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

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

Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization.

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

A Pilot Study for Legionella pneumophila Volatilome Characterization Using a Gas Sensor Array and GC/MS Techniques.

Legionellosis is a generic term describing the pneumonic (Legionnaires' disease, LD) and non-pneumonic (Pontiac fever, PF) forms of infection with bacteria belonging to the genus Legionella. Currently, the techniques used to detect Legionella spp. in water samples have certain limitations and drawbacks, and thus, there is a need to identify new tools to carry out low-cost and rapid analysis. In this regard, several studies demonstrated that a volatolomics approach rapidly detects and discriminates different species of microorganisms via their volatile signature. In this paper, the volatile organic compounds (VOCs) pattern emitted in vitro by Legionella pneumophila cultures is characterized and compared to those produced by other Legionella species and by Pseudomonas aeruginosa, using a gas sensor array and gas chromatograph mass spectrometer (GC-MS). Bacterial cultures were measured at the 3rd and 7th day after the incubation. Sensor array data analyzed via the K-nearest neighbours (k-NN) algorithm showed a sensitivity to Legionella pneumophila identification at around 89%. On the other hand, GC-MS identified a bouquet of VOCs, mainly alcohols and ketones, that enable the differentiation of Legionella pneumophila in respect to other waterborne microorganisms.

Volatolomic urinary profile analysis for diagnosis of the early stage of lung cancer.

Currently, in clinical practice there is a pressing need for potential biomarkers that can identify lung cancer at early stage before becoming symptomatic or detectable by conventional means. Several researchers have independently pointed out that the volatile organic compounds (VOCs) profile can be considered as a lung cancer fingerprint useful for diagnosis. In particular, 16% of volatiles contributing to the human volatilome are found in urine, which is therefore an ideal sample medium. Its analysis through non-invasive, relatively low-cost and straightforward techniques could offer great potential for the early diagnosis of lung cancer. In this study, urinary VOCs were analysed with a gas chromatography-ion mobility spectrometer (GC-IMS) and an electronic nose (e-nose) made by a matrix of twelve quartz microbalances complemented by a photoionization detector. This clinical prospective study involved 127 individuals, divided into two groups: 46 with lung cancer stage I-II-III confirmed by computerized tomography or positron emission tomography-imaging techniques and histology (biopsy), and 81 healthy controls. Both instruments provided a multivariate signal which, after being analysed by a machine learning algorithm, identified eight VOCs that could distinguish lung cancer patients from healthy ones. The eight VOCs are 2-pentanone, 2-hexenal, 2-hexen-1-ol, hept-4-en-2-ol, 2-heptanone, 3-octen-2-one, 4-methylpentanol, 4-methyl-octane. Results show that GC-IMS identifies lung cancer with respect to the control group with a diagnostic accuracy of 88%. Sensitivity resulted as being 85%, and specificity was 90%-Area Under the Receiver Operating Characteristics: 0.91. The contribution made by the e-nose was also important, even though the results were slightly less sensitive with an accuracy of 71.6%. Moreover, of the eight VOCs identified as potential biomarkers, five VOCs had a high sensitivity (p⩽ 0.06) for early stage (stage I) lung cancer.

Urine LOX-1 and Volatilome as Promising Tools towards the Early Detection of Renal Cancer.

Renal cell carcinoma (RCC) represents around 3% of all cancers, within which clear cell RCC (ccRCC) are the most common type (70-75%). The RCC disease regularly progresses asymptomatically and upon presentation is recurrently metastatic, therefore, an early method of detection is necessary. The identification of one or more specific biomarkers measurable in biofluids (i.e., urine) by combined approaches could surely be appropriate for this kind of cancer, especially due to easy obtainability by noninvasive method. OLR1 is a metabolic gene that encodes for the Lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1), implicated in inflammation, atherosclerosis, ROS, and metabolic disorder-associated carcinogenesis. Specifically, LOX-1 is clearly involved in tumor insurgence and progression of different human cancers. This work reports for the first time the presence of LOX-1 protein in ccRCC urine and its peculiar distribution in tumoral tissues. The urine samples headspace has also been analyzed for the presence of the volatile compounds (VOCs) by SPME-GC/MS and gas sensor array. In particular, it was found by GC/MS analysis that 2-Cyclohexen-1-one,3-methyl-6-(1-methylethyl)- correlates with LOX-1 concentration in urine. The combined approach of VOCs analysis and protein quantification could lead to promising results in terms of diagnostic and prognostic potential for ccRCC tumors.

Sensor array and gas chromatographic detection of the blood serum volatolomic signature of COVID-19.

