Breath Biopsy and Discovery of Exclusive Volatile Organic Compounds for Diagnosis of Infectious Diseases

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry

BACKGROUND

Acetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS.

RESULTS

A system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature.

SIGNIFICANCE

Results indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

Establishing breath as a biomarker platform-take home messages from the Breath Biopsy Conference 2023

The annual Breath Biopsy Conference hosted by Owlstone Medical gathers together the leading experts, early career researchers, and physicians working with breath as a biomarker platform for clinical purposes. The current topics in breath research are discussed and presented, and an overarching topical theme is identified and discussed as part of an expert panel to close the conference. The profiling of normal breath composition and the establishment of standards for analyzing breath compared to background signal were two important topics that were major focuses of this conference, as well as important innovative progress that has been made since last year, including the development of a non-invasive breath test for lung cancer and liver disease. This meeting report offers an overview of the key take-home messages from the various presentations, posters, and discussions from the conference.

A Concise Review of Exhaled Breath Testing for Respiratory Clinicians and Researchers

Exhaled breath contains an extensive reservoir of biomolecules. The collection of exhaled breath is noninvasive and low risk. Therefore, its testing is an appealing strategy for the discovery of biomarkers of respiratory diseases. In this concise review, we summarize the evidence of exhaled breath tests for airways diseases and respiratory infections. An overview of breath collection methods in both individuals who are spontaneously breathing and those receiving mechanical ventilation is outlined. We also highlight the challenges in exhaled breath testing and areas for future research.

Dogs' ability to detect an inflammatory immune response in cattle via olfaction

Introduction

Canine olfaction is a potential means for detection of respiratory disease in beef cattle. In a prior study, two dogs were trained to discriminate between nasal swabs from healthy cattle and cattle that developed Bovine Respiratory Disease. Dogs had some ability to identify samples from BRD-affected cattle, but results were ambiguous. The purpose of this study was to evaluate more dogs using better-controlled training and testing procedures.

Methods

Nasal and saliva swabs were collected from 96 cattle before and after administering a vaccine to induce an inflammatory immune response. Samples were stored at -80°C for up to 11 months before use, and samples from animals with an elevated body temperature at baseline were omitted. An automated olfactometer apparatus was constructed to improve blinding procedures and reduce opportunities for odor contamination. Four dogs were trained to distinguish between swabs from healthy and sickness-model cattle, including the two dogs from the previous study ("Runnels" and "Cheaps") and two inexperienced dogs ("Molokai" and "Amy"). During a seven-month training period, dogs were exposed to samples from 28 animals. Dogs were tested on 59 sets of unfamiliar samples.

Results

Performance varied among dogs (χ2 = 10.48, p = 0.02). Molokai's performance was above chance (0.73 ± 0.06, p = 0.0006), while Amy (0.44 ± 0.06, p = 0.43), Cheaps (0.53 ± 0.07, p = 0.79), and Runnels (0.56 ± 0.06, p = 0.43) did not respond correctly at a rate different from chance. Accuracy did not differ between nasal swabs (0.63 ± 0.08) and saliva swabs (0.53 ± 0.08, χ2 = 0.81, p = 0.37).

Discussion

The results of this study indicate that canine olfaction may be an effective means of detecting illness in beef cattle. However, individual dogs' aptitude for this detection task varies.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria: Current state of the art

Pathogenic microorganisms produce numerous metabolites, including volatile organic compounds (VOCs). Monitoring these metabolites in biological matrices (e.g., urine, blood, or breath) can reveal the presence of specific microorganisms, enabling the early diagnosis of infections and the timely implementation of targeted therapy. However, complex matrices only contain trace levels of VOCs, and their constituent components can hinder determination of these compounds. Therefore, modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed. In this paper, we discuss bacterial VOC analysis under in vitro conditions, in animal models and disease diagnosis in humans, including techniques for offline and online analysis in clinical settings. We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis, in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species interactions, the kinetics of VOC metabolism, and species- and drug-resistance specificity.

Breath metabolomics for diagnosis of acute respiratory distress syndrome

BACKGROUND

Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.

AIM

To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.

METHODS

This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC-MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS).

RESULTS

A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63-0.78) in the derivation cohort and 0.63 (CI 0.52-0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance.

CONCLUSION

An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Volatilome: Smells like microbial spirit

In recent years, the study of volatile compounds has sparked interest due to their implications in signaling and the enormous variety of bioactive properties attributed to them. Despite the absence of analysis methods standardization, there are a multitude of tools and databases that allow the identification and quantification of volatile compounds. These compounds are chemically heterogeneous and their diverse properties are exploited by various fields such as cosmetics, the food industry, agriculture and medicine, some of which will be discussed here. In virtue of volatile compounds being ubiquitous and fast chemical messengers, these molecules mediate a large number of interspecific and intraspecific interactions, which are key at an ecological level to maintaining the balance and correct functioning of ecosystems. This review briefly summarized the role of volatile compounds in inter- and intra-specific relationships as well as industrial applications associated with the use of these compounds that is emerging as a promising field of study.

