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Despite promising results obtained in the early diagnosis of several pathologies, breath analysis still remains an unused technique in clinical practice due to the lack of breath sampling standardized procedures able to guarantee a good repeatability and comparability of results. The most diffuse on an international scale breath sampling method uses polymeric bags, but, recently, devices named Mistral and ReCIVA, able to directly concentrate volatile organic compounds (VOCs) onto sorbent tubes, have been developed and launched on the market. In order to explore performances of these new automatic devices with respect to sampling in the polymeric bag and to study the differences in VOCs profile when whole or alveolar breath is collected and when pulmonary wash out with clean air is done, a tailored experimental design was developed. Three different breath sampling approaches were compared: (a) whole breath sampling by means of Tedlar bags, (b) the end-tidal breath collection using the Mistral sampler, and (c) the simultaneous collection of the whole and alveolar breath by using the ReCIVA. The obtained results showed that alveolar fraction of breath was relatively less affected by ambient air (AA) contaminants ( p -values equal to 0.04 for Mistral and 0.002 for ReCIVA Low) with respect to whole breath ( p -values equal to 0.97 for ReCIVA Whole). Compared to Tedlar bags, coherent results were obtained by using Mistral while lower VOCs levels were detected for samples (both breath and AA) collected by ReCIVA, likely due to uncorrected and fluctuating flow rates applied by this device. Finally, the analysis of all data also including data obtained by explorative analysis of the unique lung cancer (LC) breath sample showed that a clean air supply might determine a further confounding factor in breath analysis considering that lung wash-out is species-dependent.

Objectives: A number of studies have shown that the airborne transmission route could spread some viruses over a distance of 2 meters from an infected person. An epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of COVID-19 in Italy. On March 16th 2020, we presented a position paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and average concentrations of PM 10 exceeding a daily limit of 50 µg/m 3 ., Methods: To monitor the spreading of COVID-19 in Italy from February 24th to March 13th (the date of the Italian lockdown), official daily data for PM 10 levels were collected from all Italian provinces between February 9th and February 29th, taking into account the maximum lag period (14 days) between the infection and diagnosis. In addition to the number of exceedances of the daily limit value of PM 10 , we also considered population data and daily travelling information for each province., Results: Exceedance of the daily limit value of PM 10 appears to be a significant predictor of infection in univariate analyses (p<0.001). Less polluted provinces had a median of 0.03 infections over 1000 residents, while the most polluted provinces showed a median of 0.26 cases. Thirty-nine out of 41 Northern Italian provinces resulted in the category with the highest PM 10 levels, while 62 out of 66 Southern provinces presented low PM 10 concentrations (p<0.001). In Milan, the average growth rate before the lockdown was significantly higher than in Rome (0.34 vs 0.27 per day, with a doubling time of 2.0 days vs 2.6, respectively), thus suggesting a basic reproductive number R 0 >6.0, comparable with the highest values estimated for China., Conclusion: A significant association has been found between the geographical distribution of daily PM 10 exceedances and the initial spreading of COVID-19 in the 110 Italian provinces., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The burden of COVID-19 was extremely severe in Northern Italy, an area characterized by high concentrations of particulate matter (PM), which is known to negatively affect human health. Consistently with evidence already available for other viruses, we initially hypothesized the possibility of SARS-CoV-2 presence on PM, and we performed a first experiment specifically aimed at confirming or excluding this research hyphotesys., Methods: We have collected 34 PM10 samples in Bergamo area (the epicenter of the Italian COVID-19 epidemic) by using two air samplers over a continuous 3-weeks period. Filters were properly stored and underwent RNA extraction and amplification according to WHO protocols in two parallel blind analyses performed by two different authorized laboratories. Up to three highly specific molecular marker genes (E, N, and RdRP) were used to test the presence of SARS-CoV-2 RNA on particulate matter., Results: The first test showed positive results for gene E in 15 out of 16 samples, simultaneously displaying positivity also for RdRP gene in 4 samples. The second blind test got 5 additional positive results for at least one of the three marker genes. Overall, we tested 34 RNA extractions for the E, N and RdRP genes, reporting 20 positive results for at least one of the three marker genes, with positivity separately confirmed for all the three markers. Control tests to exclude false positivities were successfully accomplished., Conclusion: This is the first evidence that SARS-CoV-2 RNA can be present on PM, thus suggesting a possible use as indicator of epidemic recurrence., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The present study was aimed at determining airborne concentrations of PAHs, Nitro-/Oxy-PAHs and elements in industrial and urban areas of Taranto, a site of environmental risk in Southern Italy, after the issue of strategic measures for air pollution mitigation and control by the Italian Environment Ministry in 2012. A PM 2.5 sampling campaign was carried out from 9 to 28 December 2014 at eight receptor sites, two placed in the urban settlement and five included in the high spatial resolution fence monitoring network of the biggest European steel plant. The integration of collected data with meteorological parameters and source apportionment analysis by Positive Matrix Factorization and bivariate polar plots allowed to discriminate among emission sources and estimate their contributions. Evidence on the effect of distinct processes (homogenization, sintering) occurring inside the steel plant on airborne concentrations of PAHs and selected elements was provided. The impact of emissions from the steel plant "core" on the surrounding area was observed at receptor sites downwind to it. Moreover, the extent of the effectiveness of mitigation measures, partially applied at the moment of study's beginning, was demonstrated by mean and peak pollutant concentrations at all receptor sites up to one order of magnitude lower than those documented prior to 2012.

