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Background: Scientific evidence demonstrates that poor sleep quality can lead to various health problems. This study aimed to investigate sleep patterns among Italian university students and identify several factors that may contribute to its quality., Study Design: Cross-sectional study., Methods: An electronic questionnaire regarding sociodemographic characteristics, lifestyle, and sleep-related habits, including the Pittsburgh Sleep Quality Index (PSQI) questionnaire, was distributed between January 2022 and July 2023 among students belonging to 12 universities located in Northern, Central, and Southern Italy., Results: On a total of 1,674 questionnaires collected, the participants (mean age 24.06±4.56 years, 71.3% F) reported an average number of hours of nocturnal sleep equal to 6.89±1.28 hours. A total of 927 (54.6%) of respondents showed a poor sleep quality (PSQI >5). Regression analysis showed that better sleep quality is associated with lower age, attending universities in Northern Italy, less time spent on electronic devices during the day, not being used to study at night and not playing videogames before sleep., Conclusion: From a public health perspective, our findings suggest that public health operators should raise the awareness of young adults about the importance of sleep quality for maintaining good health, as well as the impact that certain behaviors can have on sleep.

Introduction: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers., Study Design: The aim of the study is to standardize a method for predicting the risk of Legionella contamination in the water supply of a hospital facility, by comparing Machine Learning, conventional and combined models., Methods: During the period July 2021- October 2022, water sampling for Legionella detection was performed in the rooms of an Italian hospital pavilion (89.9% of the total number of rooms). Fifty-eight parameters regarding the structural and environmental characteristics of the water network were collected. Models were built on 70% of the dataset and tested on the remaining 30% to evaluate accuracy, sensitivity, and specificity., Results: A total of 1,053 water samples were analyzed and 57 (5.4%) were positive for Legionella. Of the Machine Learning models tested, the most efficient had an input layer (56 neurons), hidden layer (30 neurons), and output layer (two neurons). Accuracy was 93.4%, sensitivity was 43.8%, and specificity was 96%. The regression model had an accuracy of 82.9%, sensitivity of 20.3%, and specificity of 97.3%. The combination of the models achieved an accuracy of 82.3%, sensitivity of 22.4%, and specificity of 98.4%. The most important parameters that influenced the model results were the type of water network (hot/cold), the replacement of filter valves, and atmospheric temperature. Among the models tested, Machine Learning obtained the best results in terms of accuracy and sensitivity., Conclusions: Future studies are required to improve these predictive models by expanding the dataset using other parameters and other pavilions of the same hospital.

Introduction: The correct method of surface disinfection in hospitals is an essential tool in the fight against the spread of healthcare-associated infections caused by multi-resistant microorganisms. Currently, there are many disinfectants on the market that can be used against different microorganisms. However, the effectiveness of different active molecules is controversial in the literature., Study Design: The aim of this study was to evaluate the effectiveness of wipes based on hydrogen peroxide (1.0 %) and highly specific plant-based surfactants, contained in H2O2TM (Hi-speed H2O2TM) products, against some hospital-associated microorganisms., Methods: The effectiveness of the wipes was tested against nosocomial and control strains of methicillin-resistant Staphylococcus aureus, carbapenem-resistant Pseudomonas aeruginosa, Klebsiella pneumoniae carbapenemase, Aspergillus fumigatus and Candida parapsilosis. Specifically, in vitro activity was assessed using three different techniques: stainless steel surface testing, surface diffusion testing and well diffusion test., Results: The three different methods tested confirm the wipes' good effectiveness against the most common multi-resistant bacteria and against fungi., Conclusions: These data show that the tested wipes could be a valid adjunct to the disinfection process and could assist in the prevention of healthcare-associated infections.

Background: Legionnaires' disease is caused by inhalation or aspiration of small water droplets contaminated with Legionella, commonly found in natural and man-made water systems and in moist soil. Over the past 5 years, notification rates of this disease have almost doubled in the European Union (EU) / European Environmental Agency (EEA), from 1.4 in 2015 to 2.2 cases per 100,000 population in 2019. Some studies show that the greater presence of the microorganism in the water network and the increase in cases of legionellosis could be related to the variations in some environmental factors, such as air temperature, which may influence the water temperature., Study Design: Climate change is currently a prominent topic worldwide because of its significant impact on the natural environment. It is responsible for the increase in numerous waterborne pathologies. The purpose of this study was to correlate the air temperature recorded in Apulia region from January 2018 to April 2023 with the presence of Legionella in the water networks of public and private facilities and the incidence rates of legionellosis during the same period., Methods: During the period from January 2018 to April 2023, water samples were collected from facilities involved in legionellosis cases and analyzed for Legionella. During the same period, all the cases notified to the regional epidemiological observatory (OER-Apulia) were included in this study. Statistical analyses were conducted using the Shapiro-Wilk test to determine whether the Legionella load was distributed normally, the Wilcoxon rank sum test to compare the air temperatures (average and range) of the negative and positive samples for Legionella detection, and the multivariate analysis (Poisson regression) to compare the Legionella load with the water sample temperature, average air temperature, and temperature range on the day of sampling. The Wilcoxon test for paired samples was used to compare legionellosis cases between the warmer and colder months., Results: Overall, 13,044 water samples were analyzed for Legionella and 460 cases of legionellosis were notified. Legionella was isolated in 20.1% of the samples examined. The difference in the air temperature between negative samples and positive samples was statistically significant (p-value < 0.0001): on days when water samples tested positive for Legionella a higher temperature range was observed than on days when water samples tested negative (p-value = 0.004). Poisson regression showed a direct correlation between Legionella load, water temperature, and average air temperature. The incidence of legionellosis cases in warmer months was higher than in colder months (p-value = 0.03)., Conclusions: Our study highlights a significant increase in the load of Legionella in the Apulian water network, and an association between warmer temperatures and legionellosis incidence. In our opinion, further investigations are needed in different contexts and territories to characterize the epidemiology of legionellosis, and to explain its extreme variability in different geographical areas and how these data may be influenced by different risk factors.

Background: The COVID-19 emergency has highlighted the importance of prevention systems and environ-mental microbiological monitoring as fundamental elements in the response to epidemics and other such threats to individual and collective health. The use of automated "No-touch" room disinfection systems eliminates or reduces the dependence on operators, thus allowing an improvement in the effectiveness of terminal disinfection., Study Design: In the present study, we focused on possible SARS-CoV-2 contamination of surfaces of com-mercial services, and the effectiveness of ozone treatment on the virus., Methods: Analyses were conducted on 4-7 October and 27-30 December 2021 in four supermarkets in an Apulian city; supermarkets A and B were equipped with an ozonisation system, while C and D were without any environmental remediation., Results: SARS-CoV-2 RNA was detected by real-time RT-PCR only in December, in 6% of the surfaces tested, and all examined samples were found to be negative after viral culture, since no cytopathic effect was observed. A statistically significant difference emerged from the comparison of October vs. December (p = 0.0289), but no statistically significant difference (p = 0.6777) emerged from the comparison between supermarkets with and without the ozonisation system., Conclusions: Although no important changes were observed by treating the environments with ozonisation systems, further studies are needed to validate the effectiveness of environmental treatments with airborne disinfectants.