Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

Exhaled breath analysis in the diagnosis of head and neck cancer

Head and neck cancer (HNC) comprises a heterogeneous group of upper aerodigestive tract malignant neoplasms, the most frequent of which is squamous cell carcinoma. HNC forms the eighth most common cancer type and the incidence is increasing. However, survival has improved only moderately during the past decades. Currently, early diagnosis remains the mainstay for improving treatment outcomes in this patient population. Unfortunately, screening methods to allow early detection of HNC are not yet established. Therefore, many cases are still diagnosed at advanced stage, compromising outcomes. Exhaled breath analysis (EBA) is a diagnostic tool that has been recently introduced for many cancers. Breath analysis is non-invasive, cost-effective, time-saving, and can potentially be applied for cancer screening. Here, we provide a summary of the accumulated evidence on the feasibility of EBA in the diagnosis of HNC.

Screening strategies for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is rare and, if untreated, has a median survival of 2-3 years. Pulmonary arterial hypertension may be idiopathic (IPAH) but is frequently associated with other conditions. Despite increased awareness, therapeutic advances, and improved outcomes, the time from symptom onset to diagnosis remains unchanged. The commonest symptoms of PAH (breathlessness and fatigue) are non-specific and clinical signs are usually subtle, frequently preventing early diagnosis where therapies may be more effective. The failure to improve the time to diagnosis largely reflects an inability to identify patients at increased risk of PAH using current approaches. To date, strategies to improve the time to diagnosis have focused on screening patients with a high prevalence [systemic sclerosis (10%), patients with portal hypertension assessed for liver transplantation (2-6%), carriers of mutations of the gene encoding bone morphogenetic protein receptor type II, and first-degree relatives of patients with heritable PAH]. In systemic sclerosis, screening algorithms have demonstrated that patients can be identified earlier, however, current approaches are resource intensive. Until, recently, it has not been considered possible to screen populations for rare conditions such as IPAH (prevalence 5-15/million/year). However, there is interest in the use of artificial intelligence approaches in medicine and the application of diagnostic algorithms to large healthcare data sets, to identify patients at risk of rare conditions. In this article, we review current approaches and challenges in screening for PAH and explore novel population-based approaches to improve detection.

Quantitative Measures of Reliability and Sensitivity of Nanoparticle-Based Sensors in Detecting Volatile Organic Compounds

We herein provide quantitative measures of sensors' reliability and sensitivity as a function of the sensor's capacity (maximum detection signal or saturation state) in addition to other adsorption-desorption parameters that define the detection signals toward volatile organic compounds (VOCs). The measures we have developed show differentiation between irregular dispersed points of sensors with low and high capacities. We show that the sharpest capacity that separates between the two types of distribution points, viz the reliability limit (RL), is tightly linked with the desorption constant k _d. Less sharp RLs give interpretations of other reliability indicators. RL also provides information about the reliability of detecting signals of VOCs for a given sensor and sensors for a particular VOC. We show that sensors with high capacities are more reliable and sensitive to detecting signals of VOCs than sensors with lower capacities.

Diagnosis of Human Echinococcosis via Exhaled Breath Analysis: A Promise for Rapid Diagnosis of Infectious Diseases Caused by Helminths

Background

Human echinococcosis is a neglected infectious disease affecting more than 1 million people globally. Its diagnosis is expensive and difficult because of lack of adequate resources in low-resource locations, where most cases occur.

Methods

A group of volunteers diagnosed with the 2 main types of echinococcosis and corresponding control groups were recruited from hospitals in Tunisia (32 patients with cystic echinococcosis and 43 controls) and Poland (16 patients with alveolar echinococcosis and 8 controls). Breath samples were collected from all patients and analyzed by gas chromatography coupled to mass spectrometry, and a specifically developed electronic nose system.

Results

The chemical analysis revealed statistically different concentrations of 2 compounds in the breath of patients with cystic echinococcosis compared to controls, and statistically different concentrations of 7 compounds in the breath of patients with alveolar echinococcosis compared to controls. The discrimination accuracy achieved by the electronic nose system was 100% for cystic echinococcosis and 92.9% for alveolar echinococcosis, while the discrimination accuracy between these 2 patient groups was 92.1%.

Conclusion

Here we advocate a noninvasive, fast, easy-to-operate and nonexpensive diagnostic tool for the diagnosis of human echinococcosis disease through exhaled breath analysis, suitable for early diagnosis and population screening.