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output., Competing Interests: The authors declare no conflict of interest.

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses.

The present study aims to evaluate the impact of e-cig second-hand aerosol on indoor air quality in terms of ultrafine particles (UFPs) and potential inhalation exposure levels of passive bystanders. E-cig second-hand aerosol characteristics in terms of UFPs number concentration and size distribution exhaled by two volunteers vaping 15 different e-liquids inside a 49 m 3 room and comparison with tobacco smoke are discussed. High temporal resolution measurements were performed under natural ventilation conditions to simulate a realistic exposure scenario. Results showed a systematic increase in UFPs number concentration (part cm -3 ) related to a 20-min vaping session (from 6.56 × 10 3 to 4.01 × 10 4 part cm -3 ), although this was one up to two order of magnitude lower than that produced by one tobacco cigarette consumption (from 1.12 × 10 5 to 1.46 × 10 5 part cm -3 ). E-cig second-hand aerosol size distribution exhibits a bimodal behavior with modes at 10.8 and 29.4 nm in contrast with the unimodal typical size distribution of tobacco smoke with peak mode at 100 nm. In the size range 6-26 nm, particles concentration in e-cig second-hand aerosol were from 2- (Dp = 25.5 nm) to 3800-fold (Dp = 9.31 nm) higher than in tobacco smoke highlighting that particles exhaled by users and potentially inhaled by bystanders are nano-sized with high penetration capacity into human airways.

Malignant pleural mesothelioma (MPM) is a rare neoplasm related to asbestos exposure and with high mortality rate. The management of patients with MPM is complex and controversial, particularly with regard to early diagnosis. In the last few years, breath analysis has been greatly implemented with this aim. In this review the strengths of breath analysis and preliminary results in searching breath biomarkers of MPM are highlighted and discussed, respectively. Through a systematic electronic literature search, collecting papers published from 2000 until December 2018, fifteen relevant scientific papers were selected. All papers considered were prospective, comparative, observational case-control studies although every single one pilot and based on a relatively small number of samples. The identification of diagnostic VOCs pattern, through breath sample characterization and the statistical data treatment, allows to obtain a strategic information for clinical diagnostics. To date the collected data provide just preliminary information and, despite the promising results and diagnostic accuracy, conclusions cannot be generalized due to the limited number of individuals included in each cohort study. Furthermore none of studies was externally validated, although validation process is a necessary step towards clinical implementation. Breathomics-based biomarker approach should be further explored to confirm and validate preliminary findings and to evaluate its potential role in monitoring the therapeutic response.

This study provided a useful approach for assessing the impact of industrial sources on surrounding, especially in a sensitive industrial area as Taranto (South of Italy). Taranto is one of the most industrialized Italian towns, where several emission sources operate simultaneously in proximity to the urban settlement. An intensive monitoring campaign of PAHs was carried out from January 28th to July 30th, 2011, in seven sites located in residential settlement around the industrial area and in the city center. The collected data were integrated with the information about wind direction and speed by means bivariate polarplot in order to characterize and localize the industrial sources. High BaP concentrations were detected especially when Benzene to Toluene ratio (B/T ratio) values excedeed 1 and all receptor sites were downwind to the steel plant. Moreover, in order to discriminate among PAH sources and quantify their contributions, a source apportionment analysis of the collected data was provided by means Principal component Analysis (PCA) and Positive Matrix Factorization (PMF) methods. Finally, the processing of PMF5.0 output by bivariate polar plot, confirmed the impact of steel plant on both industrial sites downwind the steel plant and the city center. B[a]P apportionment was quite similar for industrial and urban sites: the traffic source contributed only 11% and 24% to B[a]P measured at two sites, respectively. Therefore, the proximity of Taranto downtown to industrial pole makes negligible all other source contributions to PAH concentrations., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In the South of Italy, it is common for farmers to burn pruning waste from olive trees in spring. In order to evaluate the impact of the biomass burning source on the physical and chemical characteristics of the particulate matter (PM) emitted by these fires, a PM monitoring campaign was carried out in an olive grove. Daily PM10 samples were collected for 1 week, when there were no open fires, and when biomass was being burned, and at two different distances from the fires. Moreover, an optical particle counter and a polycyclic aromatic hydrocarbon (PAH) analyzer were used to measure the high time-resolved dimensional distribution of particles emitted and total PAHs concentrations, respectively. Chemical analysis of PM10 samples identified organic and inorganic components such as PAHs, ions, elements, and carbonaceous fractions (OC, EC). Analysis of the collected data showed the usefulness of organic and inorganic tracer species and of PAH diagnostic ratios for interpreting the impact of biomass fires on PM levels and on its chemical composition. Finally, high time-resolved monitoring of particle numbers and PAH concentrations was performed before, during, and after biomass burning, and these concentrations were seen to be very dependent on factors such as weather conditions, combustion efficiency, and temperature (smoldering versus flaming conditions), and moisture content of the wood burned.

