Breath biomarkers of active pulmonary tuberculosis

Innovative electrochemical biosensors for tuberculosis detection

Tuberculosis (TB) continues to pose a significant global health threat, highlighting the urgent need for the development of rapid, precise, and accessible diagnostic tools to effectively manage its transmission. Conventional diagnostic techniques, such as sputum microscopy and culture-based assays, face several drawbacks, including lengthy processing times, limited sensitivity, and the requirement for specialized laboratory facilities. In this landscape, electrochemical biosensors have emerged as promising alternatives, offering improved sensitivity, specificity, and rapid detection capabilities. This review presents a thorough overview of recent advancements in the development and application of innovative electrochemical biosensors for TB detection. It explores the integration of nanomaterials such as graphene, gold nanoparticles, and carbon nanotubes, focusing on their contributions to enhanced sensor performance in terms of signal amplification and biorecognition efficacy. By synthesizing current research and technological developments, this review emphasizes the considerable potential of electrochemical biosensors to transform TB diagnostics, ultimately assisting in better disease management and control strategies worldwide.

The Mass Spectrometry Revolution in Clinical Microbiology Part 2: Emerging Applications

In part 2 of this series, we highlight some of the exciting and emerging applications of mass spectrometry (MS) in pathogen detection, identification, and characterization. First, we review applications that have been recently introduced into the clinical microbiology laboratory such as direct matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) from positive blood cultures as well as its incorporation into laboratory automation. We then explore how current MALDI-TOF platforms are being developed to determine antimicrobial resistance and strain typing. Finally, we showcase some emerging approaches such as MS imaging and non-MALDI-based MS techniques such as ambient ionization and breath-based pathogen detection.

New volatile organic compounds from the exhaled breath of active tuberculosis patients

Tuberculosis (TB) remains a leading global cause of mortality, necessitating the development of innovative diagnostic approaches for early and accurate detection. Existing diagnostic methods are invasive, time-intensive, and contribute to delays in diagnosis, thereby exacerbating disease progression in patients and facilitating community transmission. To address these limitations, this study investigated human breath samples to identify volatile organic compounds (VOCs) associated with active TB as potential non-invasive biomarkers. VOCs were analysed using gas chromatography-mass spectrometry (GC-MS), supported by AMDIS and OpenChrom software for compound identification. The findings revealed the presence of previously unreported VOCs associated to breath samples from patients with active TB and multidrug-resistant TB (MDR-TB), which were absent in control participants without TB symptoms. Furthermore, the results suggest the feasibility of differentiating MDR-TB from active TB based on breath VOC profiles, marking a novel observation. These findings underpin the development of a non-invasive breathalyser diagnostic device for TB, with the potential to enhance disease management and reduce transmission.

Residential Proximity Land Use Characteristics and Exhaled Volatile Organic Compounds' Impact on Pulmonary Function in Asthmatic Children

BACKGROUND

Urban air pollution adversely affects children's respiratory systems, but the impact of volatile organic compounds (VOCs) on children's pulmonary function remains unclear. This study aims to identify exhaled VOCs linked to land use characteristics and reduced pulmonary function in asthmatic children, as well as to explore environmental thresholds influencing VOC exposure levels.

METHODS

We enrolled 97 asthmatic children, aged 7 to 20, from Changhua County, Taiwan, and collected personal and residential data, collected exhaled VOC samples, and conducted pulmonary function tests. Land use characteristics were derived from the children's residential addresses. This study used two models to explore the relationships between land use, VOC levels, and pulmonary function.

RESULTS

Our results show that m/p-xylene, 1,3,5-trimethylbenzene, and 1,2,4-trimethylbenzene were key contributors to FEV1/FVC and significantly predicted FEV1/FVC < 90% (AUC = 0.66; 95% CI: 0.53 to 0.79). These VOCs were also linked to major road areas within a 300 m buffer around children's homes.

CONCLUSIONS

This study fills a research gap on low-level outdoor VOC exposure and pediatric respiratory health, examining 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, and m/p-xylene as potential biomarkers for impaired pulmonary function in children.

