invasively monitor ORV programs and/or predict virus protection for these species. The volatile metabolome is a rich source of information that may significantly contribute to our understanding of disease and infection. Fecal samples were collected at multiple time points from raccoons and striped skunks subjected to oral treatment with rabies vaccine (or sham). Intramuscular challenge with a lethal dose of rabies virus was used to determine protection status at six (raccoons) and 11 (skunks) months post-vaccination. In addition to fecal samples, blood was collected at various time points to permit quantitative assessment of rabies antibody responses arising from immunization. Feces were analyzed by headspace gas chromatography with mass spectrometric detection and the chromatographic responses were grouped according to cluster analysis. Cluster scores were subjected to multivariate analyses of variance (MANOVA) to determine if fecal volatiles may hold a signal of immunization status. Multiple regression was then used to build models of the measured immune responses based on the metabolomic data. MANOVA results identified one cluster associated with protective status of skunks and one cluster associated with protective status of raccoons. Regression models demonstrated considerably greater success in predicting rabies antibody responses in both species. This is the first study to link volatile compounds with measures of adaptive immunity and provides further evidence that the volatile metabolome holds great promise for contributing to our understanding of disease and infections. The volatile metabolome may be an important resource for monitoring rabies immunization in raccoons and striped skunks.

Application of Volatile Organic Compound Analysis in a Nutritional Intervention Study: Differential Responses during Five Hours Following Consumption of a High- and a Low-Fat Dairy Drink

SCOPE

Exhaled volatile organic compounds (VOCs) are a possible relevant target for noninvasive assessment of metabolic responses. Using a breathomics approach, it is aimed to explore whether lipid intake influences VOC profiles in exhaled air, and to obtain insight in intra- and interindividual variations.

METHODS AND RESULTS

Three human interventions are performed. In the first, 12 males consume a high-fat drink on three study days. In the second, 12 males receive a high- and a low-fat drink on 6 days. In the third, three volunteers consume the high-fat drink again for tentative compound identification. Participants are asked to exhale, for 5 h postprandial with 15-20 min intervals, into a proton-transfer-reaction mass spectrometer, and VOCs in exhaled air are measured. Consumption of a drink alters the VOC profile, with considerable interindividual variation and quantitative intraindividual differences between days. Consumption of two different drinks results in a distinct VOC profile, caused by several specific m/z values. Most of these compounds are identified as being related to ketone body formation and lipid oxidation, showing an increase in high- versus low-fat drink.

CONCLUSION

Exhaled VOCs have the potential to assess differences in metabolic responses induced by nutrition, especially when day-to-day variation can be minimized.

An Optimistic Vision of Future: Diagnosis of Bacterial Infections by Sensing Their Associated Volatile Organic Compounds

Simple tests using sniff analysis that have the ability of diagnosing and rapidly distinguishing between infections due to different bacteria are urgently required by medical community worldwide. Professionals interested in this topic wish for these tests to be simultaneously cheap, fast, easily applicable, non-invasive, robust, reliable, and sensitive. Current analytical instrumentation has already the ability for performing real time (minutes or a few dozens of minutes) analysis of volatile bacterial biomarkers (the VOCs emitted by bacteria). Although many articles are available, a review displaying an objective evaluation of the current status in the field is still needed. This review tries to present an overview regarding the bacterial biomarkers released from in vitro cultivation of various bacterial strains and also from different biological matrices investigated, over the last 10 years. We have described results of relevant studies, which used modern analytical techniques to evaluate specific biomarker profiles associated with bacterial infections. Our purpose was to present a comprehensive view of available possibilities for detection of emitted bacterial VOCs from different matrices. We intend that this review to be of general interest for both medical doctors and for all researchers preoccupied with bacterial infectious diseases and their rapid diagnosis using analytical instrumentation.