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies., (© 2013 Elsevier B.V. All rights reserved.)

Purpose: This study was aimed to the development of an integrated approach for the characterization of particulate matter (PM) pollution events in the South of Italy., Methods: PM(10) and PM(2.5) daily samples were collected from June to November 2008 at an urban background site located in Bari (Puglia Region, South of Italy). Meteorological data, particle size distributions and atmospheric dispersion conditions were also monitored in order to provide information concerning the different features of PM sources., Results: The collected data allowed suggesting four indicators to characterize different PM(10) exceedances. PM(2.5)/PM(10) ratio, natural radioactivity, aerosol maps and back-trajectory analysis and particle distributions were considered in order to evaluate the contribution of local anthropogenic sources and to determine the different origins of intrusive air mass coming from long-range transport, such as African dust outbreaks and aerosol particles from Central and Eastern Europe. The obtained results were confirmed by applying principal component analysis to the number particle concentration dataset and by the chemical characterization of the samples (PM(10) and PM(2.5))., Conclusions: The integrated approach for PM study suggested in this paper can be useful to support the air quality managers for the development of cost-effective control strategies and the application of more suitable risk management approaches.

Several hypotheses have been advanced to explain empty follicle syndrome (EFS) but it remains a controversial topic. This paper reports experience with three IVF cycles in which no oocytes were collected. In all cases, an additional IVF cycle was performed. The ovarian stimulation protocol, ultrasound and hormonal surveillance methods, human chorionic gonadotrophin timing and oocyte retrieval technique were similar in all patients. The assessment of additional cycles demonstrated a poor response in terms of oocyte quality, since the number of mature oocytes was low despite the high number of oocytes collected. Thus, the data suggest that in these patients, EFS should be considered as a borderline form of poor response to ovarian stimulation. If this is confirmed, EFS should be a recurrent event and an empty cycle could be a good predictor that a subsequent stimulated cycle will be an unfavourable.

Aim: To evaluate clinical and psychological status of adults with childhood diagnosis of coeliac disease who were re-exposed to gluten after only a few years and now on a gluten-containing diet, compared with adults with recent diagnosis of coeliac disease, and adults who remained on gluten-free diet after childhood diagnosis., Methods: A total of 195 adults with a biopsy suggestive of coeliac disease in childhood, who either had adhered to a gluten-free diet for at least 1 year after diagnosis and now are either on gluten-free diet (n = 110) or on gluten-containing diet (n = 85), and adults with newly diagnosed coeliac disease (n = 165) underwent a medical check-up., Results: Body mass index and main laboratory indices were statistically different among groups (lowest in never on gluten-free diet, highest in gluten-free diet). The lowest average levels of bone mineral density were found among never on gluten-free diet patients. Prevalence of autoimmune disorders was increased in never on gluten-free diet when compared with the transient gluten-free diet and gluten-free diet groups. Histology revealed villous subatrophy in all patients of never on gluten-free diet group, in 39 of 110 patients of gluten-free diet and in 84 of 85 of transient gluten-free diet groups. Herpetiform dermatitis was found in three patients of gluten-free diet, three of transient gluten-free diet and three of never on gluten-free diet. Dental enamel defects were found in 15 patients of transient gluten-free diet, 43 of never on gluten-free diet and in zero of the gluten-free diet group. Pregnancy outcome was not significantly different between the two groups, but neonatal weight was lower and breast feeding was shorter in the never on gluten-free diet group. Sexual habits, alcohol intake and cigarette smoking were significantly different in the never on gluten-free diet group when compared with the other two groups., Conclusion: Gluten withdrawal in childhood partly protects coeliac adults from clinical and behavioural effects of gluten sensitivity.