Infection Diagnostics Enabled by Selective Adsorption of Breath-Based Biomarkers in Zr-Based Metal-Organic Frameworks

Exhaled breath contains trace levels of volatile organic compounds (VOCs) that can reveal information about metabolic processes or pathogens in the body. These molecules can be used for medical diagnosis, but capturing and accurately measuring them is a significant challenge in chemical separations. A highly selective nanoporous sorbent can be used to capture target molecules from a breath sample and preconcentrate them for use in a detector. In this work, we present a combined predictive modeling-experimental validation study in which five Zr-based metal-organic frameworks (MOFs) were identified and tested. These MOFs display good selectivity for a variety of VOCs known to be indicators of viral infections such as influenza and COVID-19. We first used molecular simulation to identify promising MOF candidates that were subsequently synthesized and tested for recovery of a variety of VOCs (toluene, propanal, butanone, octane, acetaldehyde) at concentrations of 20 ppm in humid nitrogen. We show that MOF-818, PCN-777, and UiO-66 have particularly good selectivity for the target molecules in the presence of humidity. These three MOFs each recover around 40-60% of the targets (with the exception of acetaldehyde) at up to 95% relative humidity. MOF-818 recovers 63% of butanone and 60% of toluene at 80% relative humidity. Recovery for acetaldehyde is lower across all MOFs at high humidity, but notably, MOF-808 recovers 90% of acetaldehyde at 60% humidity.

Diagnostic Accuracy of Breath Tests to Detect Pulmonary Tuberculosis: A Systematic Review

PURPOSE

Tuberculosis (TB) is a highly contagious infection and one of the world's leading causes of death from a single infectious agent. Currently, TB diagnosis can be established via mycobacterial cultures, Acid Fast Bacilli smear and molecular studies. In the ever-evolving landscape of medical advancements, breath tests have shown considerable promise. This systematic review aimed to evaluate the diagnostic accuracy of breath tests to detect pulmonary TB in various populations.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. We searched Embase and PubMed to identify observational studies published from database inception to May 2024. All observational studies evaluating the diagnostic accuracy of breath tests to detect pulmonary tuberculosis were included. Authors independently reviewed each article for eligibility and risk-of-bias. A senior reviewer was consulted for discrepancies.

RESULTS

The pooled sensitivity for the breath test in diagnosing TB was 0.85 (95% CI 0.78-0.90) whilst the pooled specificity was 0.83 (95% CI 0.72-0.90), although heterogeneity was high. Sub-group analysis by low/lower-middle World Bank income group status, high proportion of TB in test population, or use of a separate breath sampling kit did not reduce the heterogeneity. Publication bias was absent.

CONCLUSION

Our study found that pooled sensitivity and specificity of the breath tests in diagnosing pulmonary TB was high. Future research efforts can be directed towards investigating the diagnostic accuracy of electronic noses and gas chromatography combined with mass spectrometry, whilst improving standardisation and reproducibility of breath test techniques.

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

Exhaled breath analysis: A promising triage test for tuberculosis in young children

The diagnosis of paediatric pulmonary tuberculosis is difficult, especially in young infants who cannot expectorate sputum spontaneously. Breath testing has shown promise in diagnosing respiratory tract infections, but data on paediatric tuberculosis are limited. We performed a prospective cross-sectional study in Kenya in children younger than five years with symptoms of tuberculosis. We analysed exhaled breath with a hand-held battery-powered nose device. For data analysis, machine learning was applied using samples classified as positive (microbiologically confirmed) or negative (unlikely tuberculosis) to assess diagnostic accuracy. Breath analysis was performed in 118 children. The area under the curve of the optimal model was 0.73. At a sensitivity of 86 % (CI 62-96 %), this resulted in a specificity of 42 % (95 % CI 30-55 %). Exhaled breath analysis shows promise as a triage test for TB in young children, although the WHO target product characteristics were not met.

Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD)

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.

Volatile organic compound detection of Buruli ulcer disease: Headspace analysis of Mycobacterium ulcerans and used gauzes of Buruli-compatible ulcers

Diagnosing Buruli ulcer (BU) is complicated by limited access to the sensitive IS2404 qPCR. Experienced clinicians report a distinct odour of Buruli ulcers. We explored the potential of headspace analysis by thermal desorption-gas chromatography-mass spectrometry to detect volatile organic compounds (VOCs) from Mycobacterium ulcerans both in vitro and clinically. This study was conducted in two phases: a discovery and validation phase. During the discovery phase, VOCs that enable identification of M. ulcerans cultures were determined. During the validation phase, these VOCs were evaluated in clinical samples for which we used gauzes from patients with skin ulcerations in the Democratic Republic of Congo. Seven M. ulcerans headspace samples were compared with four from sterile growth medium and laboratory environmental air. The univariate analysis resulted in the selection of 24 retained VOC fragments and a perfect differentiation between cultures and controls. Sixteen of 24 fragments were identified, resulting in eleven unique compounds, mainly alkanes. Methylcyclohexane was the best performing compound. Based on these 24 fragments, headspace samples originating from gauzes of 50 open skin lesions (12 qPCR positive and 38 negative) were analysed and an AUC of 0.740 (95%-CI 0.583-0.897) was obtained. As this is an experimental study, future research has to confirm whether the identified compounds can serve as novel biomarkers.