Volatolomics is gaining consideration as a viable approach to diagnose several diseases, and it also shows promising results to discriminate COVID-19 patients via breath analysis. This paper extends the study of the relationship between volatile compounds (VOCs) and COVID-19 to blood serum. Blood samples were collected from subjects recruited at the emergency department of a large public hospital. The VOCs were analyzed with a gas chromatography mass spectrometer (GC/MS). GC/MS data show that in more than 100 different VOCs, the pattern of abundances of 17 compounds identifies COVID-19 from non-COVID with an accuracy of 89% (sensitivity 94% and specificity 83%). GC/MS analysis was complemented by an array of gas sensors whose data achieved an accuracy of 89% (sensitivity 94% and specificity 80%).

Aspergillus Species Discrimination Using a Gas Sensor Array.

The efficiency of electronic noses in detecting and identifying microorganisms has been proven by several studies. Since volatile compounds change with the growth of colonies, the identification of strains is highly dependent on the growing conditions. In this paper, the effects of growth were investigated with different species of Aspergillus, which is one of the most studied microorganisms because of its implications in environmental and food safety. For this purpose, we used an electronic nose previously utilized for volatilome detection applications and based on eight porphyrins-functionalized quartz microbalances. The volatile organic compounds (VOCs) released by cultured fungi were measured at 3, 5, and 10 days after the incubation. The signals from the sensors showed that the pattern of VOCs evolve with time. In particular, the separation between the three studied strains progressively decreases with time. The three strains could still be identified despite the influence of culture time. Linear Discriminant Analysis (LDA) showed an overall accuracy of 88% and 71% in the training and test sets, respectively. These results indicate that the presence of microorganisms is detectable with respect to background, however, the difference between the strains changes with the incubation time.

Targeting LOX-1 Inhibits Colorectal Cancer Metastasis in an Animal Model.

Recurrence and metastasis are the primary causes of mortality in patients with colorectal cancer (CRC), and therefore effective tools to reduce morbidity and mortality of CRC patients are necessary. LOX-1, the ox-LDL receptor, is strongly involved in inflammation, obesity, and atherosclerosis, and several studies have assessed its role in the carcinogenesis process linking ROS, metabolic disorders and cancer. We have already demonstrated in vitro that LOX-1 expression correlates to the aggressiveness of human colon cancer and its downregulation weakens the tumoral phenotype, indicating its potential function as a biomarker and a target in CRC therapy. Here we further investigate in vivo the role of LOX-1 in colon tumorigenesis by xenografting procedures, injecting nude mice both subcutaneously and intravenously with human high grade metastatic colorectal cancer cells, DLD-1, in which LOX-1 expression has been downregulated by shRNA (LOX-1RNAi cells). Histopathological and immunohistochemical evaluations have been performed on xenograft tumors. The experiments have been complemented by the analysis of the volatile compounds (VOCs) collected from the cages of injected mice and analyzed by gas-chromatography and gas sensors. After intravenous injection of LOX-1RNAi cells, we found that LOX-1 silencing influences both the engraftment of the tumor and the metastasis development, acting by angiogenesis. For the first time, we have observed that LOX-1 inhibition significantly prevents metastasis formation in injected mice and, at the same time, induces a downregulation of VEGF-A165, HIF-1α, and ß-catenin whose expression is involved in cell migration and metastasis, and a variation of histone H4 acetylation pattern suggesting also a role of LOX-1 in regulating gene transcription. The analysis of the volatile compounds (VOCs) collected from the cages of injected mice has evidenced a specific profile in those xenograft mice in which metastasis originates. These findings underline the role of LOX-1 as a potential target for inhibition of tumor progression and metastasis, enhancing current therapeutic strategies against colorectal cancer.

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.

Volatile compounds emission from teratogenic human pluripotent stem cells observed during their differentiation in vivo.

Several investigations point out that the volatile fraction of metabolites, often called volatilome, might signal the difference processes occurring in living beings, both in vitro and in vivo. These studies have been recently applied to stem cells biology, and preliminary results show that the composition of the volatilome of stem cells in vitro changes along the differentiation processes leading from pluripotency to full differentiation. The identification of pluripotent stem cells is of great importance to improve safety in regenerative medicine avoiding the formation of teratomas. In this paper, we applied gas chromatography and gas sensor array to the study of the volatilome released by mice transplanted with human induced pluripotent stem cells (hiPSCs) or embryoid bodies (EBs) derived from hiPSCs at 5 days and spontaneously differentiated cells at 27 day. Gas chromatography analysis finds that, in mice transplanted with hiPSCs, the abundance of 13 volatile compounds increases four weeks after the implant and immediately before the formation of malignant teratomas (grade 3) become observable. The same behaviour is also followed by the signals of the gas sensors. Besides this event, the gas-chromatograms and the sensors signals do not show any appreciable variation related neither among the groups of transplanted mice nor respect to a placebo population. This is the first in vivo observation of the change of volatile metabolites released by human induced pluripotent stem cells and hiPSCs-derived cells during the differentiation process. These results shed further light on the differentiation mechanisms of stem cells and suggest possible applications for diagnostic purposes for an early detection of tumor relapse after surgery.