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

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

Interaction of the III-As monolayer with SARS-CoV-2 biomarkers: implications for biosensor development

The emergence of SARS-CoV-2 in 2019 led to the global COVID-19 pandemic, highlighting the urgency for developing cost-effective and non-invasive methods to detect diseases at an early stage. Human breath, rich in volatile organic compounds (VOCs), is promising for cost-effective and rapid disease detection, with specific VOCs like methanol, ethanal, butanone, acetone, and ethyl butyrate linked to COVID-19. Recent advances in biomarker detection and gas sensing with 2D materials, particularly III-As monolayers like BAs, GaAs, and AlAs, offer high sensitivity at low concentrations, providing a novel avenue for exploring their potential in detecting COVID-19 biomarkers. This article aims to examine the effects of adsorption on different properties of III-Arsenide (BAs, GaAs and AlAs) monolayers, particularly in connection with SARS-CoV-2 biomarkers. In order to examine the interaction between the monolayers and biomarkers, first-principles computations within the framework of density functional theory (DFT) are utilized. The present study involves an investigation of the modifications in the band structure, density of states (DOS), work function, electron density difference, and optical properties (reflectance and absorbance) of III-As monolayers, with the aim of assessing their viability for the detection of SARS-CoV-2 biomarkers along with interfering gases such as CO2 and H2O. It is observed that VOCs induce a notable change in the work function of GaAs which serves as an indicator of the presence of these biomarkers. However, the changes in work function are not as substantial as those for AlAs and BAs. Additionally, the chemiresistive sensitivity, optical sensitivity and recovery time of III-As are investigated. The findings suggest that the pristine GaAs monolayer displays a significant level of sensitivity and selectivity towards the SARS-CoV-2 biomarkers, rendering it a material with potential for utilization in sensing applications. Furthermore, it has been observed that the recovery time of the GaAs monolayer subsequent to its exposure to the VOC biomarkers lies within an acceptable threshold. Upon exposure to UV light, the recovery time is further reduced. The outcomes of our study indicate that GaAs monolayers exhibit considerable potential as chemiresistive, work function-based and optical sensors for the precise and discerning identification of VOCs linked to the SARS-CoV-2 virus compared to the other two III-As monolayers.

Dogs can detect an odor profile associated with Staphylococcus aureus biofilms in cultures and biological samples

Introduction

The study investigated the utilization of odor detection dogs to identify the odor profile of Staphylococcus aureus (S. aureus) biofilms in pure in vitro samples and in in vivo biosamples from animals and humans with S. aureus periprosthetic joint infection (PJI). Biofilms form when bacterial communities aggregate on orthopedic implants leading to recalcitrant infections that are difficult to treat. Identifying PJI biofilm infections is challenging, and traditional microbiological cultures may yield negative results even in the presence of clinical signs.

Methods

Dogs were trained on pure in vitro S. aureus biofilms and tested on lacrimal fluid samples from an in vivo animal model (rabbits) and human patients with confirmed S. aureus PJI.

Results

The results demonstrated that dogs achieved a high degree of sensitivity and specificity in detecting the odor profile associated with S. aureus biofilms in rabbit samples. Preliminary results suggest that dogs can recognize S. aureus volatile organic compounds (VOCs) in human lacrimal fluid samples.

Discussion

Training odor detection dogs on in vitro S. aureus, may provide an alternative to obtaining clinical samples for training and mitigates biosecurity hazards. The findings hold promise for culture-independent diagnostics, enabling early disease detection, and improved antimicrobial stewardship. In conclusion, this research demonstrates that dogs trained on in vitro S. aureus samples can identify the consistent VOC profile of PJI S. aureus biofilm infections. The study opens avenues for further investigations into a retained VOC profile of S. aureus biofilm infection. These advancements could revolutionize infectious disease diagnosis and treatment, leading to better patient outcomes and addressing the global challenge of antimicrobial resistance.

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.