Breath biomarkers in Non-Carcinogenic diseases

The analysis of volatile organic compounds (VOCs) from human matrices like breath, perspiration, and urine has received increasing attention from academic and medical researchers worldwide. These biological-borne VOCs molecules have characteristics that can be directly related to physiologic and pathophysiologic metabolic processes. In this work, gathers a total of 292 analytes that have been identified as potential biomarkers for the diagnosis of various non-carcinogenic diseases. Herein we review the advances in VOCs with a focus on breath biomarkers and their potential role as minimally invasive tools to improve diagnosis prognosis and therapeutic monitoring.

Detection of tuberculosis-associated compounds from human skin by GCxGC-TOFMS

Tuberculosis (TB) remains a global health concern. This study aimed to investigate the potential of human skin volatile organic compounds (VOCs) as prospective biomarkers for TB diagnosis. It employed a non-invasive approach using a wearable silicone rubber band for VOC sampling, comprehensive gas chromatography - time of flight mass spectrometry (GCxGC-TOFMS), and chemometric techniques. Both targeted and untargeted biochemical screening was utilized to explore biochemical differences between healthy individuals and those with TB infection. Results confirmed a correlation between compounds found in this study, and those reported for TB from other biofluids. In a comparison to known TB-associated compounds from other biofluids our analysis established the presence of 27 of these compounds emanating from human skin. Additionally, 16 previously unreported compounds were found as potential biomarkers. The diagnostic ability of the VOCs selected by statistical methods was investigated using predictive modelling techniques. Artificial neural network multi-layered perceptron (ANN) yielded two compounds, 1H-indene, 2,3 dihydro-1,1,3-trimethyl-3-phenyl; and heptane-3-ethyl-2-methyl, as the most discriminatory, and could differentiate between TB-positive (n = 15) and TB-negative (n = 23) individuals with an area under the receiver operating characteristic curve (AUROC) of 92 %, a sensitivity of 100 % and a specificity of 94 % for six targeted features. For untargeted analysis, ANN assigned 3-methylhexane as the most discriminatory between TB-positive and TB- negative individuals. An AUROC of 98.5 %, a sensitivity of 83 %, and a specificity of 88 % were obtained for 16 untargeted features as chosen by high performance variable selection. The obtained values compare highly favourable to alternative diagnostic methods such as breath analysis and GeneXpert. Consequently, human skin VOCs hold considerable potential as a TB diagnostic screening test.

The State of the Art on Graphene-Based Sensors for Human Health Monitoring through Breath Biomarkers

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed.

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH2O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

Volatile Organic Compound Identification-Based Tuberculosis Screening among TB Suspects: A Diagnostic Accuracy Study

Tuberculosis (TB) affects a third of the global population, and a large population of infected individuals still remain undiagnosed-making the visible burden only the tip of the iceberg. The detection of tuberculosis in close-proximity patients is one of the key priorities for attaining the Sustainable Development Goals (SDG) of TB elimination by 2030. With the current battery of screening tests failing to cover this need, the authors of this paper examined a simple and inexpensive point-of-care breath analyzer (TSI-3000(I)), which is based on detecting the volatile organic compounds that are emitted from infected cells and released in exhaled breath as a screening tool for the detection of TB. A single-center pilot study for assessing the diagnostic accuracy of the point-of-care Tuberculosis Breath Analyzer was conducted, and it was compared against the WHO-recommended TrueNat assay, which is a rapid molecular test and was also treated as the reference standard in this study. Of the 334 enrolled participants with TB signs/symptoms, 42.51% were TrueNat positive for Mycobacterium tuberculosis. The sensitivity of the Tuberculosis Breath Analyzer was found to be 95.7%, with a specificity of 91.3% and a ROC area of 0.935. The test kit showed considerable/significant high sensitivity and specificity as reliability indicators. The performance of the Tuberculosis Breath Analyzer tested was found to be comparable in efficiency to that of the TrueNat assay. A large cohort-based multicentric study is feasibly required to further validate and extrapolate the results of the pilot study.

On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.

GC-MS analysis of exhaled gas for fine detection of inflammatory diseases

Exhaled gas analysis is a non-invasive test ideal for continuous monitoring of biological metabolic information. We analyzed the exhaled gas of patients with inflammatory diseases for trace gas components that could serve as biomarkers that enable early detection of inflammatory diseases and assessment of treatment efficacy. Furthermore, we examined the clinical potential of this method. We enrolled 34 patients with inflammatory disease and 69 healthy participants. Volatile components from exhaled gas were collected and analyzed by a gas chromatography-mass spectrometry system, and the data were examined for gender, age, inflammatory markers, and changes in markers before and after treatment. The data were tested for statistical significance through discriminant analysis by Volcano plot, Analysis of variance test, principal component analysis, and cluster analysis comparing healthy and patient groups. There were no significant differences in the trace components of exhaled gas by gender or age. However, we found differences in some components of the exhaled gas between healthy and untreated patients. In addition, after treatment, gas patterns including the patient-specific components changed to a state closer to the inflammation-free status. We identified trace components in the exhaled gas of patients with inflammatory diseases and found that some of these regressed after treatment.