Human beings as islands of stability: Monitoring body states using breath profiles

By checking the reproducibility of conventional mid-infrared Fourier spectroscopy of human breath in a small test study (15 individuals), we found that a set of volatile organic compounds (VOC) of the individual breath samples remains reproducible at least for 18 months. This set forms a unique individual’s “island of stability” (IOS) in a multidimensional VOC concentration space. The IOS stability can simultaneously be affected by various life effects as well as the onset of a disease. Reflecting the body state, they both should have different characteristics. Namely, they could be distinguished by different temporal profiles: In the case of life effects (beverage intake, physical or mental exercises, smoking etc.), there is a non-monotonic shift of the IOS position with the return to the steady state, whereas a progressing disease corresponds to a monotonic IOS shift. As a first step of proving these dependencies, we studied various life effects with the focus on the strength and characteristic time of the IOS shift. In general, our results support homeostasis on a long time scale of months, allostasis on scales of hours to weeks or until smoke quitting for smokers, as well as resilience in the case of recovery from a disease.

Exhaled volatile substances in children suffering from type 1 diabetes mellitus: results from a cross-sectional study

Monitoring metabolic adaptation to type 1 diabetes mellitus in children is challenging. Analysis of volatile organic compounds (VOCs) in exhaled breath is non-invasive and appears as a promising tool. However, data on breath VOC profiles in pediatric patients are limited. We conducted a cross-sectional study and applied quantitative analysis of exhaled VOCs in children suffering from type 1 diabetes mellitus (T1DM) (n = 53) and healthy controls (n = 60). Both groups were matched for sex and age. For breath gas analysis, a very sensitive direct mass spectrometric technique (PTR-TOF) was applied. The duration of disease, the mode of insulin application (continuous subcutaneous insulin infusion vs. multiple daily insulin injection) and long-term metabolic control were considered as classifiers in patients. The concentration of exhaled VOCs differed between T1DM patients and healthy children. In particular, T1DM patients exhaled significantly higher amounts of ethanol, isopropanol, dimethylsulfid, isoprene and pentanal compared to healthy controls (171, 1223, 19.6, 112 and 13.5 ppbV vs. 82.4, 784, 11.3, 49.6, and 5.30 ppbV). The most remarkable differences in concentrations were found in patients with poor metabolic control, i.e. those with a mean HbA1c above 8%. In conclusion, non-invasive breath testing may support the discovery of basic metabolic mechanisms and adaptation early in the progress of T1DM.

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.

Global Plasma Profiling for Colorectal Cancer-Associated Volatile Organic Compounds: a Proof-of-Principle Study

Volatile organic compounds (VOCs) could reflect changes resulting from ongoing pathophysiological processes and altered body metabolisms, and thus have been studied for various types of cancers. We aimed to test an advanced global metabolomic technique to characterize circulating VOCs in patients diagnosed with colorectal cancer (CRC). We employed solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography mass-spectrometry (GC × GC-MS). We analyzed 30 random plasma samples from incident cases of CRC. The 30 samples were from population controls enrolled in a large population-based case-control study. The number of metabolite peaks detected in the cases was significantly lower than that detected in the controls (median 1530 vs. 1694, P = 0.02). Partial least squares-discriminant analysis showed clear VOC profile differences between the CRC and the controls. After adjustment for multiple comparisons at the 5% false discovery rate level, five VOCs were differentially expressed between the cases and the controls. Among these five VOCs, 2,3,4-trimethyl-hexane (decreased) and 2,4-dimethylhept-1-ene (increased) were both lipid peroxidation products but not previously reported for CRC. In summary, this study pointed to an intriguing observation that the richness of volatile metabolites may be reduced in CRC cases and demonstrated the utility of SPME GC × GC-MS in discovery of candidate markers for further validation.

Chronic obstructive pulmonary disease (COPD) and lung cancer: common pathways for pathogenesis

Chronic obstructive pulmonary disease (COPD) and lung cancer comprise the leading causes of lung disease-related mortality worldwide. Exposure to tobacco smoke is a mutual aetiology underlying the two diseases, accounting for almost 90% of cases. There is accumulating evidence supporting the role of immune dysfunction, the lung microbiome, extracellular vesicles and underlying genetic susceptibility in the development of COPD and lung cancer. Further, epigenetic factors, involving DNA methylation and microRNA expression, have been implicated in both diseases. Chronic inflammation is a key feature of COPD and could be a potential driver of lung cancer development. Using next generation technologies, further studies investigating the genomics, epigenetics and gene-environment interaction in key molecular pathways will continue to elucidate the pathogenic mechanisms underlying the development of COPD and lung cancer, and contribute to the development of novel diagnostic and prognostic tools for early intervention and personalised therapeutic strategies.

