Nanoparticles and Microorganisms:from Synthesis to Toxicity

Nanoscience is a young and growing field of science. It encompasses a diversity of sub-fields such as nanotechnology and nano-medicine, all of them seeking to realize the promises of nanoscale physics. Nano means: “billionth” and conceptually all nano-like terminology implicitly refers to the nano-meter (nm) scale (10-9m). Therefore the size range covered by nanoscience is from 1 to 100 nm, which lays at the boundary of two distinct worlds of physics: the bulk material and the atomic structure. In that particular region, laws of physics transition and while the bulk material exhibits constant physics independently of its size, nanomaterials see their properties and characteristics change as a function of size. That very specific property makes nanomaterials extremely appealing for a variety of applications. These applications cover areas such as electronics, photonics, catalysts, photography, material coatings, but also biotechnology, medicine, pharmacology, textile embedding, paints, household goods, cosmetics, foods and children goods. The current dissertation covers the field of metallic nanoparticles, within which, two types have been considered: selenium nanoparticles (SeNPs) and silver nanoparticles (AgNPs). SeNPs are interesting in inorganic semiconductors and crystal respectively used in electronics and photonics, whereas the interest for AgNPs is due to their strong antimicrobial properties. NPs are not only anthropogenic, but can be produced by a variety of organisms (e.g., bacteria, fungi, yeast or plants). However their biological synthesis remains partially unknown. They can be closely related to chemically produced NPs, but can also exhibit very specific characteristics unobtainable by conventional chemistry. An understanding of the underlying mechanisms of biological synthesis if extended to the industrial level could help achieve better NPs at a lower energetic and environmental cost. The use of nanomaterials such as AgNPs to protect drinking water from pathogens or prevent microbially derived bad odors, present the risk of their release into the environment. A gap of knowledge remains as to the hazards caused by an increase in AgNPs load in freshwater and sediments on the various biotas. This thesis addressed these two fundamental questions in Chapter 1 for SeNPs and Chapters 2, 3 and 4 for AgNP

Silver release from silver nanoparticles in natural waters

Silver nanoparticles (AgNPs) are used increasingly in consumer products for their antimicrobial properties. This increased use raises ecological concern because of the release of AgNPs into the environment. Once released, zero-valent silver may be oxidized to Ag+ and the cation liberated or it may persist as AgNPs. The chemical form of Ag has implications for its toxicity; it is therefore crucial to characterize the persistence of AgNPs to predict their ecotoxicological potential. In this study, we evaluated the release of Ag from AgNPs of various sizes exposed to river and lake water for up to 4 months. Several AgNP-capping agents were also considered: polyvinylpyrrolidone (PVP), tannic acid (Tan), and citric acid (Cit). We observed a striking difference between 5, 10, and 50 nm AgNPs, with the latter being more resistant to dissolution in oxic water on a mass basis. However, the difference decreased when Ag was surface-area-normalized, suggesting an important role of the surface area in determining Ag loss. We propose that rapid initial Ag+ release was attributable to desorption of Ag+ from nanoparticle surfaces. We also observed that PVP- and Tan-AgNPs are more prone to Ag+ release than Cit-AgNPs. In addition, it is likely that oxidative dissolution also occurs but at a slower rate. This study clearly shows that small AgNPs (5 nm, PVP and Tan) dissolve rapidly and almost completely, while larger AgNPs (50 nm) have the potential to persist for an extended period of time and could serve as a continuous source of Ag ions

Rapid polymyxin NP test for the detection of polymyxin resistance mediated by the MCR-1/MCR-2 genes

The Rapid Polymyxin NP test has been recently developed to rapidly detect polymyxin resistance in Enterobacteriaceae. Here we evaluated this test for detecting MCR- 1/MCR-2-producing Enterobacteriaceae using a collection of 70 non-redundant strains either recovered from the environment, animals, or humans. Sensitivity and specificity were found to be 100%

EuReCa ONE—27 Nations, ONE Europe, ONE Registry A prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe

AbstractIntroductionThe aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe.MethodsThis was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries.ResultsData on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19.0 to 104.0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25.2%), Overall, 662/6414 (10.3%) in all cases with CPR attempted survived for at least 30 days or to hospital discharge.ConclusionThe results of EuReCa ONE highlight that OHCA is still a major public health problem accounting for a substantial number of deaths in Europe.EuReCa ONE very clearly demonstrates marked differences in the processes for data collection and reported outcomes following OHCA all over Europe. Using these data and analyses, different countries, regions, systems, and concepts can benchmark themselves and may learn from each other to further improve survival following one of our major health care events

Training and test datasets for the PredictONCO tool

<p>This dataset was used for training and validating the <a href="https://loschmidt.chemi.muni.cz/predictonco/">PredictONCO </a>web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).</p><p>The entire dataset (<strong>SEQ</strong>) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (<strong>STR</strong>: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both <strong>STR </strong>and <strong>SEQ </strong>datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. </p><p>For more details about the tool, please visit the <a href="https://loschmidt.chemi.muni.cz/predictonco/help">help page</a> or <a href="https://loschmidt.chemi.muni.cz/peg/contact/">get in touch with us</a>.</p&gt