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

BACKGROUND

Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.

METHOD

Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.

RESULTS

The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.

CONCLUSIONS

The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Manipulation by Plasmodium Parasites of Anopheles Mosquito Behavior and Human Odors

PURPOSE

The phenomenon of parasites manipulating host phenotypes is well documented; the best-known examples are manipulations of host behavior. More recently, there has been interest in whether parasites can manipulate host odor phenotypes to enhance their attractiveness to vectors. We review here evidence that Plasmodium-infected mosquitoes have enhanced attraction to human hosts, especially when the parasite is sufficiently developed to be transmissible. We also review evidence suggesting that malaria-infected host odors elicit greater mosquito attraction compared to uninfected controls.

METHODS

We reviewed and summarized the relevant literature.

RESULTS

Though evidence is mounting that supports both premises we reviewed, there are several confounds that complicate interpretation. These include differences in Plasmodium and mosquito species studied, stage of infection tested, age of human participants in trials, and methods used to quantify volatiles. In addition, a key requirement to support the hypothesis of manipulation by parasites is that costs of manipulation be identified, and ideally, quantified.

CONCLUSIONS

Substantial progress has been made to unlock the importance of odor for enhancing transmission of Plasmodium. However, there needs to be more replication using similar methods to better define the odor parameters involved in this enhancement.

Diagnosing Tuberculosis: What Do New Technologies Allow Us to (Not) Do?

New tuberculosis (TB) diagnostics are at a crossroads: their development, evaluation, and implementation is severely damaged by resource diversion due to COVID-19. Yet several technologies, especially those with potential for non-invasive non-sputum-based testing, hold promise for efficiently triaging and rapidly confirming TB near point-of-care. Such tests are, however, progressing through the pipeline slowly and will take years to reach patients and health workers. Compellingly, such tests will create new opportunities for difficult-to-diagnose populations, including primary care attendees (all-comers in high burden settings irrespective of reason for presentation) and community members (with early stage disease or risk factors like HIV), many of whom cannot easily produce sputum. Critically, all upcoming technologies have limitations that implementers and health workers need to be cognizant of to ensure optimal deployment without undermining confidence in a technology that still offers improvements over the status quo. In this state-of-the-art review, we critically appraise such technologies for active pulmonary TB diagnosis. We highlight strengths, limitations, outstanding research questions, and how current and future tests could be used in the presence of these limitations and uncertainties. Among triage tests, CRP (for which commercial near point-of-care devices exist) and computer-aided detection software with digital chest X-ray hold promise, together with late-stage blood-based assays that detect host and/or microbial biomarkers; however, aside from a handful of prototypes, the latter category has a shortage of promising late-stage alternatives. Furthermore, positive results from new triage tests may have utility in people without TB; however, their utility for informing diagnostic pathways for other diseases is under-researched (most sick people tested for TB do not have TB). For confirmatory tests, few true point-of-care options will be available soon; however, combining novel approaches like tongue swabs with established tests like Ultra have short-term promise but first require optimizations to specimen collection and processing procedures. Concerningly, no technologies yet have compelling evidence of meeting the World Health Organization optimal target product profile performance criteria, especially for important operational criteria crucial for field deployment. This is alarming as the target product profile criteria are themselves almost a decade old and require urgent revision, especially to cater for technologies made prominent by the COVID-19 diagnostic response (e.g., at-home testing and connectivity solutions). Throughout the review, we underscore the importance of how target populations and settings affect test performance and how the criteria by which these tests should be judged vary by use case, including in active case finding. Lastly, we advocate for health workers and researchers to themselves be vocal proponents of the uptake of both new tests and those - already available tests that remain suboptimally utilized.

Breath biomarkers associated withnontuberculosis mycobacteriadisease status in persons with cystic fibrosis: a pilot study

Pulmonary infections caused by mycobacteria cause significant mortality and morbidity in the human population. Diagnosing mycobacterial infections is challenging. An infection can lead to active disease or remain indolent with little clinical consequence. In patients with pulmonarynontuberculosis mycobacteria(PNTM) identification of infection and diagnosis of disease can take months to years. Our previous studies showed the potential diagnostic power of volatile molecules in the exhaled breath samples to detect active pulmonaryM. tuberculosisinfection. Herein, we demonstrate the ability to detect the disease status of PNTM in the breath of persons with cystic fibrosis (PwCF). We putatively identified 17 volatile molecules that could discriminate between active-NTM disease (n= 6), indolent patients (n= 3), and those patients who have never cultured an NTM (n= 2). The results suggest that further confirmation of the breath biomarkers as a non-invasive and culture-independent tool for diagnosis of NTM disease in a larger cohort of PwCF is warranted.