Exhaled volatile organic compounds in adult asthma: a systematic review

The search for biomarkers that can guide precision medicine in asthma, particularly those that can be translated to the clinic, has seen recent interest in exhaled volatile organic compounds (VOCs). Given the number of studies reporting "breathomics" findings and its growing integration in clinical trials, we performed a systematic review of the literature to summarise current evidence and understanding of breathomics technology in asthma.A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed (CRD42017084145) of MEDLINE, Embase and the Cochrane databases to search for any reports that assessed exhaled VOCs in adult asthma patients, using the following terms (asthma AND (volatile organic compounds AND exhaled) OR breathomics).Two authors independently determined the eligibility of 2957 unique records, of which 66 underwent full-text review. Data extraction and risk of bias assessment was performed on the 22 studies deemed to fulfil the search criteria. The studies are described in terms of methodology and the evidence narratively summarised under the following clinical headings: diagnostics, phenotyping, treatment stratification, treatment monitoring and exacerbation prediction/assessment.Our review found that most studies were designed to assess diagnostic potential rather than focus on underlying biology or treatable traits. Results are generally limited by a lack of methodological standardisation and external validation and by insufficiently powered studies, but there is consistency across the literature that exhaled VOCs are sensitive to underlying inflammation. Modern studies are applying robust breath analysis workflows to large multi-centre study designs, which should unlock the full potential of measurement of exhaled volatile organic compounds in airways diseases such as asthma.

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.

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

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

Chemiresistive Sensor Array and Machine Learning Classification of Food

Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.

Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.

eNose analysis of volatile chemicals from dogs naturally infected with Leishmania infantum in Brazil

BACKGROUND

Visceral leishmaniasis (VL) in Brazil is a neglected, vector-borne, tropical parasitic disease that is responsible for several thousand human deaths every year. The transmission route involves sand flies becoming infected after feeding on infected reservoir host, mainly dogs, and then transmitting the Leishmania infantum parasites while feeding on humans. A major component of the VL control effort is the identification and euthanasia of infected dogs to remove them as a source of infection. A rapid, non-invasive, point-of-care device able to differentiate between the odours of infected and uninfected dogs may contribute towards the accurate diagnosis of canine VL.

METHODOLOGY/PRINCIPAL FINDINGS

We analysed the headspace volatile chemicals from the hair of two groups of dogs collected in 2017 and 2018 using a bench-top eNose volatile organic chemical analyser. The dogs were categorised as infected or uninfected by PCR analysis of blood samples taken by venepuncture and the number of parasites per ml of blood was calculated for each dog by qPCR analysis. We demonstrated using a robust clustering analysis that the eNose data could be discriminated into infected and uninfected categories with specificity >94% and sensitivity >97%. The eNose device and data analysis were sufficiently sensitive to be able to identify infected dogs even when the Leishmania population in the circulating blood was very low.

CONCLUSIONS/SIGNIFICANCE

The study illustrates the potential of the eNose to rapidly and accurately identify dogs infected with Le. infantum. Future improvements to eNose analyser sensor sensitivity, sampling methodology and portability suggest that this approach could significantly improve the diagnosis of VL infected dogs in Brazil with additional potential for effective diagnosis of VL in humans as well as for the diagnosis of other parasitic diseases.

Comparison of breath sampling methods: a post hoc analysis from observational cohort studies

In this report, we present a post hoc analysis from two observational cohorts, comparing the global breath volatile profile captured when using polymer sampling bags (mixed breath) versus Bio-VOC™ (alveolar breath). The cohorts were originally designed to characterize the breath volatile profiles of Malawian children with and without uncomplicated falciparum malaria. Children aged 3-15 years were recruited from ambulatory pediatric centers in Lilongwe, Malawi. Breath sampling was carried out two months apart (one study using a Bio-VOC™ and the second using sampling bags), and all samples were analyzed by gas chromatography/mass spectrometry. The efficacy of breath collection was assessed by quantifying levels of two high prevalence breath compounds, acetone and isoprene, as well as determining the overall number of breath compounds collected and their abundance. We found that the mean number of volatiles detected using sampling bags was substantially higher than when using the Bio-VOC™ (137 vs. 47). Breath collection by Bio-VOC™ also yielded reduced levels of endogenous breath volatiles, isoprene and acetone, even after breath volume correction. This suggests that the Bio-VOC™ dilutes the volatiles and introduces dead air or ambient air. Our results suggest that sampling bags are better suited for biomarker discovery and untargeted search of volatiles in pediatric populations, as evidenced by superior breath volatile detection.

