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.

Monitoring Fish Freshness in Real Time under Realistic Conditions through a Single Metal Oxide Gas Sensor.

The realization of an unobtrusive and effective technology able to track fish freshness in real time and inform on its edibility is highly demanded, but still unachieved. In the present paper, we address this issue through a single metal oxide gas sensor working in temperature modulation mode. The system can work without an external reference air source, which is an appealing feature for its possible integration in domestic refrigerators. Tests were carried out using fresh sea bream fillets as case study and working both inside the refrigerator and at room temperature. Parallel gas chromatography-mass spectrometry and microbiological characterization indicated the marked dependence of both the microbiological condition and the gas-phase composition from the individual sample and from the storage temperature. Despite such a large variability, which may be expected in real applications, the proposed system provided similar responses whenever the total bacterial population approached and exceeded the edibility threshold of 107 CFU/g.

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.

Room Temperature Ammonia Gas Sensor Based on p-Type-like V2O5 Nanosheets towards Food Spoilage Monitoring.

Gas sensors play an important role in many areas of human life, including the monitoring of production processes, occupational safety, food quality assessment, and air pollution monitoring. Therefore, the need for gas sensors to monitor hazardous gases, such as ammonia, at low operating temperatures has become increasingly important in many fields. Sensitivity, selectivity, low cost, and ease of production are crucial characteristics for creating a capillary network of sensors for the protection of the environment and human health. However, developing gas sensors that are not only efficient but also small and inexpensive and therefore integrable into everyday life is a difficult challenge. In this paper, we report on a resistive sensor for ammonia detection based on thin V2O5 nanosheets operating at room temperature. The small thickness and porosity of the V2O5 nanosheets give the sensors good performance for sensing ammonia at room temperature (RT), with a relative change of resistance of 9.4% to 5 ppm ammonia (NH3) and an estimated detection limit of 0.4 ppm. The sensor is selective with respect to the seven interferents tested; it is repeatable and stable over the long term (four months). Although V2O5 is generally an n-type semiconductor, in this case the nanosheets show a p-type semiconductor behavior, and thus a possible sensing mechanism is proposed. The device's performance, along with its size, low cost, and low power consumption, makes it a good candidate for monitoring freshness and spoilage along the food supply chain.

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%).

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy.

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy.

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.

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.

A Camera Sensors-Based System to Study Drug Effects On In Vitro Motility: The Case of PC-3 Prostate Cancer Cells.

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

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.

Electrospinning of Polystyrene/Polyhydroxybutyrate Nanofibers Doped with Porphyrin and Graphene for Chemiresistor Gas Sensors.

Structural and functional properties of polymer composites based on carbon nanomaterials are so attractive that they have become a big challenge in chemical sensors investigation. In the present study, a thin nanofibrous layer, comprising two insulating polymers (polystyrene (PS) and polyhydroxibutyrate (PHB)), a known percentage of nanofillers of mesoporous graphitized carbon (MGC) and a free-base tetraphenylporphyrin, was deposited onto an Interdigitated Electrode (IDE) by electrospinning technology. The potentials of the working temperature to drive both the sensitivity and the selectivity of the chemical sensor were studied and described. The effects of the porphyrin combination with the composite graphene⁻polymer system appeared evident when nanofibrous layers, with and without porphyrin, were compared for their morphology and electrical and sensing parameters. Porphyrin fibers appeared smoother and thinner and were more resistive at lower temperature, but became much more conductive when temperature increased to 60⁻70 °C. Both adsorption and diffusion of chemicals seemed ruled by porphyrin according its combination inside the composite fiber, since the response rates dramatically increased (toluene and acetic acid). Finally, the opposite effect of the working temperature on the sensitivity of the porphyrin-doped fibers (i.e., increasing) and the porphyrin-free fibers (i.e., decreasing) seemed further confirmation of the key role of such a macromolecule in the VOC (volatile organic compound) adsorption.

Learning Cancer-Related Drug Efficacy Exploiting Consensus in Coordinated Motility Within Cell Clusters.

OBJECTIVE: The ability of cells to collectively move is essential in various biological contexts including cancer metastasis. In this paper, we propose an automatic video analysis tool to correlate the cell movement inhibition with replication block induced by dose-dependent chemotherapy administration. METHODS: The novel approach combines individual and collective cell kinematic analysis performed over time-lapse microscopy video frames. Cells are first localized and tracked, and then kinematic descriptors are extracted for each track. Selective track identification is performed assuming diversified cell roles within the same cluster (spontaneously forming groups of cells), and finally individual results are grouped exploiting consensus of coordinated motility within cell clusters. RESULTS: Recognition performance of three different experimental conditions (no drug, 0.5-5 µM merged in the same condition, and 50 µM) reached an average accuracy value of 88% over 958 different tracks collected in 36 clusters of diverse dimensions in eight independent experiments. CONCLUSION: An extensive application of this methodology could give a different point of view of the cancer mechanisms.

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.

Peptide Modified ZnO Nanoparticles as Gas Sensors Array for Volatile Organic Compounds (VOCs).

In this work a peptide based gas sensor array based of ZnO nanoparticles (ZnONPs) has been realized. Four different pentapeptides molecularly modeled for alcohols and esters having cysteine as a common spacer have been immobilized onto ZnONPs. ZnONPs have been morphologically and spectroscopically characterized. Modified nanoparticles have been then deposited onto quartz crystal microbalances (QCMs) and used as gas sensors with nitrogen as carrier gas. Analysis of the pure compounds modeled demonstrated a nice fitting of modeling with real data. The peptide based ZnONPs had very low sensitivity to water, compared to previously studied AuNPs peptide based gas sensors allowing the use of the array on samples with high water content. Real samples of fruit juices have been assayed; stability of the signal, good repeatability, and discrimination ability of the array was achieved.

Organs on chip approach: a tool to evaluate cancer -immune cells interactions.

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from 'wild type' donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.

Cooperative strategy for a dynamic ensemble of classification models in clinical applications: the case of MRI vertebral compression fractures.

PURPOSE: In clinical practice, the constructive consultation among experts improves the reliability of the diagnosis and leads to the definition of the treatment plan for the patient. Aggregation of the different opinions collected by many experts can be performed at the level of patient information, abnormality delineation, or final assessment. METHODS: In this study, we present a novel cooperative strategy that exploits the dynamic contribution of the classification models composing the ensemble to make the final class assignment. As a proof of concept, we applied the proposed approach to the assessment of malignant infiltration in 103 vertebral compression fractures in magnetic resonance images. RESULTS: The results obtained with repeated random subsampling and receiver operating characteristic analysis indicate that the cooperative system statistically improved ([Formula: see text]) the classification accuracy of individual modules as well as of that based on the manual segmentation of the fractures provided by the experts. CONCLUSIONS: The performances have been also compared with those obtained with those of standard ensemble classification algorithms showing superior results.