Pediatric Sepsis Diagnosis, Management, and Sub-phenotypes

Sepsis and septic shock are major causes of morbidity, mortality, and health care costs for children worldwide, including >3 million deaths annually and, among survivors, risk for new or worsening functional impairments, including reduced quality of life, new respiratory, nutritional, or technological assistance, and recurrent severe infections. Advances in understanding sepsis pathophysiology highlight a need to update the definition and diagnostic criteria for pediatric sepsis and septic shock, whereas new data support an increasing role for automated screening algorithms and biomarker combinations to assist earlier recognition. Once sepsis or septic shock is suspected, attention to prompt initiation of broad-spectrum empiric antimicrobial therapy, fluid resuscitation, and vasoactive medications remain key components to initial management with several new and ongoing studies offering new insights into how to optimize this approach. Ultimately, a key goal is for screening to encompass as many children as possible at risk for sepsis and trigger early treatment without increasing unnecessary broad-spectrum antibiotics and preventable hospitalizations. Although the role for adjunctive treatment with corticosteroids and other metabolic therapies remains incompletely defined, ongoing studies will soon offer updated guidance for optimal use. Finally, we are increasingly moving toward an era in which precision therapeutics will bring novel strategies to improve outcomes, especially for the subset of children with sepsis-induced multiple organ dysfunction syndrome and sepsis subphenotypes for whom antibiotics, fluid, vasoactive medications, and supportive care remain insufficient.

Detection of medically relevant volatile organic compounds with graphene field-effect transistors and separated by low-frequency spectral and time signatures

Exhaled human breath contains a mixture of gases including nitrogen, oxygen, carbon dioxide, water vapour and low molecular weight volatile organic compounds (VOCs). Different VOCs detected in human breath condensate have been recently related to several metabolic processes occurring inside body tissues in the pathological state, as candidate biomarkers for monitoring conditions such as lung injury, airway inflammation, immunity dysfunction, infection, and cancer. Current techniques for detecting these compounds include several types of mass spectroscopy, which are highly costly, time-consuming and dependent on trained personnel for sample analysis. The need for fast and label-free biosensors is paving the way towards the design of novel and portable electronic devices for point-of-care diagnosis with VOCs such as E-noses, and based on the measurement of signal signatures derived from their chemical composition. In this paper, we propose a device for VOC detection that was tested inside a controlled gas flow setup, resorting to graphene field-effect transistors (GFETs). Electrical measurements from graphene directly exposed to nitrogen plus VOC vapours involved cyclic measurements for the variation of graphene's resistance and low-frequency spectral noise in order to obtain distinctive signatures of the tested compounds in the time and frequency domains related, respectively, to Gutmann's theory for donor-acceptor chemical species and spectral sub-band analysis.

Secondary-Electrospray Ionization Mass Spectrometry-Based Online Analyses of Mouse Volatilome Uncover Gut Microbiome-Dictated Metabolic Changes in the Host

The symbiotic relationship between the gut microbial population is capable of regulating numerous aspects of host physiology, including metabolism. Bacteria can modulate the metabolic processes of the host by feeding on nutritional components within the lumen and releasing bioactive components into circulation. Endogenous volatile organic compound (VOC) synthesis is dependent on the availability of precursors found in mammalian metabolism. Herein, we report that microbial-mediated metabolic influences can alter the host volatilome and the prominent volatile changes can be uncovered by a novel volatile analysis technique named secondary electrospray ionization mass spectrometry. Mice were subjected to an antibiotic cocktail to deplete the microbiome and then inoculated with either single strain bacteria or fecal matter transplantation (FMT) to replete the microbial population in the gut. VOC sampling was achieved by using an advanced secondary electrospray ionization (SESI) source that directly mounted onto a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). A principal component analysis summarizing the volatile profiles of the mice revealed independent clustering of each strain of the FMT-inoculated groups, suggesting unique volatile profiles. The Mummichog algorithm uncovered phenylalanine metabolism as a significantly altered metabolic profile in the volatilome of the microbiome-repleted mice. Our results indicated that the systemic metabolic changes incurred by the host are translated to unique volatile profiles correlated to the diversity of the microbial population colonized within the host. It is thus possible to take advantage of SESI-HRMS-based platforms for noninvasive screening of VOCs to determine the contribution of various microbial colonization within human gut that may impact host health.

Water interference in the chromatographic analysis of exhaled breath samples: Challenges and mitigation strategies

This study demonstrates the adverse effects of water in exhaled breath samples on the accuracy of breath biomarker analysis when using gas chromatography. The presence of water in exhaled breath significantly modifies the retention times and peak areas of compounds, particularly for low-boiling, early eluting compounds. To tackle this issue, a two-step approach is introduced. The process begins with thorough desorption of the sorbent tube using a high split ratio and a short analysis duration, followed by a secondary analysis of the same tube. The efficacy of the new, straightforward approach was illustrated using humid breath samples and 57 compound standard mixture. This study highlights the importance of proper sample pretreatment and analysis to ensure reliable and accurate results in clinical research.