Screening for gastric cancer using exhaled breath samples

BACKGROUND

The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population.

METHODS

A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population.

RESULTS

The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier.

CONCLUSION

This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.

Breath analysis of cancer in the present and the future

Most of the currently used diagnostics for cancerous diseases have yet to meet the standards of screening, as they are insufficiently accurate and/or invasive and risky. In this review, we describe the rationale, the progress made to date, and the potential of analysing the exhaled volatile organic compounds as a pathway for enabling early diagnosis of cancer and, therefore, for achieving better clinical prognosis and survival rates. The review highlights the major advancements made in this field, from fundamentals, up to translational phases and clinical trials, with a special emphasis on sensing platforms based on nanomaterials. The prospects for breath analysis in early cancerous disease are presented and discussed.

Sensors for detecting pulmonary diseases from exhaled breath

This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath. One part of the review is a didactic presentation of the overlaying concept and the chemistry of exhaled breath. The other part discusses diverse sensors that have been developed and used for the detection of respiratory diseases (e.g. chronic obstructive pulmonary disease, asthma, lung cancer, pulmonary arterial hypertension, tuberculosis, cystic fibrosis, obstructive sleep apnoea syndrome and pneumoconiosis) by analysis of VOCs in exhaled breath. The strengths and pitfalls are discussed and criticised, particularly in the perspective in disseminating information regarding these advances. Ideas regarding the improvement of sensors, sensor arrays, sensing devices and the further planning of workflow are also discussed.

Detection of volatile organic compounds from exhaled breath by nanomaterial-based sensors is a new diagnostics frontier in the screening of pulmonary diseases.http://bit.ly/2JoBKXn

Analysis of Nitrogen-containing Compounds in Mouth-exhaled Breath by Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry

Nitrogen-containing compounds are important components in human breath. However, their origins have not yet been clearly understood. In this study, a modified electrospray ionization (ESI) source coupling with quadrupole time-of-flight mass spectrometry has been used for breath analysis. Fourteen nitrogen-containing compounds were identified in mouth-exhaled breath, and 10 of them were from the oral cavity and oropharynx. Moreover, 8 of these nitrogen-containing compounds were recognized as endogenous metabolites. This result provides important clues for exploring the biological origins of these nitrogen-containing compounds. Observation of the ion suppression phenomenon also indicates that breath analysis should be carried out after clearing of the oral cavity and oropharynx, or directly through nose-breathing to eliminate the influence of those nitrogen-containing compounds from the oral cavity.

Application of Urinary Volatile Organic Compounds (VOCs) for the Diagnosis of Prostate Cancer

BACKGROUND

Prostate cancer (PCa) screening using serum prostate-specific antigen (PSA) testing has caused unnecessary biopsies and overdiagnosis owing to its low accuracy and reliability. Therefore, there is an increased interest in identifying better PCa biomarkers. Studies showed that trained dogs can discriminate patients with PCa from unaffected men by sniffing urine. We hypothesized that urinary volatile organic compounds (VOCs) may be the source of that odor and could be used to develop urinary VOC PCa diagnosis models.

PATIENTS AND METHODS

Urine samples from 55 and 53 biopsy proven PCa-positive and -negative patients respectively were initially obtained for diagnostic model development. Urinary metabolites were analyzed by gas chromatography-mass spectrometry. A PCa diagnosis model was developed and validated using innovative statistical machine-learning techniques. A second set of samples (53 PCa-positive and 22 PCa-negative patients) were used to evaluate the previously developed PCa diagnosis model.

RESULTS

The analysis resulted in 254 and 282 VOCs for their significant association (P < .05) with either PCa-positive or -negative samples respectively. Regularized logistic regression analysis and the Firth method were then applied to predict PCa prevalence, resulting in a final model that contains 11 VOCs. Under cross-validation, the area under the receiver operating characteristic curve (AUC) for the final model was 0.92 (sensitivity, 0.96; specificity, 0.80). Further evaluation of the developed model using a testing cohort yielded an AUC of 0.86. As a comparison, the PSA-based diagnosis model only rendered an AUC of 0.54.

