Connercheng8631

01%, 0.1% and 1% by weight. A set of 20 correlations for prediction of thermal conductivity and dynamic viscosity of base fluids and corresponding nanofluids has been developed. Moreover, present results have been confronted with literature data and predictions made by use of carefully selected recognized literature correlations.Background High-fidelity simulation is being considered as a suitable environment for imparting the skills needed to deal with end-of-life (EOL) situations. The objective was to evaluate an EOL simulation project that introduced communication skills to nursing students who had not yet begun their training in real healthcare environments. Methods A sequential approach was used. The "questionnaire for the evaluation of the end-of-life project" was employed. Results A total of 130 students participated. Increasing the time spent in high-fidelity simulation significantly favored the exploration of feelings and fears regarding EOL (t = -2.37, p = 0.019), encouraged dialogue (t = -2.23, p = 0.028) and increased the acquisition of communication skills (t = -2.32, p = 0.022). Conclusions High-fidelity simulation promotes communication skills related to EOL in novice nursing students.As the most common chronic degenerative joint disease, osteoarthritis (OA) is the leading cause of pain and physical disability, affecting millions of people worldwide. Mainly characterized by articular cartilage degradation, osteophyte formation, subchondral bone remodeling, and synovial inflammation, OA is a heterogeneous disease that impacts all component tissues of the articular joint organ. Pathological changes, and thus symptoms, vary from person to person, underscoring the critical need of personalized therapies. However, there has only been limited progress towards the prevention and treatment of OA, and there are no approved effective disease-modifying osteoarthritis drugs (DMOADs). Cyclopamine Conventional treatments, including non-steroidal anti-inflammatory drugs (NSAIDs) and physical therapy, are still the major remedies to manage the symptoms until the need for total joint replacement. In this review, we provide an update of the known OA risk factors and relevant mechanisms of action. In addition, given that the lack of biologically relevant models to recapitulate human OA pathogenesis represents one of the major roadblocks in developing DMOADs, we discuss current in vivo and in vitro experimental OA models, with special emphasis on recent development and application potential of human cell-derived microphysiological tissue chip platforms.Molecular similarity is an elusive but core "unsupervised" cheminformatics concept, yet different "fingerprint" encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are "better" than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a "bowtie"-shaped artificial neural network. In the middle is a "bottleneck layer" or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.In this work, yerba mate nanoparticles (YMNs) were extracted from Ilex paraguairiencis yerba mate wastes and further used to improve the overall performance of mechanically recycled PLA (PLAR). Recycled PLA was obtained by melt reprocessing PLA subjected to an accelerated ageing process, which involved photochemical, thermal and hydrothermal ageing steps, as well as a final demanding washing step. YMNs (1 and 3 wt. %) were added to the PLAR during the melt reprocessing step and further processed into films. The main goal of the development of PLAR-YMNs bionanocomposites was to increase the barrier properties of recycled PLA, while showing good overall performance for food packaging applications. Thus, optical, structural, thermal, mechanical and barrier properties were evaluated. The incorporation of YMNs led to transparent greenish PLAR-based films with an effective blockage of harmful UV radiation. From the backbone FTIR stretching region (bands at 955 and 920 cm-1), it seems that YMNs favor the formation of crystalline domains acting as nucleating agents for PLAR. The morphological investigations revealed the good dispersion of YMNs in PLAR when they are used in the lowest amount of 1 wt. %, leading to bionanocomposites with improved mechanical performance. Although the addition of high hydrophilic YMNs increased the water vapor transmission, the addition of 1 wt. % of YMNs enhanced the oxygen barrier performance of the produced bionanocomposite films. These results show that the synergistic revalorization of post-consumer PLA and nanoparticles obtained from agri-food waste is a potential way for the production of promising packaging materials that meet with the principles of the circular economy.Pluripotent stem cells (PSCs) are considered as being an important cell source for regenerative medicine. The culture of PSCs usually requires a feeder cell layer or cell adhesive matrix coating such as Matrigel, laminin, and gelatin. Although a feeder-free culture using a matrix coating has been popular, the on-feeder culture is still an effective method for the fundamental study of regenerative medicine and stem cell biology. To culture PSCs on feeder cell layers, the elimination of feeder cells is required for biological or gene analysis and for cell passage. Therefore, a simple and cost-effective cell sorting technology is required. There are several commercialized cell-sorting methods, such as FACS or MACS. However, these methods require cell labeling by fluorescent dye or magnetic antibodies with complicated processes. To resolve these problems, we focused on dielectrophoresis (DEP) phenomena for cell separation because these do not require any fluorescent or magnetic dyes or antibodies. DEP imposes an electric force on living cells under a non-uniform AC electric field.