Urinary metabolites for urological cancer detection: a review on the application of volatile organic compounds for cancers

Cancer is one of the most devastating human diseases that causes a great number of mortalities each year worldwide. Thus, finding and treating cancers early is of increasing interest to the public and presents great opportunity for research. It is well known that the metabolism of cancer cells differs from that of normal tissues. Analysis of volatile organic compounds (VOCs), a group of small molecule metabolites, provides an emerging approach for cancer screening and disease monitoring. VOCs are continuously generated in human body and released through breath, blood, skin, urine and fecal samples, which carry information of the physiological and metabolic status. Furthermore, the development of effective analytical methods for VOCs detection is one of the challenging aspects in cancer research. In this review, the analytical methods such as solid-phase mirco-extraction (SPME) and stir bar sorptive extraction (SBSE) coupled with gas chromatography/mass spectrometry (GC-MS), the application of VOCs in urological cancers diagnosis and potential molecules pathways related to VOCs profile for cancer detection are discussed.

Screening of Microbial Volatile Organic Compounds for Detection of Disease in Cattle: Development of Lab-scale Method

The primary hurdle for diagnosis of some diseases is the long incubation required to culture and confirm the presence of bacteria. The concept of using microbial VOCs as "signature markers" could provide a faster and noninvasive diagnosis. Finding biomarkers is challenging due to the specificity required in complex matrices. The objectives of this study were to (1) build/test a lab-scale platform for screening of microbial VOCs and (2) apply it to Mycobacterium avium paratuberculosis; the vaccine strain of M. bovis Bacillus Calmette-Guérin; and M. kansasii to demonstrate detection times greater those typically required for culture. SPME-GC-MS was used for sampling, sample preparation, and analyses. For objective (1), a testing platform was built for headspace sampling of bacterial cultures grown in standard culture flasks via a biosecure closed-loop circulating airflow system. For (2), results show that the suites of VOCs produced by Mycobacteria ssp. change over time and that individual strains produce different VOCs. The developed method was successful in discriminating between strains using a pooled multi-group analysis, and in timepoint-specific multi- and pair-wise comparisons. The developed testing platform can be useful for minimally invasive and biosecure collection of biomarkers associated with human, wildlife and livestock diseases for development of diagnostic point-of-care and field surveillance.

Cerumenogram: a new frontier in cancer diagnosis in humans

Cancer is the deadliest human disease and the development of new diagnosis methods is important to increase the chances of a cure. In this work it was developed a new method, named here for the first time as cerumenogram, using cerumen (earwax) as a new biomatrix for diagnosis. Earwax samples collected from cancer patients (cancer group) and cancer-free patients (control group) were analyzed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS), following with multivariate analysis steps to process the raw data generated. In total, 158 volatile organic metabolites (VOMs) were identified in the cerumen samples. The 27 selected as potential VOMs biomarkers for cancer provided 100% discrimination between the cancer and control groups. This new test can thus be routinely employed for cancer diagnoses that is non-invasive, fast, cheap, and highly accurate.

GC-MS application in determination of volatile profiles emitted by infected and uninfected human tissue

Volatile organic compounds (VOCs) released into the headspace air over human tissues infected with different bacteria were investigated in this work. The above-mentioned VOCs result both from bacterial metabolic processes (pathogen-specific signals) and from the matrix (tissue samples themselves). The objective of this study was to investigate whether one could reliably identify various microorganism strains that exist inside infected tissue samples by direct monitoring of the headspace atmosphere above their cultures. Headspace samples were directly interrogated using a GC-MS system, which produced distinct profiles for samples contaminated with single bacterial strains or with multiple strains (mixed infections). Principal component analysis (PCA) and predictive analysis based on receiver operating characteristics curves (ROC) were the statistical procedures utilized for differentiating between infected and uninfected samples, while network analysis and heat-mapping were used to highlight the connections between emitted volatiles and infectious pathogens. By using ROC curves, obtained results demonstrated that the area under the ROC (95% probability interval) was 0.86 in case of infected samples and 0.48 for uninfected samples. On the other hand, PCA highlighted separation between components coming from infected and uninfected patients, where 67% of variance was described from the first 2 principal components. The biomarker chemicals documented from this work, as well as the developed methodology may ultimately be applied to identify bacterial infections by analyzing exhaled breath.

