Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

Being a confirmed TB patient was directly proportional to e-nose 10-sensor responses. Principal component analysis clusters showed a clear distinction between TB and HC groups, with variances of 93%, 85%, 75% and 95% for blood, breath, sputum and urine samples, respectively. Overall accuracy, sensitivity and specificity of the artificial neural network (ANN) analysis for classifying samples were >99%. The e-nose successfully distinguished TB patients from HC participants for all measured biological samples with great precision. With urine samples gaining broader acceptance for clinical diagnosis, an e-nose-based ANN can be a very useful tool for low-cost mass screening and early detection of TB patients in developing countries.

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Objectives: Benzene is commonly emitted in several industries, leading to widespread environmental and occupational exposure hazards. While less toxic solvents have been substituted for benzene, it is still a component of petroleum products and is a trace impurity in industrial products resulting in continued higher occupational exposures in industrial settings in developing countries. Materials and Methods: We investigated the potential use of an electronic nose (e-nose) to monitor the headspace volatiles in biological samples from benzene-exposed Egyptian workers and non-exposed controls. The study population comprised 150 non-smoking male workers exposed to benzene and an equal number of matching non-exposed controls. We determined biomarkers of benzene used to estimate exposure and risk including: benzene in exhaled air and blood; and its urinary metabolites such as phenol and muconic acid using gas chromatography technique and a portable e-nose. Results: The average benzene concentration measured in the ambient air of the workplace of all studied industrial settings in Alexandria, Egypt; was 97.56±88.12 μg/m 3 (range: 4.69-260.86 μg/m 3 ). Levels of phenol and muconic acid were significantly (p < 0.001) higher in both blood and urine of benzene-exposed workers as compared to non-exposed controls. Conclusions: The e-nose technology has successfully classified and distinguished benzene-exposed workers from non-exposed controls for all measured samples of blood, urine and the exhaled air with a very high degree of precision. Thus, it will be a very useful tool for the low-cost mass screening and early detection of health hazards associated with the exposure to benzene in the industry.