Biophysical response of living cells to boron nitride nanoparticles: Uptake mechanism and bio-mechanical characterization

Boron nitride nanomaterials have attracted significant interest due to their superior chemical and physical properties. Despite these novel properties, investigation on the interaction between boron nitride nanoparticle (BN NP) and living systems has been limited. In this study, BN NP (100–250 nm) is assessed as a promising biomaterial for medical applications. The toxicity of BN NP is evaluated by assessing the cells behaviours both biologically (MTT assay, ROS detection etc.) and physically (atomic force microscopy). The uptake mechanism of BN NP is studied by analysing the alternations in cellular morphology based on cell imaging techniques. The results demonstrate in vitro cytocompatibility of BN NP with immense potential for use as an effective nanoparticle for various bio-medical applications.

Male nursing students in training: A comparative analysis of hidden advantage and discrimination

Aim The aim of this study was to examine the lived experience of men training to be registered nurses within a regional New Zealand context. Design This study draws upon the key principles of descriptive phenomenology. Sample Five male students enrolled from the 1st and 3rd year of the BN programme. Findings - A Career with Prospects - Gender inequality by superiors; - Developing professional boundaries with female colleagues; - Being unique has its advantages.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.