Exhaled breath contains hundreds of volatile organic compounds (VOCs). Several independent researchers point out that the breath of lung cancer patients shows a characteristic VOC-profile which can be considered as lung cancer signature and, thus, used for diagnosis. In this regard, the analysis of exhaled breath with gas sensor arrays is a potential non-invasive, relatively low-cost and easy technique for the early detection of lung cancer. This clinical study evaluated the gas sensor array response for the identification of the exhaled breath of lung cancer patients. This study involved 146 individuals: 70 with lung cancer confirmed by computerized tomography (CT) or positron emission tomography-(PET) imaging techniques and histology (biopsy) or with clinical suspect of lung cancer and 76 healthy controls. Their exhaled breath was measured with a gas sensor array composed of a matrix of eight quartz microbalances (QMBs), each functionalized with a different metalloporphyrin. The instrument produces, for each analyzed sample, a vector of signals encoding the breath (breathprint). Breathprints were analyzed with multivariate analysis in order to correlate the sensor signals to the disease. Breathprints of the lung cancer patients were differentiated from those of the healthy controls with a sensitivity of 81% and specificity of 91%. Similar values were obtained in patients with and without metabolic comorbidities, such as diabetes, obesity and dyslipidemia (sensitivity 85%, specificity 88% and sensitivity 76%, specificity 94%, respectively). The device showed a large sensitivity to lung cancer at stage I with respect to stage II/III/IV (92% and 58% respectively). The sensitivity for stage I did not change for patients with or without metabolic comorbidities (90%, 94%, respectively). Results show that this electronic nose can discriminate the exhaled breath of the lung cancer patients from those of the healthy controls. Moreover, the largest sensitivity is observed for the subgroup of patients with a lung cancer at stage I.

The current pandemic has provided an opportunity to test wastewater-based epidemiology (WBE) as a complementary method to SARS-CoV-2 monitoring in the community. However, WBE infection estimates can be affected by uncertainty factors, such as heterogeneity in analytical procedure, wastewater volume, and population size. In this paper, raw sewage SARS-CoV-2 samples were collected from four wastewater treatment plants (WWTPs) in Tuscany (Northwest Italy) between February and December 2021. During the surveillance period, viral concentration was based on polyethylene glycol (PEG), but its precipitation method was modified from biphasic separation to centrifugation. Therefore, in parallel, the recovery efficiency of each method was evaluated at lab-scale, using two spiking viruses (human coronavirus 229E and mengovirus vMC0). SARS-CoV-2 genome was found in 80 (46.5%) of the 172 examined samples. Lab-scale experiments revealed that PEG precipitation using centrifugation had the best recovery efficiency (up to 30%). Viral SARS-CoV-2 load obtained from sewage data, adjusted by analytical method and normalized by population of each WWTP, showed a good association with the clinical data in the study area. This study highlights that environmental surveillance data need to be carefully analyzed before their use in the WBE, also considering the sensibility of the analytical methods.

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