CONCLUSION

The study describes the development of a urinary VOC-based model for PCa detection.

Enhanced Selectivity of MXene Gas Sensors through Metal Ion Intercalation: In Situ X-ray Diffraction Study

Gas molecules are known to interact with two-dimensional (2D) materials through surface adsorption where the adsorption-induced charge transfer governs the chemiresistive sensing of various gases. Recently, titanium carbide (Ti₃C₂T _x) MXene emerged as a promising sensing channel showing the highest sensitivity among 2D materials and unique gas selectivity. However, unlike conventional 2D materials, MXenes show metallic conductivity and contain interlayer water, implying that gas molecules will likely interact in a more complex way than the typical charge transfer model. Therefore, it is important to understand the role of all factors that may influence gas sensing. Here, we studied the gas-induced interlayer swelling of Ti₃C₂T _x MXene thin films and its influence on gas sensing performance. In situ X-ray diffraction was employed to simultaneously measure dynamic swelling behavior where Ti₃C₂T _x MXene films displayed selective swelling toward ethanol vapor over CO₂ gas. Results show that the controlling sodium ion concentration in the interlayers is highly important in tuning the swelling behavior and gas sensing performance. The degree of swelling matched well with the gas response intensity, and the highest gas selectivity toward ethanol vapor was achieved for Ti₃C₂T _x sensing channels treated with 0.3 mM NaOH, which also displayed the largest amount of swelling. Our results demonstrate that controlling the interlayer transport of Ti₃C₂T _x MXene is essential for enhancing the selective sensing of gas molecules.

Fluorescent electrospun PMMA microfiber mats with embedded NaYF4: Yb/Er upconverting nanoparticles

Functional upconverting nanoparticles (UCNPs) can offer new possibilities in fluorescent applications as they exhibit desired characteristic properties like large shift between the fluorescent emission signal and the infrared excitation wavelength, multi- and narrow-band absorption and emission in visible and near infrared - Vis/NIR, together with excellent photostability and low toxicity as opposed to semiconducting quantum dots. The upconversion luminescence emission or quenching characteristics of UCNPs can be altered upon exposure to physical or chemical environmental factors providing thus a functionality that can be utilized for sensing or imaging. Furthermore their functionalization with suitable indicator dyes or recognition elements can extend the range of luminescence response and ratiometric sensing to specific analytes. Synergistically, electrospun nano- and microfibers offering large surface area can enhance the functionality of UCNPs by retaining the fluorescence efficiency and improving the overall responsivity due to dramatically increased surface. For the optimization of this hybrid material system the controllable incorporation of UCNPs is required especially at increased concentration conditions needed for high brightness. Herein, we report the fabrication, morphological and optical characterization of electrospun polymer-based nanocomposite fibers, consisting of poly(methyl methacrylate) (PMMA) and upconverting lanthanide doped nanoparticles of the type NaYF₄ : 20% Yb³⁺/2% Er³⁺ @ NaYF₄. Morphological studies regarding the uniformity and aggregation effects of the UCNP inclusion within the fibers have been implemented followed by upconversion emission characterization by pulsed near-infrared excitation. The study and optimization of such nanocomposite fibrous systems could provide useful insights for the development of efficient upconverting electrospun fiber mats for a number of imaging and sensing applications.

Feasibility of lung cancer screening in developing countries: challenges, opportunities and way forward

Lung cancer is the leading cause of all cancer deaths worldwide, comprising 18.4% of all cancer deaths. Low-dose computed tomography (LDCT) has shown mortality benefit in various trials and now a standard tool for lung cancer screening. Most researches have been carried out in developed countries where lung cancer incidence and mortality is very high. There is an increasing trend in lung cancer incidence in developing countries attributed to tobacco smoking and various environmental and occupational risk factors. Implementation of lung cancer screening is challenging, so organised lung cancer screening is practically non-existent. There are numerous challenges in implementing such programs ranging from infrastructure, trained human resources, referral algorithm to cost and psychological trauma due to over-diagnosis. Pulmonary tuberculosis and other chest infections are important issues to be addressed while planning for lung cancer screening in developing countries. Burden of these diseases is very high and can lead to over-diagnosis in view of cut off of lung nodule size in various studies. Assessment of high risk cases for lung cancer is difficult as various forms of smoking make quantification non-uniform and difficult. Lung cancer screening targets only high risk population unlike screening programs for other cancers where entire population is targeted. There is a need of lung cancer screening for high risk cases as it saves life. Tobacco control and smoking cessation remain the most important long term intervention to decrease morbidity and mortality from lung cancer in developing countries. There is no sufficient evidence supporting the introduction of population-based screening for lung cancer in public health services.