Breath biopsy, a novel technology to identify head and neck squamous cell carcinoma: A systematic review

Head and neck cancers are a heterogeneous group of neoplasms, which together comprise the sixth most common cancer globally. Breath biopsies are a non-invasive clinical investigation that detect volatile organic compounds (VOCs) in exhaled breath. This systematic review examines current applications of breath biopsy for the diagnosis of head and neck squamous cell carcinoma (HNSCC), including data on efficacy and utility, and speculates on the future uses of this non-invasive detection method. Medline, PubMed, Web of Science, Cochrane and Scopus, as well as the grey literature were searched using a search strategy developed to identify relevant studies on the role of breath biopsy in the diagnosis of HNSCC. All included studies were subject to a thorough methodological quality assessment. The initial search generated a total of 1443 articles, 20 of which were eligible for review. A total of 660 HNSCC samples were investigated across the included studies. 3,7-dimethylundecane and benzaldehyde were among several VOCs to be significantly correlated with the presence of HNSCC compared to healthy controls. We show that current breath biopsy methods have high accuracy, specificity and sensitivity for identifying HNSCC. However, further studies are needed given the reported poor quality of the included studies.

Advancements in Rapid and Affordable Diagnostic Testing for Respiratory Infectious Diseases: Evaluation of Aptamer Beacon Technology for Rapid and Sensitive Detection of SAR-CoV-2 in Breath Condensate

The demand for rapid and efficient diagnostic point-of-care tests for respiratory infectious diseases has become increasingly critical in the current landscape. The emphasis on accessibility has been underscored over the past year, making it crucial to have biological components that exhibit fast and accurate kinetics. The foundation for precise, swift, and effective testing relies on the availability of highly responsive biological agents. Two published aptamer DNA sequences designated Song and MSA52 and their truncated internal stem-loop structures were studied for their potential to serve as aptamer beacons for rapid COVID detection. The candidate beacons were covalently labeled with Atto 633 dye attached to their 5' ends and Iowa Black quencher attached to their 3' ends. The whole aptamer structures exhibited the greatest fluorescence signal intensities and higher fluorescence background than their truncated internal stem-loop beacon structures suggesting that the distance between fluorophores and quenchers was greater for the whole aptamer beacon candidates versus the isolated stem-loop structures. Beacon candidates were tested against two heat- or gamma radiation-killed SARS-CoV-2 Washington 1/2020 virus samples and three different COVID spike (S) proteins to test their effectiveness. Despite the higher background fluorescence, the whole aptamer beacons showed better signal-to-noise ratios and were selected for further investigation. Limit of detection (LOD) studies revealed that both the whole Song and whole MSA52 aptamer beacon candidates had a LOD of 9.61 × 103 genome equivalents in phosphate-buffered saline using the red channel of a Promega Quantus™ fluorometer which correlated well with confirmatory spectrofluorometry. Cross-reactivity studies using numerous COVID variants, related coronaviruses, and other common respiratory pathogens suggested greater COVID selectivity for the whole MSA52 versus the whole Song aptamer beacon candidate, indicating promise for specific COVID detection. Importantly, both whole aptamer beacon candidates exhibited very rapid "bind and detect" fluorescence increases within the first 1-2 min of mixing the beacons with killed SARS-CoV-2 viruses in 100 µl samples. Overall, this work illustrates the strong potential for aptamer beacons for rapid, on-site detection and presumptive diagnosis of COVID in breath condensates or other small liquid samples. This research highlights the strong potential of aptamer beacons for addressing the need for fast and convenient diagnostic tools in global health contexts, especially in resource-limited settings.

Lung microbiome and origins of the respiratory diseases

The studies on the composition of the human microbiomes in healthy individuals, its variability in the course of inflammation, infection, antibiotic therapy, diets and different pathological conditions have revealed their intra and inter-kingdom relationships. The lung microbiome comprises of major species members of the phylum Bacteroidetes, Firmicutes, Actinobacteria, Fusobacteria and Proteobacteria, which are distributed in ecological niches along nasal cavity, nasopharynx, oropharynx, trachea and in the lungs. Commensal and pathogenic species are maintained in equilibrium as they have strong relationships. Bacterial overgrowth after dysbiosis and/or imbalanced of CD4⁺ helper T cells, CD8⁺ cytotoxic T cells and regulatory T cells (Treg) populations can promote lung inflammatory reactions and distress, and consequently acute and chronic respiratory diseases. This review is aimed to summarize the latest advances in resident lung microbiome and its participation in most common pulmonary infections and pneumonia, community-acquired pneumonia (CAP), ventilator-associated pneumonia (VAP), immunodeficiency associated pneumonia, SARS-CoV-2-associated pneumonia, acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD). We briefly describe physiological and immunological mechanisms that selectively create advantages or disadvantages for relative growth of pathogenic bacterial species in the respiratory tract. At the end, we propose some directions and analytical methods that may facilitate the identification of key genera and species of resident and transient microbes involved in the respiratory diseases' initiation and progression.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

iOBPdb A Database for Experimentally Determined Functional Characterization of Insect Odorant Binding Proteins

