The synthesis of silver nanoparticles (AgNPs) by microorganisms recently gained a greater interest due to its potential to produce them in various sizes and morphologies. In this study, for AgNP biosynthesis, we used a new Pseudomonas strain isolated from a consortium associated with the Antarctic marine ciliate Euplotes focardii. After incubation of Pseudomonas cultures with 1 mM of AgNO₃ at 22 °C, we obtained AgNPs within 24 h. Scanning electron (SEM) and transmission electron microscopy (TEM) revealed spherical polydispersed AgNPs in the size range of 20–70 nm. The average size was approximately 50 nm. Energy dispersive X-ray spectroscopy (EDS) showed the presence of a high intensity absorption peak at 3 keV, a distinctive property of nanocrystalline silver products. Fourier transform infrared (FTIR) spectroscopy found the presence of a high amount of AgNP-stabilizing proteins and other secondary metabolites. X-ray diffraction (XRD) revealed a face-centred cubic (fcc) diffraction spectrum with a crystalline nature. A comparative study between the chemically synthesized and Pseudomonas AgNPs revealed a higher antibacterial activity of the latter against common nosocomial pathogen microorganisms, including Escherichia coli, Staphylococcus aureus and Candida albicans. This study reports an efficient, rapid synthesis of stable AgNPs by a new Pseudomonas strain with high antimicrobial activity.

In the last decade, metal nanoparticles (NPs) have gained significant interest in the field of biotechnology due to their unique physiochemical properties and potential uses in a wide range of applications. Metal NP synthesis using microorganisms has emerged as an eco-friendly, clean, and viable strategy alternative to chemical and physical approaches. Herein, an original and efficient route for the microbial synthesis of copper NPs using bacterial strains newly isolated from an Antarctic consortium is described. UV-visible spectra of the NPs showed a maximum absorbance in the range of 380–385 nm. Transmission electron microscopy analysis showed that these NPs are all monodispersed, spherical in nature, and well segregated without any agglomeration and with an average size of 30 nm. X-ray powder diffraction showed a polycrystalline nature and face centered cubic lattice and revealed characteristic diffraction peaks indicating the formation of CuONPs. Fourier-transform infrared spectra confirmed the presence of capping proteins on the NP surface that act as stabilizers. All CuONPs manifested antimicrobial activity against various types of Gram-negative; Gram-positive bacteria; and fungi pathogen microorganisms including Escherichia coli, Staphylococcus aureus, and Candida albicans. The cost-effective and eco-friendly biosynthesis of these CuONPs make them particularly attractive in several application from nanotechnology to biomedical science.

Background: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. Results: We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. Conclusions: the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/

We report the characterization of the bacterial consortium associated to Euplotes focardii, a strictly psychrophilic marine ciliate that was maintained in laboratory cultures at 4 °C after its first isolation from Terra Nova Bay, in Antarctica. By Illumina genome analyser, we obtained 11,179 contigs of potential prokaryotic origin and classified them according to the NCBI’s prokaryotic attributes table. The majority of these sequences correspond to either Bacteroidetes (16 %) or Proteobacteria (78 %). The latter were dominated by gamma- (39 %, including sequences related to the pathogenic genus Francisella), and alpha-proteobacterial (30 %) sequences. Analysis of the Pfam domain family and Gene Ontology term variation revealed that the most frequent terms that appear unique to this consortium correspond to proteins involved in “transmembrane transporter activity” and “oxidoreductase activity”. Furthermore, we identified genes that encode for enzymes involved in the catabolism of complex substance for energy reserves. We also characterized members of the transposase and integrase superfamilies, whose role in bacterial evolution is well documented, as well as putative antifreeze proteins. Antibiotic treatments of E. focardii cultures delayed the cell division of the ciliate. To conclude, our results indicate that this consortium is largely represented by bacteria derived from the original Antarctic sample and may contribute to the survival of E. focardii in laboratory condition. Furthermore, our results suggest that these bacteria may have a more general role in E. focardii survival in its natural cold and oxidative environment.