A preliminary analysis of volatile metabolites of human induced pluripotent stem cells along the in vitro differentiation.

Cellular metabolism of stem cell biology is still an unexplored field. However, considering the amount of information carried by metabolomes, this is a promising field for a fast identification of stem cells itself and during the differentiation process. One of the goals of such application is the identification of residual pluripotent cells before cell transplantation to avoid the occurrence of teratomas. In this paper, we investigated in vitro the volatile compounds (VOCs) released during human induced pluripotent stem cells (hiPSCs) reprogramming. In particular, we studied hiPSCs differentiation to floating and adherent embryoid bodies until early neural progenitor cells. A preliminary Gas Chromatographic/Mass Spectrometer (GC/MS) analysis, based on a single extraction method and chromatographic separation, indicated 17 volatile compounds whose relative abundance is altered in each step of the differentiation process. The pattern of VOCs shown by hiPSCs is well distinct and makes these cells sharply separated from the other steps of differentiations. Similar behaviour has also been observed with an array of metalloporphyrins based gas sensors. The use of electronic sensors to control the process of differentiation of pluripotent stem cells might suggest a novel perspective for a fast and on-line control of differentiation processes.

Conductive Photo-Activated Porphyrin-ZnO Nanostructured Gas Sensor Array.

Chemoresistors working at room temperature are attractive for low-consumption integrated sensors. Previous studies show that this feature can be obtained with photoconductive porphyrins-coated ZnO nanostructures. Furthermore, variations of the porphyrin molecular structure alter both the chemical sensitivity and the photoconductivity, and can be used to define the sensor characteristics. Based on these assumptions, we investigated the properties of an array of four sensors made of a layer of ZnO nanoparticles coated with porphyrins with the same molecular framework but different metal atoms. The array was tested with five volatile organic compounds (VOCs), each measured at different concentrations. Results confirm that the features of individual porphyrins influence the sensor behavior, and the differences among sensors are enough to enable the discrimination of volatile compounds disregarding their concentration.

Investigation of VOCs associated with different characteristics of breast cancer cells.

The efficacy of breath volatile organic compounds (VOCs) analysis for the screening of patients bearing breast cancer lesions has been demonstrated by using gas chromatography and artificial olfactory systems. On the other hand, in-vitro studies suggest that VOCs detection could also give important indications regarding molecular and tumorigenic characteristics of tumor cells. Aim of this study was to analyze VOCs in the headspace of breast cancer cell lines in order to ascertain the potentiality of VOCs signatures in giving information about these cells and set-up a new sensor system able to detect breast tumor-associated VOCs. We identified by Gas Chromatography-Mass Spectrometry analysis a VOCs signature that discriminates breast cancer cells for: i) transformed condition; ii) cell doubling time (CDT); iii) Estrogen and Progesterone Receptors (ER, PgR) expression, and HER2 overexpression. Moreover, the signals obtained from a temperature modulated metal oxide semiconductor gas sensor can be classified in order to recognize VOCs signatures associated with breast cancer cells, CDT and ER expression. Our results demonstrate that VOCs analysis could give clinically relevant information about proliferative and molecular features of breast cancer cells and pose the basis for the optimization of a low-cost diagnostic device to be used for tumors characterization.

More than apples and oranges--detecting cancer with a fruit fly's antenna.

Cancer cells and non-cancer cells differ in their metabolism and they emit distinct volatile compound profiles, allowing to recognise cancer cells by their scent. Insect odorant receptors are excellent chemosensors with high sensitivity and a broad receptive range unmatched by current gas sensors. We thus investigated the potential of utilising the fruit fly's olfactory system to detect cancer cells. Using in vivo calcium imaging, we recorded an array of olfactory receptor neurons on the fruit fly's antenna. We performed multidimensional analysis of antenna responses, finding that cell volatiles from different cell types lead to characteristic response vectors. The distances between these response vectors are conserved across flies and can be used to discriminate healthy mammary epithelial cells from different types of breast cancer cells. This may expand the repertoire of clinical diagnostics, and it is the first step towards electronic noses equipped with biological sensors, integrating artificial and biological olfaction.