Odorant binding proteins (OBPs) are extra-cellular proteins that solubilize and transport volatile organic compounds (VOCs). Thousands of OBPs have been identified through genome sequencing, and hundreds have been characterized by fluorescence ligand binding assays in individual studies. There is a limited understanding of the comparative structure-function relations of OBPs, primarily due to a lack of a centralized database that relates OBP binding affinity and structure. Combining 181 functional studies containing 382 unique OBPs from 91 insect species, we present a database, iOBPdb, of OBP binding affinities for 622 individual VOC targets. This initial database provides powerful search and associative capabilities for retrieving and analyzing OBP-VOC binding interaction data. We have validated this dataset using phylogenetic mapping to determine the authenticity of the collected sequences and whether they cluster according to their assigned subfamilies. Potential applications include developing molecular probes for biosensors, novel bioassays and drugs, targeted pesticides that inhibit VOC/OBP interactions, and understanding odor sensing and perception in the brain.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

Artificial Olfactory Biohybrid System: An Evolving Sense of Smell

The olfactory system can detect and recognize tens of thousands of volatile organic compounds (VOCs) at low concentrations in complex environments. Bioelectronic nose (B-EN), which mimics olfactory systems, is becoming an emerging sensing technology for identifying VOCs with sensitivity and specificity. B-ENs integrate electronic sensors with bioreceptors and pattern recognition technologies to enable medical diagnosis, public security, environmental monitoring, and food safety. However, there is currently no commercially available B-EN on the market. Apart from the high selectivity and sensitivity necessary for volatile organic compound analysis, commercial B-ENs must overcome issues impacting sensor operation and other problems associated with odor localization. The emergence of nanotechnology has provided a novel research concept for addressing these problems. In this work, the structure and operational mechanisms of biomimetic olfactory systems are discussed, with an emphasis on the development and immobilization of materials. Various biosensor applications and current developments are reviewed. Challenges and opportunities for fulfilling the potential of artificial olfactory biohybrid systems in fundamental and practical research are investigated in greater depth.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain reaction (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCOhost, and Scopus, along with hand searching. The search was conducted in the titles and abstracts of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 - 82%) and inconsistent; therefore, it is only recommended for screening.

A novel set of volatile urinary biomarkers for late-life major depressive and anxiety disorders upon the progression of frailty: a pilot study

Mood and anxiety disorders are frequent in the elderly and increase the risk of frailty. This study aimed to identify novel biomarkers of major depressive disorder (MDD) and anxiety in the elderly. We examined 639 participants in the community-dwelling Otassha Study (518 individuals considered healthy control, 77 with depression, anxiety, etc.), mean age 75 years, 58.4% of female. After exclusion criteria, we analyzed VOCs from 18 individuals (9 healthy control, 9 of MDD/agoraphobia case). Urinary volatile and semi-volatile organic compounds (VOCs) were profiled using solid-phase microextraction and gas chromatography-mass spectrometry. Six urinary VOCs differed in the absolute area of the base peak between participants with MDD and/or agoraphobia and controls. High area under the receiver-operating characteristic curve (AUC) values were found for phenethyl isothiocyanate (AUC: 0.86, p = 0.009), hexanoic acid (AUC: 0.85, p = 0.012), texanol (AUC: 0.99, p = 0.0005), and texanol isomer (AUC: 0.89, p = 0.005). The combined indices of dimethyl sulfone, phenethyl isothiocyanate, and hexanoic acid, and texanol and texanol isomer showed AUCs of 0.91 (p = 0.003) and 0.99 (p = 0.0005) and correlated with the GRID-HAMD and the Kihon Checklist (CL score), respectively. These VOCs may be valuable biomarkers for evaluating MDD and/or agoraphobia in the elderly.

Validating Discriminative Signatures for Obstructive Sleep Apnea in Exhaled Breath

Rapid and reliable tools for the diagnosis and monitoring of obstructive sleep apnea (OSA) are currently lacking. Prior studies using a chemical analysis of exhaled breath have suggested the existence of an OSA-specific metabolic signature. Here, we validated this diagnostic approach and the proposed marker compounds, as well as their potential to reliably diagnose OSA. In this cross-sectional observational study, exhaled breath was analyzed using secondary electrospray ionization high-resolution mass spectrometry. The study cohort included untreated OSA patients, OSA patients treated with continuous positive airway pressure and healthy subjects. The robustness of previously reported OSA markers was validated based on detectability, significant differences between groups (Mann–Whitney U test) and classification performance. The breath analysis of 118 participants resulted in 42 previously reported markers that could be confirmed in this independent validation cohort. Nine markers were significantly increased in untreated OSA compared to treated OSA, with a subset of them being consistent with a previous validation study. An OSA prediction based on the confirmed OSA signature performed with an AUC of 0.80 (accuracy 77%, sensitivity 73% and specificity 80%). As several breath markers were clearly found to be repeatable and robust in this independent validation study, these results underscore the clinical potential of breath analysis for OSA diagnostics and monitoring.

Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Lung cancer is one of the leading causes of cancer incidence and cancer-related deaths in the world. Early diagnosis of pulmonary tumors results in improved survival compared to diagnosis with more advanced disease, yet early disease is not reliably indicated by symptoms. Despite of the improved testing and monitoring techniques for lung cancer in the past decades, most diagnostic tests, such as sputum cytology or tissue biopsies, are invasive and risky, rendering them unfeasible for large population screening. The non-invasive analysis of exhaled breath has gained attentions as an innovative screening method to measure chemical alterations within the human volatilome profile as a result of oncogenesis. More importantly, volatile organic compounds (VOCs) have been correlated to the pathophysiology of disease since the source of volatile compounds relies mostly on endogenous metabolic processes that are altered as a result of disease onset. Therefore, studying VOCs emitted from human breath may assist lung cancer diagnosis, treatment monitoring, and other surveillance of this devastating disease. In this mini review, we evaluated recent human studies that have attempted to identify lung cancer-derived volatiles in exhaled breath of patients. We also examined reported volatiles in cell cultures of lung cancer to better understand the origins of cancer-associated VOCs. We highlight the metabolic processes of lung cancer that could be responsible for the endogenous synthesis of these VOCs and pinpoint the protein-encoding genes involved in these pathways. Finally, we highlight the potential value of a breath test in lung cancer and propose prominent areas for future research required for the incorporation of VOCs-based testing into clinical settings.

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma

Simple Summary

Malignant pleural mesothelioma (MPM) is an incurable asbestos-related thoracic cancer for which early-stage diagnosis remains a major challenge. Volatile organic compounds (VOCs), which are metabolites present in exhaled breath, have proven to be promising non-invasive biomarkers for MPM. However, without the necessary validation in an independent group of individuals, clinical implementation is hampered. Therefore, we performed external validation of a VOC-based prediction model for MPM, which initially revealed a poor performance and thus poor generalisability of the model. However, subsequent updating of the model improved its performance in the validation cohort, resulting in a more generalisable model with a screening potential, which could significantly impact MPM management.

Abstract

During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AEx) controls. The predictive performance of the previously developed model was assessed by determining the degree of agreement between the predicted and actual outcome of the participants (patient/control). Additionally, to optimise the performance, the model was updated by refitting it to the validation cohort. External validation revealed a poor performance of the original model as the accuracy was estimated at only 41%, indicating poor generalisability. However, subsequent updating of the model improved the differentiation between MPM patients and AEx controls significantly (73% accuracy, 92% sensitivity, and 92% negative predictive value), substantiating the validity of the original predictors. This updated model will be more generalisable to the target population and exhibits key characteristics of a potential screening test for MPM, which could significantly impact MPM management.

Diagnostic Ability of Volatile Organic Compounds in Digestive Cancer: A Systematic Review With Meta-Analysis

Background:

Volatile organic compounds (VOCs) have been involved in cancer diagnosis via breath, urine, and feces. We aimed to assess the diagnostic ability of VOCs on digestive cancers.

Methods:

We systematically reviewed prospective clinical trials evaluating VOCs’ diagnostic ability on esophageal, gastric, colorectal, hepatic, and pancreatic cancer (PC). Databases including PubMed and Ovid-Medline were searched.

Results:

A total of 35 trials with 5314 patient-times qualified for inclusion. The pooled sensitivity of VOCs diagnosing gastroesophageal cancer from healthy controls is 0.89 (95% confidence interval [CI]: 0.82-0.94), the pooled specificity is 0.890 (95% CI: 0.84-0.93), and area under the curve (AUC) of the summary receiver operating characteristic curve is 0.95 (95% CI: 0.93-0.95). The pooled sensitivity of VOCs diagnosing colorectal cancer from heathy controls is 0.92 (95% CI: 0.85-0.96), the pooled specificity is 0.88 (95% CI: 0.77-0.94), and the AUC is 0.96 (95% CI: 0.94-0.97). The pooled sensitivity of VOCs distinguishing gastrointestinal (GI) cancer from precancerous lesions is 0.84 (95% CI: 0.67-0.92), the pooled specificity is 0.74 (95% CI: 0.43-0.91), and the AUC is 0.87 (95% CI: 0.84-0.89). The pooled sensitivity of VOCs diagnosing hepatocellular carcinoma is 0.68 (95% CI: 0.52-0.81), the pooled specificity is 0.81 (95% CI: 0.47-0.96), and the AUC is 0.78 (95% CI: 0.74-0.81). The pooled sensitivity of VOCs diagnosing PC is 0.88 (95% CI: 0.80-0.93), the pooled specificity is 0.82 (95% CI: 0.62-0.93), and the AUC is 0.92 (95% CI: 0.89-0.94).