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients

Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.

Biodiagnostics in an era of global pandemics-From biosensing materials to data management

The novel corona virus SARS-CoV-2 (COVID-19) has exposed the world to challenges never before seen in fast diagnostics, monitoring, and prevention of the outbreak. As a result, different approaches for fast diagnostic and screening are made and yet to find the ideal way. The current mini-review provides and examines evidence-based innovative and rapid chemical sensing and related biodiagnostic solutions to deal with infectious disease and related pandemic emergencies, which could offer the best possible care for the general population and improve the approachability of the pandemic information, insights, and surrounding contexts. The review discusses how integration of sensing devices with big data analysis, artificial Intelligence or machine learning, and clinical decision support system, could improve the accuracy of the recorded patterns of the disease conditions within an ocean of information. At the end, the mini-review provides a prospective on the requirements to improve our coping of the pandemic-related biodiagnostics as well as future opportunities.

Breath Metabolites to Diagnose Infection

BACKGROUND

Starkly highlighted by the current COVID-19 pandemic, infectious diseases continue to have an outsized impact on human health worldwide. Diagnostic testing for infection can be challenging due to resource limitations, time constraints, or shortcomings in the accuracy of existing diagnostics. Rapid, simple diagnostics are highly desirable. There is increasing interest in the development of diagnostics that use exhaled breath analysis as a convenient and safe diagnostic method, as breath sampling is noninvasive, secure, and easy to perform. Volatile organic compounds (VOCs) present in exhaled breath reflect the fingerprint of the underlying metabolic and biophysical processes during disease.

CONTENT

In this review, we overview the major biomarkers present in exhaled breath in infectious diseases. We outline the promising recent advances in breath-based diagnosis of respiratory infections, including those caused by influenza virus, SARS-CoV-2, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Aspergillus fumigatus. In addition, we review the current landscape of diagnosis of 2 other globally important infections: Helicobacter pylori gastrointestinal infection and malaria.

SUMMARY

Characteristic and reproducible breath VOCs are associated with several infectious diseases, suggesting breath analysis as a promising strategy for diagnostic development. Ongoing challenges include poor standardization of breath collection and analysis and lack of validation studies. Further research is required to expand the applicability of breath analysis to clinical settings.

Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis

Purpose

To investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.

Methods

Three groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.

Results

Thirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92–1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.

Conclusion

The newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.

Breath biomarkers of total body irradiation in non-human primates

BACKGROUND

Radiation exposure causes oxidative stress, eliciting production of metabolites that are exhaled in the breath as volatile organic compounds (VOCs). We evaluated breath VOCs as potential biomarkers for use in radiation biodosimetry.

METHODS

Five anesthetized non-human primates receive total body irradiation (TBI) of three daily fractions of 120 cGy per day for three days, resulting in a cumulative dose of 10.8 Gy. Breath samples were collected prior to irradiation and after each radiation fraction, and analyzed with gas chromatography mass spectrometry.

RESULTS

TBI elicited a prompt and statistically significant increase in the abundance of several hundred VOCs in the breath, including some that were increased more than five-fold, with100% sensitivity and 100% specificity for radiation exposure. The most significant breath VOC biomarkers of radiation mainly comprised straight-chain n-alkanes (e.g. hexane), as well as methylated alkanes (e.g. 3-methyl-pentane) and alkane derivatives (e.g. 2-butyl-1-octanol), consistent with metabolic products of oxidative stress. An unidentified breath VOC biomarker increased more than ten-fold following TBI, and rose linearly with the total cumulative dose of radiation (R2=0.92).

CONCLUSIONS

TBI of non-human primates elicited increased production of breath VOCs consistent with increased oxidative stress. These findings provide a rational basis for further evaluation of breath VOC biomarkers in human radiation biodosimetry.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

As an ancient infectious disease, tuberculosis (TB) is still the leading cause of death from a single infectious agent worldwide. Latent TB infection (LTBI) has been recognized as the largest source of new TB cases and is one of the biggest obstacles to achieving the aim of the End TB Strategy. The latest data indicate that a considerable percentage of the population with LTBI and the lack of differential diagnosis between LTBI and active TB (aTB) may be potential reasons for the high TB morbidity and mortality in countries with high TB burdens. The tuberculin skin test (TST) has been used to diagnose TB for > 100 years, but it fails to distinguish patients with LTBI from those with aTB and people who have received Bacillus Calmette–Guérin vaccination. To overcome the limitations of TST, several new skin tests and interferon-gamma release assays have been developed, such as the Diaskintest, C-Tb skin test, EC-Test, and T-cell spot of the TB assay, QuantiFERON-TB Gold In-Tube, QuantiFERON-TB Gold-Plus, LIAISON QuantiFERON-TB Gold Plus test, and LIOFeron TB/LTBI. However, these methods cannot distinguish LTBI from aTB. To investigate the reasons why all these methods cannot distinguish LTBI from aTB, we have explained the concept and definition of LTBI and expounded on the immunological mechanism of LTBI in this review. In addition, we have outlined the research status, future directions, and challenges of LTBI differential diagnosis, including novel biomarkers derived from Mycobacterium tuberculosis and hosts, new models and algorithms, omics technologies, and microbiota.

