Objectives: Safe pharmaceutical care (PC) requires an interprofessional team approach, involving physicians, nurses and pharmacists. Nurses’ roles however, are not always explicit and clear, complicating interprofessional collaboration. The aim of this study is to describe nurses’ practice and interprofessional collaboration in PC, from the viewpoint of nurses, physicians and pharmacists. Design: A cross-sectional survey. Setting: The study was conducted in 17 European countries, each with their own health systems. Participants: Pharmacists, physicians and nurses with an active role in PC were surveyed. Main outcome measures: Nurses’ involvement in PC, experiences of interprofessional collaboration and communication and views on nurses’ competences. Results: A total of 4888 nurses, 974 physicians and 857 pharmacists from 17 European countries responded. Providing patient education and information (PEI), monitoring medicines adherence (MMA), monitoring adverse/therapeutic effects (ME) and prescribing medicines were considered integral to nursing practice by 78%, 73%, 69% and 15% of nurses, respectively. Most respondents were convinced that quality of PC would be improved by increasing nurses’ involvement in ME (95%), MMA (95%), PEI (91%) and prescribing (53%). Mean scores for the reported quality of collaboration between nurses and physicians, collaboration between nurses and pharmacists and interprofessional communication were respectively <7/10, ≤4/10, <6/10 for all four aspects of PC. Conclusions: ME, MMA, PEI and prescribing are part of nurses’ activities, and most healthcare professionals felt their involvement should be extended. Collaboration between nurses and physicians on PC is limited and between nurses and pharmacists even more.
Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.
MULTIFILE
Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.
Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
Biotherapeutic medicines such as peptides, recombinant proteins, and monoclonal antibodies have successfully entered the market for treating or providing protection against chronic and life-threatening diseases. The number of relevant commercial products is rapidly increasing. Due to degradation in the gastro-intestinal tract, protein-based drugs cannot be taken orally but need to be administered via alternative routes. The parenteral injection is still the most widely applied administration route but therapy compliance of injection-based pharmacotherapies is a concern. Long-acting injectable (LAI) sustained release dosage forms such as microparticles allow less frequent injection to maintain plasma levels within their therapeutic window. Spider Silk Protein and Poly Lactic-co-Glycolic Acid (PLGA) have been attractive candidates to fabricate devices for drug delivery applications. However, conventional microencapsulation processes to manufacture microparticles encounter drawbacks such as protein activity loss, unacceptable residual organic solvents, complex processing, and difficult scale-up. Supercritical fluids (SCF), such as supercritical carbon dioxide (scCO2), have been used to produce protein-loaded microparticles and is advantageous over conventional methods regarding adjustable fluid properties, mild operating conditions, interfacial tensionless, cheap, non-toxicity, easy downstream processing and environment-friendly. Supercritical microfluidics (SCMF) depict the idea to combine strengths of process scale reduction with unique properties of SCF. Concerning the development of long-acting microparticles for biological therapeutics, SCMF processing offers several benefits over conventionally larger-scale systems such as enhanced control on fluid flow and other critical processing parameters such as pressure and temperature, easy modulation of product properties (such as particle size, morphology, and composition), cheaper equipment build-up, and convenient parallelization for high-throughput production. The objective of this project is to develop a mild microfluidic scCO2 based process for the production of long-acting injectable protein-loaded microparticles with, for example, Spider Silk Protein or PLGA as the encapsulating materials, and to evaluate the techno-economic potential of such SCMF technology for practical & industrial production.