Conclusions:

Volatile organic compounds have potential role in diagnosing GI cancer with comparatively high sensitivity, specificity, and AUC (PROSPERO registration number: CRD42021260039).

Haze Air Pollution Health Impacts of Breath-Borne VOCs

Here, we investigated the use of breath-borne volatile organic compounds (VOCs) for rapid monitoring of air pollution health effects on humans. Forty-seven healthy college students were recruited, and their exhaled breath samples (n = 235) were collected and analyzed for VOCs before, on, and after two separate haze pollution episodes using gas chromatography-ion mobility spectrometry (GC-IMS). Using a paired t-test and machine learning model (Gradient Boosting Machine, GBM), six exhaled VOC species including propanol and isoprene were revealed to differ significantly among pre-, on-, and post-exposure in both haze episodes, while none was found between clean control days. The GBM model was shown capable of differentiating between pre- and on-exposure to haze pollution with a precision of 90-100% for both haze episodes. However, poor performance was detected for the same model between two different clean days. In addition to gender and particular haze occurrence influences, correlation analysis revealed that NH₄⁺, NO₃^-, acetic acid, mesylate, CO, NO₂, PM_2.5, and O₃ played important roles in the changes in breath-borne VOC fingerprints following haze air pollution exposure. This work has demonstrated direct evidence of human health impacts of haze pollution while identifying potential breath-borne VOC biomarkers such as propanol and isoprene for haze air pollution exposure.

Biomedical detection dogs for the identification of SARS-CoV-2 Infections from axillary sweat and breath samples

A PCR test of a nasal swab is still the "gold standard" for detecting a SARS-CoV-2 infection. However, PCR testing could be usefully complemented by non-invasive, fast, reliable, cheap methods for detecting infected individuals in busy areas (e.g. airports and railway stations) or remote areas. Detection of the volatile, semivolatile and non-volatile compound signature of SARS-CoV-2 infection by trained sniffer dogs might meet these requirements. Previous studies have shown that well-trained dogs can detect SARS-CoV-2 in sweat, saliva and urine samples. The objective of the present study was to assess the performance of dogs trained to detect the presence of SARS-CoV-2 in axillary-sweat-stained gauzes and on expired breath trapped in surgical masks. The samples were provided by individuals suffering from mild-to-severe coronavirus disease 2019 (COVID-19), asymptomatic individuals, and individuals vaccinated against COVID-19. Results: Seven trained dogs tested on 886 presentations of sweat samples from 241 subjects and detected SARS-CoV-2 with a diagnostic sensitivity (relative to the PCR test result) of 89.6% (95% confidence interval (CI): 86.4-92.2%) and a specificity of 83.9% (95% CI: 80.3-87.0%) - even when people with a low viral load were included in the analysis. When considering the 207 presentations of sweat samples from vaccinated individuals, the sensitivity and specificity were respectively 85.7% (95% CI: 68.5-94.3) and 86.0% (95% CI: 80.2-90.3%). The likelihood of a false-positive result was greater in the two weeks immediately after COVID-19 vaccination. Four of the seven dogs also tested on 262 presentations of mask samples from 98 subjects; the diagnostic sensitivity was 83.1% (95% CI: 73.2-89.9) and the specificity was 88.6% (95% CI: 83.3-92.4%). There was no difference (McNemar's test P=0.999) in the dogs' abilities to detect the presence of SARS-CoV-2 in paired samples of sweat-stained gauzes vs. surgical masks worn for only 10 minutes. Conclusion: Our findings confirm the promise of SARS-CoV-2 screening by detection dogs and broaden the method's scope to vaccinated individuals and easy-to-obtain face masks, and suggest that a "dogs + confirmatory rapid antigen detection tests" screening strategy might be worth investigating.