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.

Next-Generation Digital Biomarkers for Tuberculosis and Antibiotic Stewardship: Perspective on Novel Molecular Digital Biomarkers in Sweat, Saliva, and Exhaled Breath

The internet of health care things enables a remote connection between health care professionals and patients wearing smart biosensors. Wearable smart devices are potentially affordable, sensitive, specific, user-friendly, rapid, robust, lab-independent, and deliverable to the end user for point-of-care testing. The datasets derived from these devices are known as digital biomarkers. They represent a novel patient-centered approach to collecting longitudinal, context-derived health insights. Adding automated, analytical smartphone applications will enable their use in high-, middle-, and low-income countries. So far, digital biomarkers have been focused primarily on accelerometer data and heart rate due to well-established sensors originating from the consumer market. Novel emerging smart biosensors will detect biomarkers (or compounds) independent of a lab and noninvasively in sweat, saliva, and exhaled breath. These molecular digital biomarkers are a promising novel approach to reduce the burden from 2 major infectious diseases with urgent unmet needs: tuberculosis and infections with multidrug resistant pathogens. Active tuberculosis (aTbc) is one of the deadliest diseases from an infectious agent. However, a simple and reliable test for its detection is still missing. Furthermore, inappropriate antimicrobial use leads to the development of antimicrobial resistance, which is associated with high mortality and health care costs. From this perspective, we discuss the innovative approach of a noninvasive and lab-independent collection of novel biomarkers to detect aTbc, which at the same time may additionally serve as a scalable therapeutic drug monitoring approach for antibiotics. These molecular digital biomarkers are next-generation digital biomarkers and have the potential to shape the future of infectious diseases.

Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test.

Aptamer-Based Diagnostic Systems for the Rapid Screening of TB at the Point-of-Care

The transmission of Tuberculosis (TB) is very rapid and the burden it places on health care systems is felt globally. The effective management and prevention of this disease requires that it is detected early. Current TB diagnostic approaches, such as the culture, sputum smear, skin tuberculin, and molecular tests are time-consuming, and some are unaffordable for low-income countries. Rapid tests for disease biomarker detection are mostly based on immunological assays that use antibodies which are costly to produce, have low sensitivity and stability. Aptamers can replace antibodies in these diagnostic tests for the development of new rapid tests that are more cost effective; more stable at high temperatures and therefore have a better shelf life; do not have batch-to-batch variations, and thus more consistently bind to a specific target with similar or higher specificity and selectivity and are therefore more reliable. Advancements in TB research, in particular the application of proteomics to identify TB specific biomarkers, led to the identification of a number of biomarker proteins, that can be used to develop aptamer-based diagnostic assays able to screen individuals at the point-of-care (POC) more efficiently in resource-limited settings.

Development and validation of a point-of-care breath test for octane detection

BACKGROUND

There is a demand for a non-invasive bedside method to diagnose Acute Respiratory Distress Syndrome (ARDS). Octane was discovered and validated as the most important breath biomarker for diagnosis of ARDS using gas-chromatography and mass-spectrometry (GC-MS). However, GC-MS is unsuitable as a point-of-care (POC) test in the intensive care unit (ICU). Therefore, we determined if a newly developed POC breath test can reliably detect octane in exhaled breath of invasively ventilated ICU patients.

METHODS

Two developmental steps were taken to design a POC breath test that relies on gas-chromatography using air as carrier gas with a photoionization detector. Calibration measurements were performed with a laboratory prototype in healthy subjects. Subsequently, invasively ventilated patients were included for validation and assessment of repeatability. After evolving to a POC breath test, this device was validated in a second group of invasively ventilated patients. Octane concentration was based on the area under the curve, which was extracted from the chromatogram and compared to known values from calibration measurements.

RESULTS

Five healthy subjects and 53 invasively ventilated patients were included. Calibration showed a linear relation (R2 = 1.0) between the octane concentration and the quantified octane peak in the low parts per billion (ppb) range. For the POC breath test the repeatability was excellent (R2 = 0.98, ICC = 0.97 (95% CI 0.94-0.99)).

