[The implementation of clinical assessment by nurses in Switzerland: A cross-sectional study from the nursing perspective]. / Die Umsetzung des klinischen Assessments durch Pflegefachpersonen in der Schweiz.

The implementation of clinical assessment by nurses in Switzerland: A cross-sectional study from the nursing perspective Abstract: Background: Clinical assessment is a core competency of BSc and MSc prepared nurses in Switzerland. However, influencing factors of its implementation and the experience in the interprofessional team has been studied little so far. Objective: The aim of this study was to explore how often clinical assessment skills are used by BSc and MSc graduated nurses in Switzerland, to identify facilitating and hindering factors for implementation and to investigate the nurses experience of the implementation in the interprofessional team. Method: We used an exploratory, descriptive research design employing a cross-sectional study of the convenience sample of BScN and MScN graduates in Switzerland. 264 questionnaires were analysed quantitatively and 149 written statements as part of the survey qualitatively. Results: Respondents indicated that they routinely only use 18.9% of the 53 assessment skills they were trained in. The "respiratory system", "abdomen", "skin" and "mental status" were used most frequently. MScN graduates use respiratory assessments and BScN graduates use abdominal assessments more frequently. Lack of time and interruptions, lack of influence on patient care and specialty specific skills are hindering factors for the implementation. Better conditions at the institutional level and appreciation within the interprofessional team can make an encouraging contribution in future. Conclusion: This study illustrates, that the theory-practice transfer of clinical assessment in Switzerland needs to be further promoted.

Medicinal Polypharmacology in the Clinic - Translating the Polypharmacolome into Therapeutic Benefit.

Drugs with multiple targets, often annotated as 'unselective', 'promiscuous', 'multitarget', or 'polypharmacological', are widely considered in both academic and industrial research as a high risk due to the likelihood of adverse effects. However, retrospective analyses have shown that particularly approved drugs bear rich polypharmacological profiles. This raises the question whether our perception of the specificity paradigm ('one drug-one target concept') is correct - and if specifically multitarget drugs should be developed instead of being rejected. These questions provoke a paradigm shift - regarding the development of polypharmacological drugs not as a 'waste of investment', but acknowledging the existence of a 'lack of investment'. This perspective provides an insight into modern drug development highlighting latest drug candidates that have not been assessed in a broader polypharmacology-based context elsewhere embedded in a historic framework of classical and modern approved multitarget drugs. The article shall be an inspiration to the scientific community to re-consider current standards, and more, to evolve to a better understanding of polypharmacology from a challenge to an opportunity.

Clonal evolution in tyrosine kinase inhibitor-resistance: lessons from in vitro-models.

Introduction: Resistance in anti-cancer treatment is a result of clonal evolution and clonal selection. In chronic myeloid leukemia (CML), the hematopoietic neoplasm is predominantly caused by the formation of the BCR::ABL1 kinase. Evidently, treatment with tyrosine kinase inhibitors (TKIs) is tremendously successful. It has become the role model of targeted therapy. However, therapy resistance to TKIs leads to loss of molecular remission in about 25% of CML patients being partially due to BCR::ABL1 kinase mutations, while for the remaining cases, various other mechanisms are discussed. Methods: Here, we established an in vitro-TKI resistance model against the TKIs imatinib and nilotinib and performed exome sequencing. Results: In this model, acquired sequence variants in NRAS, KRAS, PTPN11, and PDGFRB were identified in TKI resistance. The well-known pathogenic NRAS p.(Gln61Lys) variant provided a strong benefit for CML cells under TKI exposure visible by increased cell number (6.2-fold, p < 0.001) and decreased apoptosis (-25%, p < 0.001), proving the functionality of our approach. The transfection of PTPN11 p.(Tyr279Cys) led to increased cell number (1.7-fold, p = 0.03) and proliferation (2.0-fold, p < 0.001) under imatinib treatment. Discussion: Our data demonstrate that our in vitro-model can be used to study the effect of specific variants on TKI resistance and to identify new driver mutations and genes playing a role in TKI resistance. The established pipeline can be used to study candidates acquired in TKI-resistant patients, thereby providing new options for the development of new therapy strategies to overcome resistance.

Exploring and mapping the universe of evolutionary graphs identifies structural properties affecting fixation probability and time.

Population structure can be modeled by evolutionary graphs, which can have a substantial influence on the fate of mutants. Individuals are located on the nodes of these graphs, competing to take over the graph via the links. Applications for this framework range from the ecology of river systems and cancer initiation in colonic crypts to biotechnological search for optimal mutations. In all these applications, both the probability of fixation and the associated time are of interest. We study this problem for all undirected and unweighted graphs up to a certain size. We devise a genetic algorithm to find graphs with high or low fixation probability and short or long fixation time and study their structure searching for common themes. Our work unravels structural properties that maximize or minimize fixation probability and time, which allows us to contribute to a first map of the universe of evolutionary graphs.

Exact numerical calculation of fixation probability and time on graphs.

The Moran process on graphs is a popular model to study the dynamics of evolution in a spatially structured population. Exact analytical solutions for the fixation probability and time of a new mutant have been found for only a few classes of graphs so far. Simulations are time-expensive and many realizations are necessary, as the variance of the fixation times is high. We present an algorithm that numerically computes these quantities for arbitrary small graphs by an approach based on the transition matrix. The advantage over simulations is that the calculation has to be executed only once. Building the transition matrix is automated by our algorithm. This enables a fast and interactive study of different graph structures and their effect on fixation probability and time. We provide a fast implementation in C with this note (Hindersin et al., 2016). Our code is very flexible, as it can handle two different update mechanisms (Birth-death or death-Birth), as well as arbitrary directed or undirected graphs.