Conventional diagnostic methods for lung cancer are unsuitable for widespread screening because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1-20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solid-phase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer.

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Background:Tumour growth is accompanied by gene and/or protein changes that may lead to peroxidation of the cell membrane species and, hence, to the emission of volatile organic compounds (VOCs). In this study, we investigated the ability of a nanosensor array to discriminate between breath VOCs that characterise healthy states and the most widespread cancer states in the developed world: lung, breast, colorectal, and prostate cancers.Methods:Exhaled alveolar breath was collected from 177 volunteers aged 20–75 years (patients with lung, colon, breast, and prostate cancers and healthy controls). Breath from cancerous subjects was collected before any treatment. The healthy population was healthy according to subjective patient's data. The breath of volunteers was examined by a tailor-made array of cross-reactive nanosensors based on organically functionalised gold nanoparticles and gas chromatography linked to the mass spectrometry technique (GC-MS).Results:The results showed that the nanosensor array could differentiate between ‘healthy' and ‘cancerous' breath, and, furthermore, between the breath of patients having different cancer types. Moreover, the nanosensor array could distinguish between the breath patterns of different cancers in the same statistical analysis, irrespective of age, gender, lifestyle, and other confounding factors. The GC-MS results showed that each cancer could have a unique pattern of VOCs, when compared with healthy states, but not when compared with other cancer types.Conclusions:The reported results could lead to the development of an inexpensive, easy-to-use, portable, non-invasive tool that overcomes many of the deficiencies associated with the currently available diagnostic methods for cancer.

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Background:Head-and-neck cancer (HNC) is the eighth most common malignancy worldwide. It is often diagnosed late due to a lack of screening methods and overall cure is achieved in <50% of patients. Head-and-neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract, mostly HNC or lung cancer (LC), making lifelong follow-up necessary.Methods:Alveolar breath was collected from 87 volunteers (HNC and LC patients and healthy controls) in a cross-sectional clinical trial. The discriminative power of a tailor-made Nanoscale Artificial Nose (NA-NOSE) based on an array of five gold nanoparticle sensors was tested, using 62 breath samples. The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation. The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry (GC–MS), using 40 breath samples.Results:The NA-NOSE could clearly distinguish between (i) HNC patients and healthy controls, (ii) LC patients and healthy controls, and (iii) HNC and LC patients. The GC–MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.Conclusion:The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

INTRODUCTION The search for non-invasive diagnostic methods of lung cancer has led to new avenues of research, including the exploration of the exhaled breath. Previous studies have shown that lung cancer can in principle be detected through exhaled breath analysis. This study evaluated the potential of exhaled breath analysis for the distinction of benign and malignant pulmonary nodules (PNs). METHODS Breath samples were taken from 72 patients with PNs in a prospective trial. Profiles of volatile organic compounds (VOCs) were determined by (i) gas chromatography/mass spectrometry (GC-MS) combined with solid phase microextraction (SPME) and by (ii) a chemical nanoarray. RESULTS 53 PNs were malignant and 19 were benign with similar smoking histories and co-morbidities. Nodule size (mean +/− SD) was 2.7±1.7 vs. 1.6±1.3 cm (p=0.004) respectively. Within the malignant group, 47 were NSCLC and 6 were SCLC. Thirty had early stage disease and 23 had advanced disease. GC-MS analysis identified a significantly higher concentration of 1-octene in the breath of lung cancer, and the nanoarray distinguished significantly between benign vs. malignant PNs (p<0.0001; accuracy 88±3%), between adeno- and squamous- cell carcinomas (p<0.0001; 88±3%) and between early stage and advanced disease (p<0.0001; 88±2%). CONCLUSIONS In this pilot study, breath analysis discriminated benign from malignant PNs in a high-risk cohort based on lung cancer related VOC profiles. Further, it discriminated adeno-and squamous- cell carcinoma and between early vs. advanced disease. Further studies are required to validate this non-invasive approach, using a larger cohort of patients with PNs detected by CT.

Highly selective detection, rapid response (<20 s), and superior sensitivity (Rair/Rgas> 50) against specific target gases, particularly at the 1 ppm level, still remain considerable challenges in gas sensor applications. We propose a rational design and facile synthesis concept for achieving exceptionally sensitive and selective detection of trace target biomarkers in exhaled human breath using a protein nanocage templating route for sensitizing electrospun nanofibers (NFs). The mesoporous WO3 NFs, functionalized with well-dispersed nanoscale Pt, Pd, and Rh catalytic nanoparticles (NPs), exhibit excellent sensing performance, even at parts per billion level concentrations of gases in a humid atmosphere. Functionalized WO3 NFs with nanoscale catalysts are demonstrated to show great promise for the reliable diagnosis of diseases.

Bio-inspired Pt (∼2 nm) and Au (∼2.7 nm) catalysts encapsulated by a protein shell, i.e., Pt-apoferritin (Pt@AF) and Au-apoferriten (Au@AF), were synthesized via the hollow protein nanocage (apoferritin) templating route and directly functionalized on the interior and exterior walls of electrospun SnO2 nanotubes (NTs) during controlled single-nozzle electrospinning followed by high temperature calcination with heating rate control. Fast crystallization of the exterior shell and outward diffusion of the interior Sn precursors and crystallites result in the continued growth of a tubular wall, which is related to rapid heating driven Ostwald-ripening behavior. Very importantly, the Pt and Au nanoparticles (NPs) were immobilized onto thin-walled SnO2 NTs with a diameter of ∼350 nm and a shell thickness of ∼40 nm without any aggregation of catalysts due to high dispersibility, which originated from repulsive electrostatic (Coulombic) forces acting on the surface charged protein shells, leading to an enhanced catalytic effect and outstanding gas sensing properties. Pt-loaded SnO2 NTs exhibited superior acetone response (R(air)/R(gas) = 92 at 5 ppm) compared to pure SnO2 NFs (R(air)/R(gas) = 4.8 at 5 ppm) and SnO2 NTs (Rair/Rgas = 11 at 5 ppm) while Au-loaded SnO2 NTs showed a high response when exposed to hydrogen sulfide (R(air)/R(gas) = 34 at 5 ppm), offering selective gas detection with minimal cross-sensitivity against other interfering gases such as NH3, CO, NO, C6H5CH3, and C5H12. Our results provide a new insight into facile, cost-effective, and highly dispersible catalyst loading on the interior and exterior walls of hollow metal oxide NTs via simple electrospinning as a potential breath analyzer.