Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

Pro-Inflammatory and Cytotoxic Effects of Polystyrene Microplastics on Human and Murine Intestinal Cell Lines.

Plastic is a polymer extremely resistant to degradation that can remain for up to hundreds or thousands of years, leading to the accumulation of massive amounts of plastic waste throughout the planet's ecosystems. Due to exposure to various environmental factors, plastic breaks down into smaller particles named microplastics (1-5000 µm) and nanoplastics (<1 µm). Microplastics (MPs) are ubiquitous pollutants but, still, little is known about their effects on human and animal health. Herein, our aim is to investigate cytotoxicity, oxidative stress, inflammation and correlated gene modulation following exposure to polystyrene microplastics (PS-MPs) in HRT-18 and CMT-93 epithelial cell lines. After 6, 24 and 48 h PS-MPs treatment, cell viability (MTT) and oxidative stress (SOD) assays were performed; subsequently, expression changes and cytokines release were investigated by Real-Time PCR and Magnetic-beads panel Multiplex Assay, respectively. For each exposure time, a significantly increased cytotoxicity was observed in both cell lines, whereas SOD activity increased only in CMT-93 cells. Furthermore, Magnetic-beads Multiplex Assay revealed an increased release of IL-8 in HRT-18 cells' medium, also confirmed by gene expression analysis. Results obtained suggest the presence of a pro-inflammatory pattern induced by PS-MPs treatment that could be related to the observed increase in cytotoxicity.

A Scoping Review on GIS Technologies Applied to Farmed Fish Health Management.

Finfish aquaculture, one of the fastest growing intensive sectors worldwide, is threatened by numerous transmissible diseases that may have devastating impacts on its economic sustainability. This review (2010-2022) used a PRISMA extension for scoping reviews and a text mining approach to explore the extent to which geographical information systems (GIS) are used in farmed fish health management and to unveil the main GIS technologies, databases, and functions used to update the spatiotemporal data underpinning risk and predictive models in aquatic surveillance programmes. After filtering for eligibility criteria, the literature search provided 54 records, highlighting the limited use of GIS technologies for disease prevention and control, as well as the prevalence of GIS application in marine salmonid farming, especially for viruses and parasitic diseases typically associated with these species. The text mining generated five main research areas, underlining a limited range of investigated species, rearing environments, and diseases, as well as highlighting the lack of GIS-based methodologies at the core of such publications. This scoping review provides a source of information for future more detailed literature analyses and outcomes to support the development of geospatial disease spread models and expand in-field GIS technologies for the prevention and mitigation of fish disease epidemics.

Potential SARS-CoV-2 Susceptibility of Cetaceans Stranded along the Italian Coastline.

Due to marine mammals' demonstrated susceptibility to SARS-CoV-2, based upon the homology level of their angiotensin-converting enzyme 2 (ACE2) viral receptor with the human one, alongside the global SARS-CoV-2 occurrence and fecal contamination of the river and marine ecosystems, SARS-CoV-2 infection may be plausibly expected to occur also in cetaceans, with special emphasis on inshore species like bottlenose dolphins (Tursiops truncatus). Moreover, based on immune and inflammatory responses to SARS-CoV-2 infection in humans, macrophages could also play an important role in antiviral defense mechanisms. In order to provide a more in-depth insight into SARS-CoV-2 susceptibility in marine mammals, we evaluated the presence of SARS-CoV-2 and the expression of ACE2 and the pan-macrophage marker CD68. Aliquots of tissue samples, belonging to cetaceans stranded along the Italian coastline during 2020-2021, were collected for SARS-CoV-2 analysis by real-time PCR (RT-PCRT) (N = 43) and Immunohistochemistry (IHC) (N = 59); thirty-two aliquots of pulmonary tissue sample (N = 17 Tursiops truncatus, N = 15 Stenella coeruleoalba) available at the Mediterranean Marine Mammal Tissue Bank (MMMTB) of the University of Padua (Legnaro, Padua, Italy) were analyzed to investigate ACE2 expression by IHC. In addition, ACE2 and CD68 were also investigated by Double-Labeling Immunofluorescence (IF) Confocal Laser Microscopy. No SARS-CoV-2 positivity was found in samples analyzed for the survey while ACE2 protein was detected in the lower respiratory tract albeit heterogeneously for age, gender/sex, and species, suggesting that ACE2 expression can vary between different lung regions and among individuals. Finally, double IF analysis showed elevated colocalization of ACE2 and CD68 in macrophages only when an evident inflammatory reaction was present, such as in human SARS-CoV-2 infection.

Evaluation of the efficacy of a training course in food safety addressed to food charity volunteers.

In Italy, the Banco Alimentare Onlus manages a network of 8,000 charitable organizations that distribute 67,000 tons of foodstuffs to 1.6 million needy persons. To provide their volunteers with the required food safety knowledge, the Banco Alimentare del Piemonte Onlus commissioned the Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta to hold training courses in food safety. Before and after each session, the participants completed a questionnaire to evaluate their knowledge on the topic of food safety. The responses were entered in a dedicated database and analyzed using STATA ver. 15.1. Comparison of the scores for each participant before and after training revealed a considerable discordance [ICC 0.06, 95% confidence interval (CI) 0.00-0.18]. Analysis of the post-training questionnaires showed that the number of questions left unanswered decreased and the number of correct answers increased. The difference between the percentage of correct and incorrect responses before and after the training course was statistically significant (P<0.001). Comparison of responses to the pre- and post-training questionnaires provided the data for statistical evaluation of the efficacy of the training course.