Preliminary method for profiling volatile organic compounds in breath that correlate with pulmonary function and other clinical traits of subjects diagnosed with cystic fibrosis: a pilot study

Cystic fibrosis (CF) is characterized by chronic respiratory infections which progressively decrease lung function over time. Affected individuals experience episodes of intensified respiratory symptoms called pulmonary exacerbations (PEx) which accelerate pulmonary function decline and decrease survival. There is no standard classification for PEx, which results in treatments that are heterogeneous. Improving PEx classification and management is a significant priority for people with CF. Previous studies have shown volatile organic compounds (VOCs) in exhaled breath can be used as biomarkers because they are products of metabolic pathways dysregulated by different diseases. To provide insights on PEx classification and other clinical factors, exhaled breath was collected from subjects with CF, with some experiencing PEx and others at baseline. Exhaled breath was collected in Tedlar bags during tidal breathing for VOC analysis by solid phase microextraction coupled to gas chromatography-mass spectrometry. Statistical significance testing between quantitative and categorical clinical variables displayed percent-predicted forced expiratory volume in one second (FEV1pp) was decreased in subjects experiencing PEx. VOCs correlating with other clinical variables (body mass index, age, use of highly effective modulator therapies, and need for antibiotics) were also explored. VOCs correlating to potential confounding variables were removed and analyzed by regression for correlations with FEV1pp measurements. The VOC with the highest correlation with FEV1pp (3,7-dimethyldecane) also gave the lowest p-value when comparing subjects at baseline and during PEx. Receiver operator characteristic curves showed 3,7-dimethyldecane had a higher ability to classify PEx (area under the curve (AUC) = 0.91) relative to FEV1pp values at collection (AUC = 0.83). However, normalized ΔFEV1pp values had the highest capability to distinguish PEx (AUC = 0.93). These results show that exhaled VOCs may be a source of biomarkers for various clinical traits of CF, including PEx, that should be explored in larger sample cohorts and validation studies.

Breath odor-based individual authentication by an artificial olfactory sensor system and machine learning

The potential feasibility of breath odor sensing-based individual authentication was demonstrated by a 16-channel chemiresistive sensor array and machine learning.

Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC₁ = 95.9%, PC₂ = 1.0%, PC₃ = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%–99.9%) and 100% (88.4%–100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.

Rapid diagnostic tests in the management of pneumonia

INTRODUCTION

Pneumonia is one of the main causes of mortality associated with infectious diseases worldwide. Several challenges have been identified in the management of patients with pneumonia, ranging from accurate and cost-effective microbiological investigations, prompt and adequate therapeutic management, and optimal treatment duration.

AREAS COVERED

In this review, an updated summary on the current management of pneumonia patients is provided and the epidemiological issues of infectious respiratory diseases, which in the current pandemic situation are of particular concern, are addressed. The clinical and microbiological approaches to pneumonia diagnosis are reviewed, including discussion about the new molecular assays pointing out both their strengths and limitations. Finally, the current recommendations about antibiotic treatment are examined and discussed depending on the epidemiological contexts, including those with high prevalence of multidrug-resistant bacteria.

EXPERT OPINION

We claim that rapid diagnostic tests, if well-positioned in the diagnostic workflow and reserved for the subset of patients who could most benefit from these technologies, may represent an interesting and feasible tool to optimize timing of targeted treatments especially in terms of early de-escalation or discontinuation of antibiotic therapy.

Exhaled breath biomarkers of influenza infection and influenza vaccination

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVP). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

Detection of Volatile Organic Compounds as Potential Novel Biomarkers for Chorioamnionitis - Proof of Experimental Models

Background: Histologic chorioamnionitis is only diagnosed postnatally which prevents interventions. We hypothesized that volatile organic compounds (VOCs) in the amniotic fluid might be useful biomarkers for chorioamnionitis and that VOC profiles differ between amnionitis of different origins. Methods: Time-mated ewes received intra-amniotic injections of media or saline (controls), or live Ureaplasma parvum serovar 3 (Up) 14, 7 or 3d prior to c-section at day 124 gestational age (GA). 100 μg recombinant ovine IL-1α was instilled at 7, 3 or 1d prior to delivery. Headspace VOC profiles were measured from amniotic fluids at birth using ion mobility spectrometer coupled with multi-capillary columns. Results: 127 VOC peaks were identified. 27 VOCs differed between samples from controls and Up- or IL-1α induced amnionitis. The best discrimination between amnionitis by Up vs. IL-1α was reached by 2-methylpentane, with a sensitivity/specificity of 96/95% and a positive predictive value/negative predictive values of 96 and 95%. The concentration of 2-methylpentane in VOCs peaked 7d after intra-amniotic instillation of Up. Discussion: We established a novel method to study headspace VOC profiles of amniotic fluids. VOC profiles may be a useful tool to detect and to assess the duration of amnionitis induced by Up. 2-methylpentane was previously described in the exhalate of women with pre-eclampsia and might be a volatile biomarker for amnionitis. Amniotic fluids analyzed by ion mobility spectrometry coupled with multi-capillary columns may provide bedside diagnosis of amnionitis and understanding inflammatory mechanisms during pregnancy.