Non-Invasive Detection of Anastomotic Leakage Following Esophageal and Pancreatic Surgery by Urinary Analysis

BACKGROUND

Esophagectomy or pancreaticoduodenectomy is the standard surgical approach for patients with tumors of the esophagus or pancreatic head. Postoperative mortality is strongly correlated with the occurrence of anastomotic leakage (AL). Delay in diagnosis leads to delay in treatment, which ratifies the need for development of novel and accurate non-invasive diagnostic tests for detection of AL. Urinary volatile organic compounds (VOCs) reflect the metabolic status of an individual, which is associated with a systemic immunological response. The aim of this study was to determine the diagnostic accuracy of urinary VOCs to detect AL after esophagectomy or pancreaticoduodenectomy.

METHODS

In the present study, urinary VOCs of 63 patients after esophagectomy (n = 31) or pancreaticoduodenectomy (n = 32) were analyzed by means of field asymmetric ion mobility spectrometry. AL was defined according to international study groups.

RESULTS

AL was observed in 15 patients (24%). Urinary VOCs of patients with AL after pancreaticoduodenectomy could be distinguished from uncomplicated controls, area under the curve 0.85 (95% CI 0.76-0.93), sensitivity 76%, and specificity 77%. However, this was not observed following esophagectomy, area under the curve 0.51 (95% CI 0.37-0.65).

CONCLUSION

In our study population AL following pancreaticoduodenectomy could be discriminated from uncomplicated controls by means of urinary VOC analysis, NTC03203434.

Breath metabolome of mice infected with Pseudomonas aeruginosa

INTRODUCTION

The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa.

OBJECTIVES

To determine a discriminatory core of molecules in the "breath-print" of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific "breath-print" signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20-25, 2014. https://doi.org/10.1111/2049-632X.12107 ).

METHODS

Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model.

RESULTS

Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups.

CONCLUSIONS

To the best of our knowledge, this is the first study to report on a 'core' murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

Gas Chromatography Mass Spectrometry (GC-MS) Quantification of Metabolites in Stool Using 13C Labelled Compounds

It has become increasingly important to qualitatively and quantitatively assess the volatile metabolites in a range of bodily fluids for use in monitoring health. There has been relatively little work on the quantitative analysis of compounds, particularly with respect to the effects of ethnicity or geographic location. A novel method for the quantification of compounds in stool using ¹³C labelled compounds as internal standards is presented. Using thermal desorption gas chromatography mass spectrometry, stool samples from 38 healthy volunteers were analysed. The ¹³C labelled compounds, acetone, ethyl butanoate, ethanoic acid, butanoic acid, 3-methylbutanoic acid, and indole, were added as internal standards. This process mimics the solubility characteristics of the compounds and thus the method was able to quantify the compounds within the solid stool. In total, 15 compounds were quantified: Dimethyl sulphide (26–25,626 ng/g), acetone (442–3006 ng/g), ethyl butanoate (39–2468 ng/g), ethyl 2-methylbutanoate (0.3–180 ng/g), dimethyl disulphide (35–1303 ng/g), 1-octen-3-one (12 ng/g), dimethyl trisulphide (10–410 ng/g), 1-octen-3-ol (0.4–58 ng/g), ethanoic acid (672–12,963 ng/g), butanoic acid (2493–11,553 ng/g), 3-methylbutanoic acid (64–8262 ng/g), pentanoic acid (88–21,886 ng/g), indole (290–5477 ng/g), and 3-methyl indole (37–3483 ng/g). Moreover, by altering the pH of the stool to pH 13 in conjunction with the addition of ¹³C trimethylamine, the method was successful in detecting and quantifying trimethylamine for the first time in stool samples (range 40–5312 ng/g). Statistical analysis revealed that samples from U.K. origin had five significantly different compounds (ethyl butanoate, 1-octen-3-ol, ethanoic acid, butanoic acid, pentanoic acid, and indole) from those of South American origin. However, there were no significant differences between vegetarian and omnivore samples. These findings are supported by pre-existing literature evidence. Moreover, we have tentatively identified 12 compounds previously not reported as having been found in stool.

Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers

Wearable devices are gaining considerable attention owing to the ease with which they can collect crucial information in real-time, both continuously and noninvasively, regarding a wearer's health. A concise summary is given of the three main elements that enable autonomous detection and monitoring of the likelihood or the existence of a health-risk state in continuous and real-time modes, with an emphasis on emerging materials and fabrication techniques in the relevant fields. The first element is the sensing technology used in the noninvasive detection of physiological markers relevant to the state of health. The second element is self-powered devices for longer periods of use by drawing energy from bodily movement and temperature. The third element is the self-healing properties of the materials used in the wearable devices to extended usage if they become scratched or cut. Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further improvement of skin-based wearable devices are also presented and discussed.

Fecal Volatile Organic Compounds in Preterm Infants Are Influenced by Enteral Feeding Composition

Fecal volatile organic compound (VOC) analysis has shown great potential as a noninvasive diagnostic biomarker for a variety of diseases. Before clinical implementation, the factors influencing the outcome of VOC analysis need to be assessed. Recent studies found that the sampling conditions can influence the outcome of VOC analysis. However, the dietary influences remains unknown, especially in (preterm) infants. Therefore, we assessed the effects of feeding composition on fecal VOC patterns of preterm infants (born at <30 weeks gestation). Two subgroups were defined: (1) daily intake >75% breastmilk (BM) feeding and (2) daily intake >75% formula milk (FM) feeding. Fecal samples, which were collected at 7, 14 and 21 days postnatally, were analyzed by an electronic nose device (Cyranose 320^®). In total, 30 preterm infants were included (15 FM, 15 BM). No differences in the fecal VOC patterns were observed at the three predefined time-points. Combining the fecal VOC profiles of these time-points resulted in a statistically significant difference between the two subgroups although this discriminative accuracy was only modest (AUC [95% CI]; p-value; sensitivity; and specificity of 0.64 [0.51–0.77]; 0.04; 68%; and 51%, respectively). Our results suggest that the influence of enteral feeding on the outcome of fecal VOC analysis cannot be ignored in this population. Furthermore, in both subgroups, the fecal VOC patterns showed a stable longitudinal course within the first month of life.

Detection of high-risk carbapenem-resistant Klebsiella pneumoniae and Enterobacter cloacae isolates using volatile molecular profiles

Infections caused by carbapenem-resistant Enterobacteriaceae (CRE) are alarming in the clinical setting, as CRE isolates often exhibit resistance to most clinically-available antibiotics. Klebsiella pneumoniae carbapenemase (KPC) is the most common carbapenemase carried by CRE in North America and Europe, frequently detected in isolates of K. pneumoniae, Escherichia coli, and Enterobacter cloacae. Notably, KPC-expressing strains often arise from clonal lineages, with sequence type 258 (ST258) representing the dominant lineage in K. pneumoniae, ST131 in E. coli, and ST78 and ST171 in E. cloacae. Prior studies have demonstrated that carbapenem-resistant K. pneumoniae differs from carbapenem-susceptible K. pneumoniae at both the transcriptomic and soluble metabolomic levels. In the present study, we sought to determine whether carbapenem-resistant and carbapenem-susceptible isolates of K. pneumoniae, E. coli, and E. cloacae produce distinct volatile metabolic profiles. We were able to identify a volatile metabolic fingerprint that could discriminate between CRE and non-CRE with an area under the receiver operating characteristic curve (AUROC) as high as 0.912. Species-specific AUROCs were as high as 0.988 for K. pneumoniae and 1.000 for E. cloacae. Paradoxically, curing of KPC-expressing plasmids from a subset of K. pneumoniae isolates further accentuated the metabolic differences observed between ST258 and non-ST258.