CONCLUSION

This is the first study to show that a POC breath test can rapidly and reliably detect octane, with excellent repeatability, at clinically relevant levels of low ppb in exhaled breath of ventilated ICU patients. This opens possibilities for targeted exhaled breath analysis to be used as a bedside test and makes it a potential diagnostic tool for the early detection of ARDS.

Breath-Based Diagnosis of Infectious Diseases: A Review of the Current Landscape

Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.

Application of Volatilome Analysis to the Diagnosis of Mycobacteria Infection in Livestock

Volatile organic compounds (VOCs) are small molecular mass metabolites which compose the volatilome, whose analysis has been widely employed in different areas. This innovative approach has emerged in research as a diagnostic alternative to different diseases in human and veterinary medicine, which still present constraints regarding analytical and diagnostic sensitivity. Such is the case of the infection by mycobacteria responsible for tuberculosis and paratuberculosis in livestock. Although eradication and control programs have been partly managed with success in many countries worldwide, the often low sensitivity of the current diagnostic techniques against Mycobacterium bovis (as well as other mycobacteria from Mycobacterium tuberculosis complex) and Mycobacterium avium subsp. paratuberculosis together with other hurdles such as low mycobacteria loads in samples, a tedious process of microbiological culture, inhibition by many variables, or intermittent shedding of the mycobacteria highlight the importance of evaluating new techniques that open different options and complement the diagnostic paradigm. In this sense, volatilome analysis stands as a potential option because it fulfills part of the mycobacterial diagnosis requirements. The aim of the present review is to compile the information related to the diagnosis of tuberculosis and paratuberculosis in livestock through the analysis of VOCs by using different biological matrices. The analytical techniques used for the evaluation of VOCs are discussed focusing on the advantages and drawbacks offered compared with the routine diagnostic tools. In addition, the differences described in the literature among in vivo and in vitro assays, natural and experimental infections, and the use of specific VOCs (targeted analysis) and complete VOC pattern (non-targeted analysis) are highlighted. This review emphasizes how this methodology could be useful in the problematic diagnosis of tuberculosis and paratuberculosis in livestock and poses challenges to be addressed in future research.

Breath can discriminate tuberculosis from other lower respiratory illness in children

Pediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.

Human tuberculosis and Mycobacterium tuberculosis complex: A review on genetic diversity, pathogenesis and omics approaches in host biomarkers discovery

Mycobacterium tuberculosis complex (MTBC) refers to a group of mycobacteria encompassing nine members of closely related species that causes tuberculosis in animals and humans. Among the nine members, Mycobacterium tuberculosis (M. tuberculosis) remains the main causative agent for human tuberculosis that results in high mortality and morbidity globally. In general, MTBC species are low in diversity but exhibit distinctive biological differences and phenotypes among different MTBC lineages. MTBC species are likely to have evolved from a common ancestor through insertions/deletions processes resulting in species speciation with different degrees of pathogenicity. The pathogenesis of human tuberculosis is complex and remains poorly understood. It involves multi-interactions or evolutionary co-options between host factors and bacterial determinants for survival of the MTBC. Granuloma formation as a protection or survival mechanism in hosts by MTBC remains controversial. Additionally, MTBC species are capable of modulating host immune response and have adopted several mechanisms to evade from host immune attack in order to survive in humans. On the other hand, current diagnostic tools for human tuberculosis are inadequate and have several shortcomings. Numerous studies have suggested the potential of host biomarkers in early diagnosis of tuberculosis, in disease differentiation and in treatment monitoring. "Multi-omics" approaches provide holistic views to dissect the association of MTBC species with humans and offer great advantages in host biomarkers discovery. Thus, in this review, we seek to understand how the genetic variations in MTBC lead to species speciation with different pathogenicity. Furthermore, we also discuss how the host and bacterial players contribute to the pathogenesis of human tuberculosis. Lastly, we provide an overview of the journey of "omics" approaches in host biomarkers discovery in human tuberculosis and provide some interesting insights on the challenges and directions of "omics" approaches in host biomarkers innovation and clinical implementation.

Rats sniff out pulmonary tuberculosis from sputum: a diagnostic accuracy meta-analysis

In Sub-Saharan Africa, African giant pouched rats (Cricetomys gambianus) are trained to identify TB patients by smelling sputum. We conducted a systematic review and meta-analysis of the data to see if this novel method is comparable to traditional laboratory screening and detection methods like Ziehl-Neelsen stain-based assays (ZN) and bacterial culture. The search and data processing strategy is registered at PROSPERO (CRD42019123629). Medline via PubMed, EMBASE, Web of Science, and Cochrane Library databases were systematically searched for the keywords "pouched rat" and "tuberculosis". Data from 53,181 samples obtained from 24,600 patients were extracted from seven studies. Using sample-wise detection, the sensitivity of the studies was 86.7% [95% CI 80.4-91.2%], while the specificity was 88.4% [95% CI 79.7-93.7%]. For patient-wise detection, the sensitivity was 81.3% [95% CI 64.0-91.4%], while the specificity was 73.4% [95% CI 62.8-81.9%]. Good and excellent classification was assessed by hierarchical summary receiver-operating characteristic analysis for patient-wise and sample-wise detections, respectively. Our study is the first systematic review and meta-analysis of the above relatively inexpensive and rapid screening method. The results indicate that African giant pouched rats can discriminate healthy controls from TB individuals by sniffing sputum with even a higher accuracy than a single ZN screening.

