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It has been suggested that microorganisms present in honey are a potential source of antimicrobial compounds. This study aimed to isolate and characterize bacteria from 46 Greek honey samples of diverse botanical and geographical origin and to determine whether these bacteria demonstrate antibacterial activity against five important nosocomial and foodborne pathogens. In total, 2014 bacterial isolates were obtained and screened for antibacterial activity. Overall, 16% of the isolates inhibited the growth of Staphylococcus aureus, 11.2% inhibited the growth of Pseudomonas aeruginosa and Acinetobacter baumannii, 10.2% inhibited the growth of Salmonella Typhimurium and 12.4% of the isolates affected the growth of Citrobacter freundii. In total, 316 isolates that inhibited the growth of more than two of the tested pathogens were grouped by restriction fragment length polymorphisms (RFLP) analysis of the 16S rRNA gene amplicon. Fifty of them were identified by 16S rRNA gene sequencing. The majority, 62% of the isolates, belonged to the genus Bacillus. Only 10% of the isolates were identified as Gram-negative bacteria. Furthermore, in several bacterial isolates, genes encoding polyketide synthases and nonribosomal peptide synthetases that catalyze the biosynthesis of secondary metabolites which might contribute to the exerted antimicrobial activity, were detected. This study demonstrates that honey microbiota exerts antimicrobial activity and is a putative source of secondary metabolites against important nosocomial and food pathogens that warrants further investigation.

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal–Wallis and Mann–Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001–0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve—AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient’s physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.