Panoramic Visualization of Circulating MicroRNAs Across Neurodegenerative Diseases in Humans

Neurodegenerative diseases (NDs) such as Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and dementia pose one of the greatest health challenges this century. Although these NDs have been looked at as single entities, the underlying molecular mechanisms have never been collectively visualized to date. With the advent of high-throughput genomic and proteomic technologies, we now have the opportunity to visualize these diseases in a whole new perspective, which will provide a clear understanding of the primary and secondary events vital in achieving the final resolution of these diseases guiding us to new treatment strategies to possibly treat these diseases together. We created a knowledge base of all microRNAs known to be differentially expressed in various body fluids of ND patients. We then used several bioinformatic methods to understand the functional intersections and differences between AD, PD, ALS, and MS. These results provide a unique panoramic view of possible functional intersections between AD, PD, MS, and ALS at the level of microRNA and their cognate genes and pathways, along with the entities that unify and separate them. While the microRNA signatures were apparent for each ND, the unique observation in our study was that hsa-miR-30b-5p overlapped between all four NDS, and has significant functional roles described across NDs. Furthermore, our results also show the evidence of functional convergence of miRNAs which was associated with the regulation of their cognate genes represented in pathways that included fatty acid synthesis and metabolism, ECM receptor interactions, prion diseases, and several signaling pathways critical to neuron differentiation and survival, underpinning their relevance in NDs. Envisioning this group of NDs together has allowed us to propose new ways of utilizing circulating miRNAs as biomarkers  and in visualizing diverse NDs more holistically . The critical molecular insights gained through the discovery of ND-associated miRNAs, overlapping miRNAs, and the functional convergence of microRNAs on vital pathways strongly implicated in neurodegenerative processes can prove immensely valuable in the identifying new generation of biomarkers, along with the development of miRNAs into therapeutics.

Light-Regulated Electrochemical Reaction Assisted Core-Shell Heterostructure for Detecting Specific Volatile Markers with Controllable Sensitivity and Selectivity

Breath analysis has been considered a noninvasive, safe, and reliable way to diagnose cancer at very early stage. Rapid detection of cancer volatile markers in breath samples via a portable sensing device will lay the foundation of future early cancer diagnosis. Nevertheless, unsatisfactory sensitivity and specificity of these sensing devices restrain the clinical application of breath analysis. Herein, we proposed the strategy of designing the light-regulated electrochemical reaction assisted core-shell heterostructure to address the issue of concern; that is, the photoactive shell will be designed for trigging the light-regulated electrochemical reaction and enhancing the sensitivity while a catalytic active core will play the function of removing interference gases. After screening of various core candidates, Fe₂O₃ was found to exhibit relatively low conversion rate to 3-methylhexane, which is one of the representative volatile markers for breath analysis, suggesting that mutual interference would be eliminated by Fe₂O₃. Based on this assumption, an electrochemical sensor comprising core-shell Fe₂O₃@ZnO-SE (vs Mn-based RE) was fabricated and sensing properties to 6 kinds of volatile markers was evaluated. Interestingly, the thickness of ZnO shell significantly influenced the response behavior; typically, the Fe₂O₃@ZnO with shell thickness of 4.8 nm offers the sensor high selectivity to 3-methylhexane. In contrast, significantly mutual response interference is observed for the Fe₂O₃@ZnO with extremely thick/thin shell. Particularly, sensing properties are greatly enhanced upon illumination; a detection limit to 3-methylhexane can even be as low as 0.072 ppm which will be useful in clinic application. Besides, the high selectivity of the sensor to 3-methylhexane is further confirmed by the testing of simulated breath samples. In summary, we anticipate that the strategy proposed in this research will be a starting point for artificially tailoring the sensitivity and selectivity of future sensing devices.

Non-contact breath sampling for sensor-based breath analysis

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes (normal, relaxed breathing and deep breathing) and two exhalation manners (via smaller and wider lips opening) forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing.