Progress in the development of olfactory-based bioelectronic chemosensors

Artificial chemosensory devices have a wide range of applications in industry, security, and medicine. The development of these devices has been inspired by the speed, sensitivity, and selectivity by which the olfactory system in animals can probe the chemical nature of the environment. In this review, we examine how molecular and cellular components of natural olfactory systems have been incorporated into artificial chemosensors, or bioelectronic sensors. We focus on the biological material that has been combined with signal transduction systems to develop artificial chemosensory devices. The strengths and limitations of different biological chemosensory material at the heart of these devices, as well as the reported overall effectiveness of the different bioelectronic sensor designs, is examined. This review also discusses future directions and challenges for continuing to advance development of bioelectronic sensors.

In vitro detection of common rhinosinusitis bacteria by the eNose utilising differential mobility spectrometry

Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients.

Volatile fingerprinting of Pseudomonas aeruginosa and respiratory syncytial virus infection in an in vitro cystic fibrosis co-infection model

Volatile molecules in exhaled breath represent potential biomarkers in the setting of infectious diseases, particularly those affecting the respiratory tract. In particular, Pseudomonas aeruginosa is a critically important respiratory pathogen in specific subsets of the population, such as those with cystic fibrosis (CF). Infections caused by P. aeruginosa can be particularly problematic when co-infection with respiratory syncytial virus (RSV) occurs, as this is correlated with the establishment of chronic P. aeruginosa infection. In the present study, we evaluate the volatile metabolites produced by P. aeruginosa (PAO1)-infected, RSV-infected, co-infected, or uninfected CF bronchial epithelial (CFBE) cells, in vitro. We identified a volatile metabolic signature that could discriminate between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE with an area under the receiver operating characteristic curve (AUROC) of 0.850, using the machine learning algorithm random forest (RF). Although we could not discriminate between RSV-infected and non-RSV-infected CFBE (AUROC = 0.431), we note that sample classification probabilities for RSV-infected cell, generated using RF, were between those of uninfected CFBE and P. aeruginosa-infected CFBE, suggesting that RSV infection may result in a volatile metabolic profile that shares attributes with both of these groups. To more precisely elucidate the biological origins of the volatile metabolites that were discriminatory between P. aeruginosa-infected and non-P. aeruginosa-infected CFBE, we measured the volatile metabolites produced by P. aeruginosa grown in the absence of CFBE. Our findings suggest that the discriminatory metabolites produced likely result from the interaction of P. aeruginosa with the CFBE cells, rather than the metabolism of media components by the bacterium. Taken together, our findings support the notion that P. aeruginosa interacting with CFBE yields a particular volatile metabolic signature. Such a signature may have clinical utility in the monitoring of individuals with CF.

Validation of biofilm formation on human skin wound models and demonstration of clinically translatable bacteria-specific volatile signatures

Biofilms are major contributors to delayed wound healing and there is a need for clinically relevant experimental models to assess theranostics. Microorganisms release volatile organic compounds (VOCs) and the ability to identify these in infected cutaneous wounds could lead to efficient non-invasive diagnosis. The aims here were to develop and assess bacterial biofilm formation and identify their VOC profiles in an in vitro model and validate in human ex vivo incisional and excisional cutaneous wound models. Biofilm development was assessed using multiple microscopy techniques with biofilm-forming deficient controls and quantified using metabolic and biomass assays; and VOC production measured by gas chromatography-mass spectrometry. The production of most VOCs was affected by biofilm development and model used. Some VOCs were specific either for planktonic or biofilm growth. The relative abundance of some VOCs was significantly increased or decreased by biofilm growth phase (P < 0.05). Some Staphylococcus aureus and Pseudomonas aeruginosa VOCs correlated with biofilm metabolic activity and biomass (R ≤ −0.5; ≥0.5). We present for the first time bacterial biofilm formation in human ex vivo cutaneous wound models and their specific VOC profiles. These models provide a vehicle for human skin-relevant biofilm studies and VOC detection has potential clinical translatability in efficient non-invasive diagnosis of wound infection.

Breath volatile organic compounds of lung transplant recipients with and without chronic lung allograft dysfunction

INTRODUCTION

Chronic lung allograft dysfunction with its clinical correlative of bronchiolitis obliterans syndrome (BOS) remains the major limiting factor for long-term graft survival. Currently there are no established methods for the early diagnosis or prediction of BOS. To assess the feasibility of breath collection as a non-invasive tool and the potential of breath volatile organic compounds (VOC) for the early detection of BOS, we compared the breath VOC composition between transplant patients without and different stages of BOS.