A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis

Background

Due to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients.

Methods

Firstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database.

Results

Based on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers.

Conclusions

Overall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12890-020-01316-2.

Evaluation of Volatile Organic Compounds Obtained from Breath and Feces to Detect Mycobacterium tuberculosis Complex in Wild Boar (Sus scrofa) in Doñana National Park, Spain

The presence of Mycobacterium tuberculosis complex (MTBC) in wild swine, such as in wild boar (Sus scrofa) in Eurasia, is cause for serious concern. Development of accurate, efficient, and noninvasive methods to detect MTBC in wild swine would be highly beneficial to surveillance and disease management efforts in affected populations. Here, we describe the first report of identification of volatile organic compounds (VOC) obtained from the breath and feces of wild boar to distinguish between MTBC-positive and MTBC-negative boar. We analyzed breath and fecal VOC collected from 15 MTBC-positive and 18 MTBC-negative wild boar in Donaña National Park in Southeast Spain. Analyses were divided into three age classes, namely, adults (>2 years), sub-adults (12–24 months), and juveniles (<12 months). We identified significant compounds by applying the two-tailed statistical t-test for two samples assuming unequal variance, with an α value of 0.05. One statistically significant VOC was identified in breath samples from adult wild boar and 14 were identified in breath samples from juvenile wild boar. One statistically significant VOC was identified in fecal samples collected from sub-adult wild boar and three were identified in fecal samples from juvenile wild boar. In addition, discriminant function analysis (DFA) was used to build classification models for MTBC prediction in juvenile animals. Using DFA, we were able to distinguish between MTBC-positive juvenile wild boar and MTBC-negative juvenile wild boar using breath VOC or fecal VOC. Based on our results, further research is warranted and should be performed using larger sample sizes, as well as wild boar from various geographic locations, to verify these compounds as biomarkers for MTBC infection in this species. This new approach to detect MTBC infection in free-ranging wild boar potentially comprises a reliable and efficient screening tool for surveillance in animal populations.

Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach

Although tuberculosis (TB) is a severe health problem worldwide, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used in the study of infectious diseases. We performed metabolome profiling to identify potential biomarkers in patients with active TB. Serum samples from 21 patients with active pulmonary TB, 20 subjects with latent TB infection (LTBI), and 28 healthy controls were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by multivariate and univariate analyses. Metabolic profiles indicated higher serum levels of glutamate, sulfoxy methionine, and aspartate and lower serum levels of glutamine, methionine, and asparagine in active TB patients than in LTBI subjects or healthy controls. The ratios between metabolically related partners (glutamate/glutamine, sulfoxy methionine/methionine, and aspartate/asparagine) were also elevated in the active TB group. There was no significant difference in the serum concentration of these metabolites according to the disease extent or risk of relapse in active TB patients. Novel serum biomarkers such as glutamate, sulfoxy methionine, aspartate, glutamine, methionine, and asparagine are potentially useful for adjunctive, rapid, and noninvasive pulmonary TB diagnosis.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Background

Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.

Methods

We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy.

Results

A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement.

Conclusions

There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.

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.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

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.

Diagnosis of tuberculosis through breath test: A systematic review

Background

Breath tests may diagnose tuberculosis (TB) through detecting specific volatile organic compounds produced by Mycobacterium tuberculosis or the infected host.

Methods

To estimate the diagnostic accuracy of breath test with electronic-nose and other devices against culture or other tests for TB, we screened multiple databases until January 6, 2019.

Findings

We included fourteen studies, with 1715 subjects in the analysis. The pooled sensitivity and specificity of electronic-nose were 0.93 (95% CI 0.82–0.97) and 0.93 (95% CI 0.82–0.97), respectively, and no heterogeneity was found. The sensitivity and specificity of other breath test devices ranged from 0.62 to 1.00, and 0.11 to 0.84, respectively.

Interpretation

The low to moderate evidence of these studies shows that breath tests can diagnose TB accurately, however, to give a real-time test result, additional development is needed. Research should also focus on sputum smear negative TB, children, and the positioning of breath testing in the diagnostic work flow.

Funding

The authors received no specific funding for this work.