Wearable and flexible electronics for continuous molecular monitoring

Wearable biosensors have received tremendous attention over the past decade owing to their great potential in predictive analytics and treatment toward personalized medicine. Flexible electronics could serve as an ideal platform for personalized wearable devices because of their unique properties such as light weight, low cost, high flexibility and great conformability. Unlike most reported flexible sensors that mainly track physical activities and vital signs, the new generation of wearable and flexible chemical sensors enables real-time, continuous and fast detection of accessible biomarkers from the human body, and allows for the collection of large-scale information about the individual's dynamic health status at the molecular level. In this article, we review and highlight recent advances in wearable and flexible sensors toward continuous and non-invasive molecular analysis in sweat, tears, saliva, interstitial fluid, blood, wound exudate as well as exhaled breath. The flexible platforms, sensing mechanisms, and device and system configurations employed for continuous monitoring are summarized. We also discuss the key challenges and opportunities of the wearable and flexible chemical sensors that lie ahead.

Nanostructured Gas Sensors for Medical and Health Applications: Low to High Dimensional Materials

Human breath has long been known as a system that can be used to diagnose diseases. With advancements in modern nanotechnology, gas sensors can now diagnose, predict, and monitor a wide range of diseases from human breath. From cancer to diabetes, the need to treat at the earliest stages of a disease to both increase patient outcomes and decrease treatment costs is vital. Therefore, it is the promising candidate of rapid and non-invasive human breath gas sensors over traditional methods that will fulfill this need. In this review, we focus on the nano-dimensional design of current state-of-the-art gas sensors, which have achieved records in selectivity, specificity, and sensitivity. We highlight the methods of fabrication for these devices and relate their nano-dimensional materials to their record performance to provide a pathway for the gas sensors that will supersede.

Breath Sensors for Health Monitoring

Breath sensors can revolutionize medical diagnostics by on-demand detection and monitoring of health parameters in a noninvasive and personalized fashion. Despite extensive research for more than two decades, however, only a few breath sensors have been translated into clinical practice. Actually, most never even left the scientific laboratories. Here, we describe key challenges that currently impede realization of breath sensors and highlight strategies to overcome them. Specifically, we start with breath marker selection (with emphasis on metabolic and inflammatory markers) and breath sampling. Next, the sensitivity, stability, and selectivity requirements for breath sensors are described. Concepts are elaborated to systematically address these requirements by material design (focusing on chemoresistive metal oxides), orthogonal arrays, and filters. Finally, aspects of portable device integration, user communication, and clinical applicability are discussed.

[Medical diagnosis by breath analysis: odor sensors]

An odor sensor is a device for detecting target odors within a mixture, used in many fields including medical diagnosis. Electronic noses are networks of olfactory sensors, consisting of a surface whose properties are modified in the presence of odors, coupled with a measurement system. Their olfactory signature is analyzed in comparison with databases. Such portable devices can monitor body odors, e.g. in the breath, so as to reliably diagnose various pathologies at an early stage and non-invasively. It is tempting to use the naturally optimized molecular recognition of odorants and intrinsic sensitivity of the animal olfactory system to detect and discriminate minute amounts of odorants. New bioelectronic hybrid devices or "bioelectronic noses" can be designed by replacing the artificial sensory elements of e-noses by proteins naturally binding odorants, particularly olfactory receptors. As in the animal olfactory system, the detection and discrimination of odorants require a network of olfactory receptors. Prototypes of such miniaturized bioelectronic noses yield promising results.

Sensors for Lung Cancer Diagnosis

The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population screening, and then, it is strongly related to non-invasiveness and low cost. Sensor technology takes advantage of the microelectronics revolution, and then, it promises to develop devices sufficiently sensitive to detect lung cancer biomarkers. A number of biosensors for the detection of cancer-related proteins have been demonstrated in recent years. At the same time, the interest is growing towards the analysis of volatile metabolites that could be measured directly from the breath. In this paper, a review of the state-of-the-art of biosensors and volatile compound sensors is presented.

Nanostructured Chemiresistive Gas Sensors for Medical Applications

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors.