METHODS

75 outpatients (25 BOS stage 0, 25 BOS stage 1 + 2, 25 BOS stage 3) after bilateral lung transplantation were included. Exclusion criteria were active smoking, oxygen therapy and acute infection. Patients inhaled room air through a VOC and sterile filter and exhaled into an aluminum reservoir tube. Breath was loaded directly onto Tenax^® TA adsorption tubes and was subsequently analyzed by gas-chromatography/mass-spectrometry.

RESULTS

The three groups were age and gender matched, but differed with respect to time since transplantation, the spectrum of underlying disease, and treatment regimes. Relative to patients without BOS, BOS stage 3 patients showed a larger number of different VOCs, and more pronounced differences in the level of VOCs as compared to BOS stage 1 + 2 patients. Logistic regression analysis found no differences between controls and BOS 1 + 2, but four VOCs (heptane, isopropyl-myristate, ethyl-acetate, ionone) with a significant contribution to the discrimination between controls and BOS stage 3. A combination of these four VOCs separated these groups with an area under the curve of 0.87.

CONCLUSION

Breath sample collection using our reservoir sampler in the clinical environment was feasible. Our results suggest that breath VOCs can discriminate severe BOS. However, convincing evidence for VOCs with a potential to detect early onset BOS is lacking.

Volatile biomarkers of symptomatic and asymptomatic malaria infection in humans

Malaria elimination efforts are hindered by the prevalence of asymptomatic infections, which frequently go undetected and untreated. Consequently, there is a pressing need for improved diagnostic screening methods. Based on extensive collections of skin odors from human populations in Kenya, we report broad and consistent effects of malaria infection on human volatile emissions. Furthermore, we found that predictive models based on machine learning algorithms reliably determined infection status based on volatile biomarkers and, critically, identified asymptomatic infections with 100% sensitivity, even in the case of low-level infections not detectable by microscopy. These findings suggest that volatile biomarkers have significant potential for the development of robust, noninvasive screening methods for detecting symptomatic and asymptomatic malaria infections under field conditions.

Malaria remains among the world’s deadliest diseases, and control efforts depend critically on the availability of effective diagnostic tools, particularly for the identification of asymptomatic infections, which play a key role in disease persistence and may account for most instances of transmission but often evade detection by current screening methods. Research on humans and in animal models has shown that infection by malaria parasites elicits changes in host odors that influence vector attraction, suggesting that such changes might yield robust biomarkers of infection status. Here we present findings based on extensive collections of skin volatiles from human populations with high rates of malaria infection in Kenya. We report broad and consistent effects of malaria infection on human volatile profiles, as well as significant divergence in the effects of symptomatic and asymptomatic infections. Furthermore, predictive models based on machine learning algorithms reliably determined infection status based on volatile biomarkers. Critically, our models identified asymptomatic infections with 100% sensitivity, even in the case of low-level infections not detectable by microscopy, far exceeding the performance of currently available rapid diagnostic tests in this regard. We also identified a set of individual compounds that emerged as consistently important predictors of infection status. These findings suggest that volatile biomarkers may have significant potential for the development of a robust, noninvasive screening method for detecting malaria infections under field conditions.

Headspace sorptive extraction-gas chromatography-mass spectrometry method to measure volatile emissions from human airway cell cultures

