In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Air contamination in the hospital setting can be a reason for the spread of nosocomial infection among susceptible patients. The aim of this study was to identify bacterial species, and their load and drug resistance, in the air of a tertiary hospital. Air samples were collected on a monthly basis for 12 consecutive months in four different departments of the hospital (Intensive Care Unit (ICU), Internal Medicine Ward (IMW), Surgical Ward (SW), and Neonatal Unit (NU)). In total, 101 samples were collected, out of which 158 Gram-positive (GP) and 44 Gram-negative (GN) strains were isolated. The majority of GP isolates were Staphylococcus spp. (n = 100). The highest total microbial load was reported in the IMW (p = 0.005), while the highest Staphylococcus load was observed in the ICU (p = 0.018). GP bacterial load was higher in autumn, while GN load was higher in spring. Regarding drug resistance, four multi-drug-resistant (MDR) strains and one extensively drug-resistant (XDR) strain were isolated in the ICU, two MDR strains and one XDR strain in the SW, one MDR strain in the IMW and one MDR strain in the NU samples. Air in hospital settings is contaminated with various microbes; some of them are MDR, consisting a potential cause of hospital-acquired infection.

Background: Breastfeeding is not only important for the bond between the mother and the child but is also associated with many health benefits. Socio-demographic factors have been found to be important for the initiation and longer duration of breastfeeding. Aim: To record the breastfeeding characteristics and to explore possible associations with socio-cultural and demographic factors in a culturally diverse area in Greece. Materials and methods: A questionnaire including information about breastfeeding characteristics, medical history and demographics was completed by parents of children in the primary schools of the area. Results: The study included 700 school-age children (352 boys), mean age 9.4±1.9 years. Breastfeeding was reported in 54.9% of them, with median (IQR) duration of 120 (range 60-360) days. Prevalence of breastfeeding was slightly higher in semi-urban areas, versus rural or urban settlements, but the duration there was significantly lower (p<0.001). Better educated mothers tended to initiate or sustain breastfeeding for a longer period; however, this difference was not statistically significant. Children of the native Muslim minority had been breastfed for a longer time (median duration 360 vs. 120 days, p<0.001), however, no difference was noted in prevalence. Conclusion: Prevalence of breastfeeding in Greece is approximately 55%, similar to previous reports and along with duration is determined by certain socio-cultural factors.

This study examined the microbial quality of air and surfaces in six elevators at a tertiary hospital in Greece in 2022. The results highlighted hospital elevators as potentially relevant environmental reservoirs of microbial contamination, with contact surfaces (buttons) exhibiting significantly higher microbial loads than the air. Strong seasonality was also documented; the elevated microbial burden observed during the October sampling period likely reflects seasonal fluctuations in occupant traffic and environmental factors, although direct traffic and microclimatic variables were not monitored. Presumptive Staphylococcus spp. strains dominated the air, while Gram-negative bacteria recovered on MacConkey agar prevailed on surfaces. Phenotypically resistant isolates were also detected, including isolates showing reduced susceptibility or resistance to clinically relevant antimicrobial agents, highlighting the potential value of incorporating AMR surveillance into environmental monitoring. The semi-quantitative environmental exposure assessment showed that, while air quality remained within acceptable limits, the surface-contact route via elevator buttons represented the largest estimated exposure component. Healthcare workers showed the highest estimated cumulative exposure because of the greater frequency of elevator use assumed in the exposure scenario. In conclusion, hospital elevators may act as environmental reservoirs of microbial contamination and potential exposure pathways within the hospital environment, highlighting the need for enhanced hygiene measures, systematic monitoring and the integration of antimicrobial resistance into exposure assessment.