Carbon Nanotube Chemical Sensors

Carbon nanotubes (CNTs) promise to advance a number of real-world technologies. Of these applications, they are particularly attractive for uses in chemical sensors for environmental and health monitoring. However, chemical sensors based on CNTs are often lacking in selectivity, and the elucidation of their sensing mechanisms remains challenging. This review is a comprehensive description of the parameters that give rise to the sensing capabilities of CNT-based sensors and the application of CNT-based devices in chemical sensing. This review begins with the discussion of the sensing mechanisms in CNT-based devices, the chemical methods of CNT functionalization, architectures of sensors, performance parameters, and theoretical models used to describe CNT sensors. It then discusses the expansive applications of CNT-based sensors to multiple areas including environmental monitoring, food and agriculture applications, biological sensors, and national security. The discussion of each analyte focuses on the strategies used to impart selectivity and the molecular interactions between the selector and the analyte. Finally, the review concludes with a brief outlook over future developments in the field of chemical sensors and their prospects for commercialization.

Effect of film thickness in gelatin hybrid gels for artificial olfaction

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

A pH-based sensor array for the detection and identification of proteins using CdSe/ZnS quantum dots as an indicator

A novel and simple fluorescence sensor array was developed for the detection and identification of proteins using pH buffer solutions as sensing elements.

Ligand-Capped Ultrapure Metal Nanoparticle Sensors for the Detection of Cutaneous Leishmaniasis Disease in Exhaled Breath

Human cutaneous leishmaniasis, although designated as one of the most neglected tropical diseases, remains underestimated due to its misdiagnosis. The diagnosis is mainly based on the microscopic detection of amastigote forms, isolation of the parasite, or the detection of Leishmania DNA, in addition to its differential clinical characterization; these tools are not always available in routine daily practice, and they are expensive and time-consuming. Here, we present a simple-to-use, noninvasive approach for human cutaneous leishmaniasis diagnosis, which is based on the analysis of volatile organic compounds in exhaled breath with an array of specifically designed chemical gas sensors. The study was realized on a group of n = 28 volunteers diagnosed with human cutaneous leishmaniasis and a group of n = 32 healthy controls, recruited in various sites from Tunisia, an endemic country of the disease. The classification success rate of human cutaneous leishmaniasis patients achieved by our sensors test was 98.2% accuracy, 96.4% sensitivity, and 100% specificity. Remarkably, one of the sensors, based on CuNPs functionalized with 2-mercaptobenzoxazole, yielded 100% accuracy, 100% sensitivity, and 100% specificity for human cutaneous leishmaniasis discrimination. While AuNPs have been the most extensively used in metal nanoparticle-ligand sensing films for breath sensing, our results demonstrate that chemical sensors based on ligand-capped CuNPs also hold great potential for breath volatile organic compounds detection. Additionally, the chemical analysis of the breath samples with gas chromatography coupled to mass spectrometry identified nine putative breath biomarkers for human cutaneous leishmaniasis.

Nanowire-Based Biosensors: From Growth to Applications

Over the past decade, synthesized nanomaterials, such as carbon nanotube, nanoparticle, quantum dot, and nanowire, have already made breakthroughs in various fields, including biomedical sensors. Enormous surface area-to-volume ratio of the nanomaterials increases sensitivity dramatically compared with macro-sized material. Herein we present a comprehensive review about the working principle and fabrication process of nanowire sensor. Moreover, its applications for the detection of biomarker, virus, and DNA, as well as for drug discovery, are reviewed. Recent advances including self-powering, reusability, sensitivity in high ionic strength solvent, and long-term stability are surveyed and highlighted as well. Nanowire is expected to lead significant improvement of biomedical sensor in the near future.

Profiling Single Cancer Cells with Volatolomics Approach

Single-cell analysis is a rapidly evolving to characterize molecular information at the individual cell level. Here, we present a new approach with the potential to overcome several key challenges facing the currently available techniques. The approach is based on the identification of volatile organic compounds (VOCs), viz. organic compounds having relatively high vapor pressure, emitted to the cell's headspace. This concept is demonstrated using lung cancer cells with various p53 genetic status and normal lung cells. The VOCs were analyzed by gas chromatography combined with mass spectrometry. Among hundreds of detected compounds, 18 VOCs showed significant changes in their concentration levels in tumor cells versus control. The composition of these VOCs was found to depend, also, on the sub-molecular structure of the p53 genetic status. Analyzing the VOCs offers a complementary way of querying the molecular mechanisms of cancer as well as of developing new generation(s) of biomedical approaches for personalized screening and diagnosis.

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Measurement of VOCs was achieved at the single-cell level

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Genetic changes influence the emitted volatiles of single and bulk cancer cells

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Single-cell VOC analysis measures population heterogeneity in initial stage of tumors

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Volatolomics research can promote non-invasive, simple, and cost-effective diagnostics

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.