The human respiratory tract releases volatile metabolites into exhaled breath that can be utilized for noninvasive health diagnostics. To understand the origin of this metabolic process, our group has previously analyzed the headspace above human epithelial cell cultures using solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). In the present work, we improve our model by employing sorbent-covered magnetic stir bars for headspace sorptive extraction (HSSE). Sorbent-coated stir bar analyte recovery increased by 52 times and captured 97 more compounds than SPME. Our data show that HSSE is preferred over liquid extraction via stir bar sorptive extraction (SBSE), which failed to distinguish volatiles unique to the cell samples compared against media controls. Two different cellular media were also compared, and we found that Opti-MEM® is preferred for volatile analysis. We optimized HSSE analytical parameters such as extraction time (24 h), desorption temperature (300 °C) and desorption time (7 min). Finally, we developed an internal standard for cell culture VOC studies by introducing 842 ng of deuterated decane per 5 mL of cell medium to account for error from extraction, desorption, chromatography and detection. This improved model will serve as a platform for future metabolic cell culture studies to examine changes in epithelial VOCs caused by perturbations such as viral or bacterial infections, opening opportunities for improved, noninvasive pulmonary diagnostics.

SPME-GC×GC-TOF MS fingerprint of virally-infected cell culture: Sample preparation optimization and data processing evaluation

Untargeted metabolomics study of volatile organic compounds produced by different cell cultures is a field that has gained increasing attention over the years. Solid-phase microextraction has been the sampling technique of choice for most of the applications mainly due to its simplicity to implement. However, a careful optimization of the analytical conditions is necessary to obtain the best performances, which are highly matrix-dependent. In this work, five different solid-phase microextraction fibers were compared for the analysis of the volatiles produced by cell culture infected with the human respiratory syncytial virus. A central composite design was applied to determine the best time-temperature combination to maximize the extraction efficiency and the salting-out effect was evaluated as well. The linearity of the optimized method, along with limits of detection and quantification and repeatability was assessed. Finally, the effect of i) different normalization techniques (i.e. z-score and probabilistic quotient normalization), ii) data transformation (i.e. in logarithmic scale), and iii) different feature selection algorithms (i.e. Fisher ratio and random forest) on the capability of discriminating between infected and not-infected cell culture was evaluated.

Volatile fingerprinting of human respiratory viruses from cell culture

Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.

Machine learning for the meta-analyses of microbial pathogens' volatile signatures

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

Breath-based diagnosis of fungal infections

Invasive aspergillosis and other invasive fungal infections are associated with significant morbidity and mortality in immunocompromised patients, in large part due to limitations of existing diagnostic methods for these infections. Detection of species-specific volatile sesquiterpene metabolites of fungal origin in the breath of patients with invasive fungal infections allows the diagnosis and monitoring of these infections in vivo, non-invasively and more rapidly than possible with current diagnostic methods. While detection of exogenous microbial volatile metabolites in the breath has opened up a new and exciting dimension of diagnostic research and development in infectious diseases, we discuss the daunting challenges to volatile diagnostic biomarker discovery and clinical development.

The potential role of exhaled breath analysis in the diagnostic process of pneumonia-a systematic review

Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned pre-clinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.

Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups

The identification of pathogen-specific volatile metabolic 'fingerprints' could lead to the rapid identification of disease-causing organisms either directly from ex vivo patient bio-specimens or from in vitro cultures. In the present study, we have evaluated the volatile metabolites produced by 100 clinical isolates belonging to ten distinct pathogen groups that, in aggregate, account for 90% of bloodstream infections, 90% of urinary tract infections, and 80% of infections encountered in the intensive care unit setting. Headspace volatile metabolites produced in vitro were concentrated using headspace solid-phase microextraction and analyzed via two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). A total of 811 volatile metabolites were detected across all samples, of which 203 were: (1) detected in 9 or 10 (of 10) isolates belonging to one or more pathogen groups, and (2) significantly more abundant in cultures relative to sterile media. Network analysis revealed a distinct metabolic fingerprint associated with each pathogen group, and analysis via Random Forest using leave-one-out cross-validation resulted in a 95% accuracy for the differentiation between groups. The present findings support the results of prior studies that have reported on the differential production of volatile metabolites across pathogenic bacteria and fungi, and provide additional insight through the inclusion of pathogen groups that have seldom been studied previously, including Acinetobacter spp., coagulase-negative Staphylococcus, and Proteus mirabilis, as well as the utilization of HS-SPME-GC×GC-TOFMS for improved sensitivity and resolution relative to traditional gas chromatography-based techniques.